Many fielded mobile robot systems have demonstrated the importance of directly estimating the 3D shape of objects in the robot's vicinity. The most mature solutions available today use active laser scanning or stereo camera pairs, but both approaches require specialized and expensive sensors. In prior publications, we have demonstrated the generation of stereo images from a single very low-cost camera using structure from motion (SFM) techniques. In this paper we demonstrate the practical usage of single-camera stereo in real-world mobile robot applications. Stereo imagery tends to produce incomplete 3D shape reconstructions of man-made objects because of smooth/glary regions that defeat stereo matching algorithms. We demonstrate robust object detection despite such incompleteness through matching of simple parameterized geometric models. Results are presented where parked cars are detected, and then recognized via license plate recognition, all in real time by a robot traveling through a parking lot.

In support of the U.S. Army vision for increased mobility, survivability, and lethality, the Army Research Laboratory is currently developing a new version of the low frequency ultra-wideband (UWB) synthetic aperture radar (SAR) to support forward imaging. One of the goals in the development of this version of the radar is to make it affordable. This paper presents a study of various forward imaging radar configurations that could be employed in a forward imaging radar system to achieve good imaging resolution with a reasonable number of transmitters/receivers. This study provided us with insights to efficiently configure our transmitter/receiver array. In this study, we examined various radar configurations such as monostatic and some variations of bistatic cases. We provide the analysis of the synthetic aperture radar (SAR) image resolution for these configurations and show the effectiveness of the bistatic configuration with only two transmitters at the ends of the physical array. In addition to the analysis, we also provide simulation results to demonstrate the expected imaging resolutions with respect to the radar configuration and the imaging geometry. Finally, we also consider the use of two squinted transmitters at the two ends and exploit the forward motion of the vehicle to form image on the two sides.

Vehicle-borne smuggling is widespread because of the availability, flexibility and capacity of the cars and trucks. Inspecting vehicles at border crossings and checkpoints are key security elements. At the present time, most vehicle security inspections at home and abroad are conducted manually. Remotely operated vehicle inspection robots could be integrated into the operating procedures to improve throughput while reducing the workload burden on security personnel. The robotic inspection must be effective at detecting contraband and efficient at clearing the "clean" vehicles that make up the bulk of the traffic stream, while limiting the workload burden on the operators. In this paper, we present a systems engineering approach to robotic vehicle inspection. We review the tactics, techniques and procedures to interdict contraband. We present an operational concept for robotic vehicle inspection within this framework, and identify needed capabilities. We review the technologies currently available to meet these needs. Finally, we summarize the immediate potential and R&D challenges for effective contraband detection robots.

This paper presents an algorithm for online image-based terrain classification that mimics a human supervisor's segmentation and classification of training images into "Go" and "NoGo" regions. The algorithm identifies a set of image chips (or exemplars) in the training images that span the range of terrain appearance. It then uses the exemplars to segment novel images and assign a Go/NoGo classification. System parameters adapt to new inputs, providing a mechanism for learning. System performance is compared to that obtained via offline fuzzy c-means clustering and support vector machine classification.

The Imaging, Robotics, and Intelligent Systems (IRIS) Laboratory at the University of Tennessee is currently developing a modular approach to unmanned systems to increase mission flexibility and aid system interoperability for security and surveillance applications. The main focus of the IRIS research is the development of sensor bricks where the term brick denotes a self-contained system that consists of the sensor itself, a processing unit, wireless communications, and a power source. Prototypes of a variety of sensor bricks have been developed. These systems include a thermal imaging brick, a quad video brick, a 3D range brick, and a nuclear (gamma ray and neutron) detection bricks. These bricks have been integrated in a modular fashion into mobility platforms to form functional unmanned systems. Research avenues include sensor processing algorithms, system integration, communications architecture, multi-sensor fusion, sensor planning, sensor-based localization, and path planning. This research is focused towards security and surveillance applications such as under vehicle inspection, wide-area perimeter surveillance, and high value asset monitoring. This paper presents an overview of the IRIS research activities in modular robotics and includes results from prototype systems.

The US Navy and other Department of Defense (DoD) and Department of Homeland Security (DHS) organizations are increasingly interested in the use of unmanned surface vehicles (USVs) for a variety of missions and applications. In order for USVs to fill these roles, they must be capable of a relatively high degree of autonomous navigation. Space and Naval Warfare Systems Center, San Diego is developing core technologies required for robust USV operation in a real-world environment, primarily focusing on autonomous navigation, obstacle avoidance, and path planning.

State-of-the-art unmanned ground vehicles are capable of understanding and adapting to arbitrary road terrain for navigation. The robotic mobility platforms mounted with sensors detect and report security concerns for subsequent action. Often, the information based on the localization of the unmanned vehicle is not sufficient for deploying army resources. In such a scenario, a three dimensional (3D) map of the area that the ground vehicle has surveyed in its trajectory would provide a priori spatial knowledge for directing resources in an efficient manner. To that end, we propose a mobile, modular imaging system that incorporates multi-modal sensors for mapping unstructured arbitrary terrain. Our proposed system leverages 3D laser-range sensors, video cameras, global positioning systems (GPS) and inertial measurement units (IMU) towards the generation of photo-realistic, geometrically accurate, geo-referenced 3D terrain models. Based on the summary of the state-of-the-art systems, we address the need and hence several challenges in the real-time deployment, integration and visualization of data from multiple sensors. We document design issues concerning each of these sensors and present a simple temporal alignment method to integrate multi-sensor data into textured 3D models. These 3D models, in addition to serving as a priori for path planning, can also be used in simulators that study vehicle-terrain interaction. Furthermore, we show our 3D models possessing the required accuracy even for crack detection towards road surface inspection in airfields and highways.

The use of robots for (semi-) autonomous operations in complex terrains such as urban environments poses difficult mobility, mapping, and perception challenges. To be able to work efficiently, a robot should be provided with sensors and software such that it can perceive and analyze the world in 3D. Real-time 3D sensing and perception in this operational context are paramount. To address these challenges, DRDC Valcartier has developed over the past years a compact sensor that combines a wide baseline stereo camera and a laser scanner with a full 360 degree azimuth and 55 degree elevation field of view allowing the robot to view and manage overhang obstacles as well as obstacles at ground level. Sensing in 3D is common but to efficiently navigate and work in complex terrain, the robot should also perceive, decide and act in three dimensions. Therefore, 3D information should be preserved and exploited in all steps of the process. To achieve this, we use a multiresolution octree to store the acquired data, allowing mapping of large environments while keeping the representation compact and memory efficient. Ray tracing is used to build and update the 3D occupancy model. This model is used, via a temporary 2.5D map, for navigation, obstacle avoidance and efficient frontier-based exploration. This paper describes the volumetric sensor concept, describes its design features and presents an overview of the 3D software framework that allows 3D information persistency through all computation steps. Simulation and real-world experiments are presented at the end of the paper to demonstrate the key elements of our approach.

The use of head-aimed vision systems for remote navigation and weapons aiming greatly increases the mission performance of armed unmanned ground vehicles. Head-aimed human/robotic vision interfaces greatly improves situational awareness. Task performance in target tracking and threat identification is increased by 200 to 300 percent.

Trends in combat technology research point to an increasing role for uninhabited vehicles and other robotic elements in modern warfare tactics. However, real-time control of multiple uninhabited battlefield robots and other semi-autonomous systems, in diverse fields of operation, is a difficult problem for modern warfighters that, while identified, has not been adequately addressed. Soar Technology is applying software agent technology to simplify demands on the human operator. Our goal is to build intelligent systems capable of finding the best balance of control between the human and autonomous system capabilities. We are developing an Intelligent Control Framework (ICF) from which to create agent-based systems that are able to dynamically delegate responsibilities across multiple robotic assets and the human operator. This paper describes proposed changes to our ICF architecture based on principles of human-machine teamwork derived from collaborative discourse theory. We outline the principles and the new architecture, and give examples of the benefits that can be realized from our approach.

This paper describes a system for implementing adjustable autonomy levels in simulated unmanned vehicles using an approach based upon the fields of deontics and Joint Intention Theory (JIT). It discusses Soar Technology's Intelligent Control Framework research project (ICF), the authors' use of deontics in the creation of adjustable autonomy for ICF, and some possible future directions in which the research could be expanded. Use of deontics and JIT in ICF has allowed us to define system-wide formal limits on the behavior of the unmanned systems controlled by ICF, to increase the flexibility of our adjustable autonomy system, and to decrease the granularity of the autonomy adjustments. This set of formalisms allows the unmanned system maximal autonomy in the default case, while allowing the user and supervisory agents to constrain that autonomy in situations when necessary. Unlike more strictly layered adjustable autonomy formalisms, our adjustable autonomy formalism can be used to restrict subsets of autonomous behaviors, rather than entire systems, in response to situational requirements.

The field of autonomous vehicles is a rapidly growing one, with significant interest from both government and industry sectors. Autonomous vehicles represent the intersection of artificial intelligence (AI) and robotics, combining decision-making with real-time control. Autonomous vehicles are desired for use in search and rescue, urban reconnaissance, mine detonation, supply convoys, and more. The general adage is to use robots for anything dull, dirty, dangerous or dumb. While a great deal of research has been done on autonomous systems, there are only a handful of fielded examples incorporating machine autonomy beyond the level of teleoperation, especially in outdoor/complex environments. In an attempt to assess and understand the current state of the art in autonomous vehicle development, a few areas where unsolved problems remain became clear. This paper outlines those areas and provides suggestions for the focus of science and technology research. The first step in evaluating the current state of autonomous vehicle development was to develop a definition of autonomy. A number of autonomy level classification systems were reviewed. The resulting working definitions and classification schemes used by the authors are summarized in the opening sections of the paper. The remainder of the report discusses current approaches and challenges in decision-making and real-time control for autonomous vehicles. Suggested research focus areas for near-, mid-, and long-term development are also presented.

This paper describes an extension of scripts, which have been used to control sequences of robot behavior, to facilitate human-robot coordination. The script mechanism permits the human to both conduct expected, complementary activities with the robot and to intervene opportunistically taking direct control. Scripts address the six major issues associated with human-robot coordination. They allow the human to visualize the robot's mental model of the situation and build a better overall understanding of the situation and what level of autonomy or intervention is needed. It also maintains synchronization of the world and robot models so that control can be seamlessly transferred between human and robot while eliminating "coordination surprise". The extended script mechanism and its implementation in Java on an Inuktun micro-VGTV robot for the technical search task in urban search and rescue is described.

Collecting environmental data in coastal bays presents several challenges to the scientist. One of the most pressing issues is how to efficiently and reliably gather data in shallow water areas-environments that often preclude the use of traditional boats. Obstacles that are encountered in such environments include difficulty in covering large territories and the presence of inaccessible areas due to a variety of reasons, such as soft bottoms or contamination. There is also a high probability of disturbing the test area while placing the sensors. This paper outlines the development of a remotely operated boat and its real-time control system.

In support of Canadian Forces transformation, Defence R&D Canada (DRDC) has established an ongoing program to develop machine intelligence for semi-autonomous vehicles and systems. Focussing on mine clearance and remote scouting for over a decade, DRDC Suffield has developed numerous UGVs controlled remotely over point-to-point radio links. Though this strategy removes personnel from potential danger, DRDC recognized that human factors and communications bandwidth limit teleoperation and that only networked, autonomous unmanned systems can conserve these valuable resources. This paper describes the outcome of the first autonomy project, Autonomous Land Systems (ALS), designed to demonstrate basic autonomous multivehicle land capabilities.

In 2002 Defence R&D Canada changed research direction from pure tele-operated land vehicles to general autonomy for land, air, and sea craft. The unique constraints of the military environment coupled with the complexity of autonomous systems drove DRDC to carefully plan a research and development infrastructure that would provide state of the art tools without restricting research scope. DRDC's long term objectives for its autonomy program address disparate unmanned ground vehicle (UGV), unattended ground sensor (UGS), air (UAV), and subsea and surface (UUV and USV) vehicles operating together with minimal human oversight. Individually, these systems will range in complexity from simple reconnaissance mini-UAVs streaming video to sophisticated autonomous combat UGVs exploiting embedded and remote sensing. Together, these systems can provide low risk, long endurance, battlefield services assuming they can communicate and cooperate with manned and unmanned systems. A key enabling technology for this new research is a software architecture capable of meeting both DRDC's current and future requirements. DRDC built upon recent advances in the computing science field while developing its software architecture know as the Architecture for Autonomy (AFA). Although a well established practice in computing science, frameworks have only recently entered common use by unmanned vehicles. For industry and government, the complexity, cost, and time to re-implement stable systems often exceeds the perceived benefits of adopting a modern software infrastructure. Thus, most persevere with legacy software, adapting and modifying software when and wherever possible or necessary -- adopting strategic software frameworks only when no justifiable legacy exists. Conversely, academic programs with short one or two year projects frequently exploit strategic software frameworks but with little enduring impact. The open-source movement radically changes this picture. Academic frameworks, open to public scrutiny and modification, now rival commercial frameworks in both quality and economic impact. Further, industry now realizes that open source frameworks can reduce cost and risk of systems engineering. This paper describes the Architecture for Autonomy implemented by DRDC and how this architecture meets DRDC's current needs. It also presents an argument for why this architecture should also satisfy DRDC's future requirements as well.

The objective of the Autonomous Intelligent Systems Section of Defence R&D Canada - Suffield is best described by its mission statement, which is "to augment soldiers and combat systems by developing and demonstrating practical, cost effective, autonomous intelligent systems capable of completing military missions in complex operating environments." The mobility requirement for ground-based mobile systems operating in urban settings must increase significantly if robotic technology is to augment human efforts in these roles and environments. The intelligence required for autonomous systems to operate in complex environments demands advances in many fields of robotics. This has resulted in large bodies of research in areas of perception, world representation, and navigation, but the problem of locomotion in complex terrain has largely been ignored. In order to achieve its objective, the Autonomous Intelligent Systems Section is pursuing research that explores the use of intelligent mobility algorithms designed to improve robot mobility. Intelligent mobility uses sensing, control, and learning algorithms to extract measured variables from the world, control vehicle dynamics, and learn by experience. These algorithms seek to exploit available world representations of the environment and the inherent dexterity of the robot to allow the vehicle to interact with its surroundings and produce locomotion in complex terrain. The primary focus of the paper is to present the intelligent mobility research within the framework of the research methodology, plan and direction defined at Defence R&D Canada - Suffield. It discusses the progress and future direction of intelligent mobility research and presents the research tools, topics, and plans to address this critical research gap. This research will create effective intelligence to improve the mobility of ground-based mobile systems operating in urban settings to assist the Canadian Forces in their future urban operations.

Complexity is a dominant, multi-dimensional attribute of the battlespace, and is evident in the geography, manmade infrastructure, force asymmetry and organizational processes. The Unmanned Aerial Vehicle represents a strategic enabler for military operations in complex environments by providing a flexible means of acquiring real-time information and deriving actionable knowledge. Limitations arising from remotely piloted UAV operation together with the desired operational flexibility in complex environments both dictate the need for increasingly autonomous UAV operation within a rigorous airspace integration framework. UAV autonomy relies primarily on access to missioncritical information from on-board sensors and networked datalink, together with comprehensive, efficient and robust algorithms for decisions on course of action. Global battlefield networking extends the notion of individual vehicle operation to a coordinated team, whose members carry out complementary and/or redundant tasks. DRDC research on cooperative teaming of UAVs covers in particular the development and implementation of cooperative control based on model predictive control. In the context of operations in complex environments, the present paper discusses the selected approach to cooperative control, and presents applications to formation flight, collision avoidance, real-time implementation and multi-processing, and fault-detection, isolation and recovery.

Defence R&D Canada (DRDC) is exploiting a synthetic environment to explore the use of multiple coordinated sensors to perform maritime surveillance. A distributed architecture is proposed in which teams from other DRDC labs, industry and academia can experiment with solutions based on constructive or virtual simulations that run locally at their facilities, but participate through a distributed simulation employing the High Level Architecture. The problem is set in the context of the surveillance of traffic routes and fishing vessels and consists, in its most general form, of a dynamic m-vehicle, n-target coordination problem that requires task assignment and trajectory generation components in the solution. An example solution to a reduced form of the problem that was generated with a human-in-the-loop simulator is provided.

The MultiAgent Tactical Sentry (MATS) project addressed a Canadian Forces (CF) requirement to remotely detect NBC threats. This requirement was met by integrating a suite of primary NBC sensors onto a remotely operated vehicular platform. From inception to completion, the project spanned 30 months. End user trials continue with the initial production run systems and consequently, the CF techniques, tactics, and procedures (TTPs) are evolving on a continual basis.

This paper describes the field-oriented philosophy of the Institute for Safety Security Rescue Technology (iSSRT) and summarizes the activities and lessons learned during calendar year 2005 of its two centers: the Center for Robot-Assisted Search and Rescue and the NSF Safety Security Rescue industry/university cooperative research center. In 2005, iSSRT participated in four responses (La Conchita, CA, Mudslides, Hurricane Dennis, Hurricane Katrina, Hurricane Wilma) and conducted three field experiments (NJTF-1, Camp Hurricane, Richmond, MO). The lessons learned covered mobility, operator control units, wireless communications, and general reliability. The work has collectively identified six emerging issues for future work. Based on these studies, a 10-hour, 1 continuing education unit credit course on rescue robotics has been created and is available. Rescue robots and sensors are available for loan upon request.

In this paper, we present preliminary work on a novel wearable joystick for gloves-on human/computer interaction in hazardous environments. Interacting with traditional input devices can be clumsy and inconvenient for the operator in hazardous environments due to the bulkiness of multiple system components and troublesome wires. During a collapsed structure search, for example, protective clothing, uneven footing, and "snag" points in the environment can render traditional input devices impractical. Wearable computing has been studied by various researchers to increase the portability of devices and to improve the proprioceptive sense of the wearer's intentions. Specifically, glove-like input devices to recognize hand gestures have been developed for general-purpose applications. But, regardless of their performance, prior gloves have been fragile and cumbersome to use in rough environments. In this paper, we present a new wearable joystick to remove the wires from a simple, two-degree of freedom glove interface. Thus, we develop a wearable joystick that is low cost, durable and robust, and wire-free at the glove. In order to evaluate the wearable joystick, we take into consideration two metrics during operator tests of a commercial robot: task completion time and path tortuosity. We employ fractal analysis to measure path tortuosity. Preliminary user test results are presented that compare the performance of both a wearable joystick and a traditional joystick.

With over 100 models of unmanned vehicles now available for military and civilian safety, security or rescue applications, it is important to for agencies to establish acceptance testing. However, there appears to be no general guidelines for what constitutes a reasonable acceptance test. This paper describes i) a preliminary method for acceptance testing by a customer of the mechanical and electrical components of an unmanned ground vehicle system, ii) how it has been applied to a man-packable micro-robot, and iii) discusses the value of testing both to ensure that the customer has a workable system and to improve design. The test method automated the operation of the robot to repeatedly exercise all aspects and combinations of components on the robot for 6 hours. The acceptance testing process uncovered many failures consistent with those shown to occur in the field, showing that testing by the user does predict failures. The process also demonstrated that the testing by the manufacturer can provide important design data that can be used to identify, diagnose, and prevent long-term problems. Also, the structured testing environment showed that sensor systems can be used to predict errors and changes in performance, as well as uncovering unmodeled behavior in subsystems.

We study the problem of dispersing a group of small robots in an unknown environment. The objective is to cover the environment as much as possible while staying within communications range. We assume there is no central control, the environment is unknown and with complex obstacles, the robots operate without any central control, and have only limited communications with other robots and limited sensing capabilities. We present algorithms and validate them experimentally in the Player/Stage simulation environment.

For the Wayfarer Project, funded by the US Army through TARDEC, we have developed technologies that enable manportable PackBot Wayfarer UGVs to perform autonomous reconnaissance in urban terrain. Each Wayfarer UGV can autonomously follow urban streets and building perimeters while avoiding obstacles and building a map of the terrain. Each UGV is equipped with a 3D stereo vision system, a 360-degree planar LIDAR, GPS, INS, compass, and odometry. The Hough transform is applied to LIDAR range data to detect building walls for street following and perimeter following. We have demonstrated Wayfarer's ability to autonomously follow roads in urban and rural environments, while building a map of the surrounding terrain. Recently, we have developed a ruggedized version of the Wayfarer Navigation Payload for use in rough terrain and all-weather conditions. The new payload incorporates a compact Tyzx G2 stereo vision module and a high-performance Athena Guidestar INS/GPS unit.

Many emerging UV (Unmanned Vehicle) cooperative control systems utilizing mission decomposition and generic UV management techniques are UAV (Unmanned Aerial Vehicle) oriented and transition well from model simulations to hardware due to the relative homogeneity of the air environment. Unmanned Surface Vehicles (USV's) and other ground borne vehicles, to function robustly, must have an additional onboard capacity to negotiate local environmental and indigenous operational factors in order to be commanded by a network, and this capacity is most easily delineated into Skill Sets. The Autonomous Maritime Navigation Program (AMN) is developing USV systems which target full intelligent autonomous operations and autonomy Skill Sets to allow USV's to perform unsupervised complex missions over extended time periods at the platform level with minimum human supervision. Importantly, this allows control systems developed for cooperating UV's to effectively control USV's by enabling local platform issues decision making at the platform level. Using a 40 foot laboratory boat, advanced on-board control, sensing, data fusion, physical plant and payload monitoring and management are being adapted and integrated as a system to replace traditional human crew functions. This paper discusses a path to achieve the goal of full USV autonomy equipped with skills to self manage, survive and navigate, and progress being made with enabling technology pieces. Initiatives and partnerships have been formed with academia, industry, and other DoD laboratories to these ends in both independent and collaborative RDT&E projects. Discussion includes ongoing work in sensing, data fusion, dynamic mission planning, execution and boat operations, and integration to JAUS/TCS control protocols.

Unmanned ground and air systems operating in collaboration have the potential to provide future Joint Forces a significant capability for operations in complex terrain. Collaborative Engagement Experiment (CEE) is a consolidation of separate Air Force, Army and Navy collaborative efforts within the Joint Robotics Program (JRP) to provide a picture of the future of unmanned warfare. The Air Force Research Laboratory (AFRL), Material and Manufacturing Directorate, Aerospace Expeditionary Force Division, Force Protection Branch (AFRL/MLQF), The Army Aviation and Missile Research, Development and Engineering Center (AMRDEC) Joint Technology Center (JTC)/Systems Integration Laboratory (SIL), and the Space and Naval Warfare Systems Center - San Diego (SSC San Diego) are conducting technical research and proof of principle experiments for an envisioned operational concept for extended range, three dimensional, collaborative operations between unmanned systems, with enhanced situational awareness for lethal operations in complex terrain. This paper describes the work by these organizations to date and outlines some of the plans for future work.

The University of Alabama in Huntsville (UAH) is currently investigating techniques and technologies for the integration of a small unmanned aerial vehicle (SUAV) with small unmanned ground vehicles (SUGV). Each vehicle has its own set of unique capabilities, but the efficient integration of the two for a specific application requires modifying and integrating both systems. UAH has been flying and testing an autonomously-controlled small helicopter, called the Flying Bassett (Base Airborne Surveillance and Sensing for Emergency Threat Tracking) for over a year. Recently, integrated operations were performed with four SUGVs, the Matilda (Mesa Robotics, Huntsville, AL), the US Navy Vanguard, the UAH Rover, and the Penetrator (Mesa Robotics). The program has progressed from 1) building an air and ground capability for video and infrared surveillance, 2) integration with ground vehicles in realistic scenarios, to 3) deployment and recovery of ground vehicles. The work was done with the cooperation of the US Army at Ft. Benning, GA and Redstone Arsenal, AL, the Federal Bureau of Investigation in Huntsville, AL, the US Naval Reserve in Knoxville, TN, and local emergency organizations. The results so far have shown that when the air and ground systems are employed together, their utility is greatly enhanced.

This paper presents an automated classification system for images based on their visual complexity. The image complexity is approximated using a clutter measure, and parameters for processing it are dynamically chosen. The classification method is part of a vision-based collision avoidance system for low altitude aerial vehicles, intended to be used during search and rescue operations in urban settings. The collision avoidance system focuses on detecting thin obstacles such as wires and power lines. Automatic parameter selection for edge detection shows a 5% and 12% performance improvement for medium and heavily cluttered images respectively. The automatic classification enabled the algorithm to identify near invisible power lines in a 60 frame video footage from a SUAV helicopter crashing during a search and rescue mission at hurricane Katrina, without any manual intervention.

In this paper, we will present the technique of automatic registration and mosaicking for the multispectral images acquired by a mini-UAV platform. The mini-UAV in the research is manufactured and operated by Air-O-Space Internationl (AOSI) L.L.C., where a 3-band multispectral sensor system captures data at green (550nm), red (650 nm), and NIR (820nm) bands. The imagery is converted to a digital format and downlinked to the ground station in real-time. Automatic image registration is needed to co-register these three band images so that the final commerical products, such as pseduo-CIR image and NDVI image (e.g., for agicultural study), can be generated in near real-time. There are two types of image registration approaches: area-based and feature-based. Since most of image scenes are about crop fields, trees, grass, and soil, where no prominent feature details can be easily extracted, so the area-based method is adopted. The control point detection is the key for the successful automatic image registration and mosaicking. In order to control the false alarms during the control point detection, the potential exploration area, i.e., region of interest, is searched first; to remove the inaccurate detected control points, control point selection is conducted based on the occurrence frequency of the resulant coordinate displacements. For image mosaicking where the rotational misalignment can be large, the rotation is adjusted before the control point detection, which can greatly mitigate the limitation of the area-based method. The overall turn-around time (from image acquisition to commercial product generation) is about a couple of hours. This cost-effective UAV system including the developed software is very supportive to the timely decision-making in practical applications, such as agricultual and forestry monitoring.

In this paper we present an algorithm for the autonomous navigation of an unmanned aerial vehicle (UAV) following a moving target. The UAV in consideration is a fixed wing aircraft that has physical constraints on airspeed and maneuverability. The target however is not considered to be constrained and can move in any general pattern. We show a single circular pattern navigation algorithm that works for targets moving at any speed with any pattern where other methods switch between different navigation strategies in different scenarios. Simulation performed takes into consideration that the aircraft also needs to visually track the target using a mounted camera. The camera is also controlled by the algorithm according to the position and orientation of the aircraft and the position of the target. Experiments show that the algorithm presented successfully tracks and follows moving targets.

Within the military, the Explosive Ordnance Disposal (EOD) community has been an early adopter of robotic capabilities. The Joint Service EOD (JSEOD) Program is in the process of fielding its third generation of robotic systems to the EOD technicians. Robots have been an invaluable asset to the EOD technician, and they have been critical to operations in Iraq as we prosecute the IED problem. This paper provides a brief history of past EOD robotic systems, a description of currently fielded and supported systems, and the future of robotic programs within the Joint Service EOD community.

The Defence R&D Canada (DRDC) has been given strategic direction to pursue research to increase the independence and effectiveness of military vehicles and systems. This led to the creation of the Autonomous Land Systems (ALS) project that was completed in 2005 with a successful demonstration of semi-autonomous UGVs in open partially vegetated environments. Cohort is a newly funded project that will work to devleop effective UxV teams for urban and complex environments. This paper will briefly discuss the state of the UGV research at the completion of ALS and other research projects supporting Cohort. The goals and challenges of Cohort will be outlined as well as the research plan that is involving many of DRDC's laboratories from across Canada.

This paper provides an overview of the development and demonstration of intelligent autonomy technologies for control of heterogeneous unmanned naval air and sea vehicles and describes some of the current limitations of such technologies. The focus is on modular technologies that support highly automated retasking and fully autonomous dynamic replanning for up to ten heterogeneous unmanned systems based on high-level mission objectives, priorities, constraints, and Rules-of-Engagement. A key aspect of the demonstrations is incorporating frequent naval operator evaluations in order to gain better understanding of the integrated man/machine system and its tactical utility. These evaluations help ensure that the automation can provide information to the user in a meaningful way and that the user has a sufficient level of control and situation awareness to task the system as needed to complete complex mission tasks. Another important aspect of the program is examination of the interactions of higher-level autonomy algorithms with other relevant components that would be needed within the decision-making and control loops. Examples of these are vision and other sensor processing algorithms, sensor fusion, obstacle avoidance, and other lower level vehicle autonomous navigation, guidance, and control functions. Initial experiments have been completed using medium and high-fidelity vehicle simulations in a virtual warfare environment and inexpensive surrogate vehicles in flight and in-water demonstrations. Simulation experiments included integration of multi-vehicle task allocation, dynamic replanning under constraints, lower level autonomous vehicle control, automatic assessment of the impact of contingencies on plans, management of situation awareness data, operator alert management, and a mixed-initiative operator interface. In-water demonstrations of a maritime situation awareness capability were completed in both a river and a harbor environment using unmanned surface vehicles and a buoy as surrogate platforms. In addition, a multiple heterogeneous vehicle demonstration was performed using five different types of small unmanned air and ground vehicles. This provided some initial experimentation with specifying tasking for high-level mission objectives and then mapping those objectives onto heterogeneous unmanned vehicles that each have different lower-level autonomy software. Finally, this paper will discuss lessons learned.

In May 2003, the Federal Republic of Germany and the Republic of France awarded a contract to RHEINMETALL LANDSYSTEME GmbH (Germany), MBDA (France) and THALES (France) for the joint development of a technology demonstrator for a vehicle-based close-in countermine system. The objective of this cooperation project, known as MMSR-SYDERA, is to show that, in a full-scale development program, it will be possible to meet the joint operational requirements issued by the German and French armies, which are based on the following missions: Fast route opening, Sensitive route opening and Area Clearing. In order to fulfill the three different missions and deal with an extensive array of mine threats, the MMSR-SYDERA countermine system combines two modes of countermine operation, i.e. triggering mines at a safe distance or with only easy-to-repair-damages (so-called decoying) or detecting mines with sensors for low-order clearing. Thus, the plan requires for the MMSR-SYDERA system to be composed of five vehicles deployed in different configurations in a convoy on the roads to be cleared. One year after the first paper, this article reports the status of the Demonstrator as well as the first vehicle level trials, and focuses on specific topics like the embedded safety components and behaviors linked to the remote control operation, and the wireless links used between the vehicles. After industrial system trials in the second half of 2006, Customer's evaluations of the system demonstrator will be carried out at the beginning of 2007.

The Army's ARV (Armed Robotic Vehicle) Robotic Technologies (ART) program is working on the development of various technological thrusts for use in the robotic forces of the future. The ART program will develop, integrate and demonstrate the technology required to advance the maneuver technologies (i.e., perception, mobility, tactical behaviors) and increase the survivability of unmanned platforms for the future force while focusing on reducing the soldiers' burden by providing an increase in vehicle autonomy coinciding with a decrease in the total number user interventions required to control the unmanned assets. This program will advance the state of the art in perception technologies to provide the unmanned platform an increasingly accurate view of the terrain that surrounds it; while developing tactical/mission behavior technologies to provide the Unmanned Ground Vehicle (UGV) the capability to maneuver tactically, in conjunction with the manned systems in an autonomous mode. The ART testbed will be integrated with the advanced technology software and associated hardware developed under this effort, and incorporate appropriate mission modules (e.g. RSTA sensors, MILES, etc.) to support Warfighter experiments and evaluations (virtual and field) in a military significant environment (open/rolling and complex/urban terrain). The outcome of these experiments as well as other lessons learned through out the program life cycle will be used to reduce the current risks that are identified for the future UGV systems that will be developed under the Future Combat Systems (FCS) program, including the early integration of an FCS-like autonomous navigation system onto a tracked skid steer platform.

Road sign detection is important to a robotic vehicle that automatically drives on roads. In this paper, road signs are detected by means of rules that restrict color and shape and require signs to appear only in limited regions in an image. They are then recognized using a template matching method and tracked through a sequence of images. The method is fast and can easily be modified to include new classes of signs. The road sign detection is used as part of a control system that autonomously drives a vehicle over paved roads. The primary application is to detect intersections, which are usually marked with street name signs or stop signs. An estimate of the range to the sign is computed based on the size of the sign and provides a cue to intersection detection software and driving control.

The RiSE robot is a biologically inspired, six legged climbing robot, designed for general mobility in scansorial (vertical walls, horizontal ledges, ground level) environments. It exhibits ground reaction forces that are similar to animal climbers and does not rely on suction, magnets or other surface-dependent specializations to achieve adhesion and shear force. We describe RiSE's body and leg design as well as its electromechanical, communications and computational infrastructure. We review design iterations that enable RiSE to climb 90° carpeted, cork covered and (a growing range of) stucco surfaces in the quasi-static regime.

We discuss the gait generation and control architecture of a bioinspired climbing robot that presently climbs a variety of vertical surfaces, including carpet, cork and a growing range of stucco-like surfaces in the quasi-static regime. The initial version of the robot utilizes a collection of gaits (cyclic feed-forward motion patterns) to locomote over these surfaces, with each gait tuned for a specific surface and set of operating conditions. The need for more flexibility in gait specification (e.g., adjusting number of feet on the ground), more intricate shaping of workspace motions (e.g., shaping the details of the foot attachment and detachment trajectories), and the need to encode gait "transitions" (e.g., tripod to pentapod gait structure) has led us to separate this trajectory generation scheme into the functional composition of a phase assigning transformation of the "clock space" (the six dimensional torus) followed by a map from phase into leg joints that decouples the geometric details of a particular gait. This decomposition also supports the introduction of sensory feedback to allow recovery from unexpected event and to adapt to changing surface geometries.

Climbing animal's feet use combinations of interlocking and bonding mechanisms in a staggering array of designs. The most successful climbers' feet exhibit a complex hierarchy of varied mechanical structures at multiple scales, combining small appendages that generate shear or adhesive forces with compliant suspension systems that promote intimate contact with surfaces. Recent progress is presented in mechanical and materials design that integrates novel dry adhesive and microspine structures mounted on passively compliant suspensions into successively improved generations of feet targeted at the RiSE (Robots in Scansorial Environments) family of climbing robots. The current version can ascend 90° carpeted, cork covered and a growing range of stucco surfaces in the quasi-static regime. Specifications of a "public interface" for integrating a broad range of synthetic appendages into the foot assemblies are presented in the hopes of encouraging as large as possible a community of MEMs and Nanomaterials designers to submit adhesive or friction enhancing materials for operational tests using the robot.

Robots can serve as hardware models for testing biological hypotheses. Both for this reason and to improve the state of the art of robotics, we strive to incorporate biological principles of insect locomotion into robotic designs. Previous research has resulted in a line of robots with leg designs based on walking and climbing movements of the cockroach Blaberus discoidalis. The current version, Robot V, uses muscle-like Braided Pneumatic Actuators (BPAs). In this paper, we use recorded electromyograms (EMGs) to drive robot joint motion. A muscle activation model was developed that transforms EMGs recorded from behaving cockroaches into appropriate commands for the robot. The transform is implemented by multiplying the EMG by an input gain thus generating an input pressure signal, which is used to drive a one-way closed loop pressure controller. The actuator then can be modeled as a capacitance with input rectification. The actuator exhaust valve is given a leak rate, making the transform a leaky integrator for air pressure, which drives the output force of the actuator. We find parameters of this transform by minimizing the difference between the robot motion produced and that observed in the cockroach. Although we have not reproduced full-amplitude cockroach motion using this robot, results from evaluation on reduced-amplitude cockroach angle data strongly suggest that braided pneumatic actuators can be used as part of a physical model of a biological system.

In this paper, we generate gaits for mixed systems, that is, dynamic systems that are subject to a set of nonholonomic constraints. What is unique about mixed systems is that when we express their dynamics in body coordinates, the motion of these systems can be attributed to two decoupled terms: the geometric and dynamic phase shifts. In our prior work, we analyzed systems whose dynamic phase shift was null by definition. Purely mechanical and principally kinematic systems are two classes of mechanical systems that have this property. We generated gaits for these two classes of systems by intuitively evaluating their geometric phase shift and relating it to a volume integral under well-defined height functions. One of the contributions of this paper is to present a similar intuitive approach for computing the dynamic phase shift. We achieve this, by introducing a new scaled momentum variable that not only simplifies the momentum evolution equation but also allows us to introduce a new set of well-defined gamma functions which enable us to intuitively evaluate the dynamic phase shift. More specifically, by analyzing these novel gamma functions in a similar way to how we analyzed height functions, and by analyzing the sign-definiteness of the scaled momentum variable, we are able to ensure that the dynamic phase shift is non-zero solely along the desired fiber direction. Finally, we also introduce a novel mechanical system, the variable inertia snakeboard, which is a generalization of the original snakeboard that was previously studied in the literature. Not only does this general system help us identify regions of the base space where we can not define a certain type of gaits, but also it helps us verify the generality and applicability of our gait generation approach.

The results of two usability experiments evaluating an interface for the operation of OctArm, a biologically inspired robotic arm modeled after an octopus tentacle, are reported. Due to the many degrees-of-freedom (DOF) for the operator to control, such 'continuum' robotic limbs provide unique challenges for human operators because they do not map intuitively. Two modes have been developed to control the arm and reduce the DOF under the explicit direction of the operator. In coupled velocity (CV) mode, a joystick controls changes in arm curvature. In end-effector (EE) mode, a joystick controls the arm by moving the position of an endpoint along a straight line. In Experiment 1, participants used the two modes to grasp objects placed at different locations in a virtual reality modeling language (VRML). Objective measures of performance and subjective preferences were recorded. Results revealed lower grasp times and a subjective preference for the CV mode. Recommendations for improving the interface included providing additional feedback and implementation of an error recovery function. In Experiment 2, only the CV mode was tested with improved training of participants and several changes to the interface. The error recovery function was implemented, allowing participants to reverse through previously attained positions. The mean time to complete the trials in the second usability test was reduced by more than 4 minutes compared with the first usability test, confirming the interface changes improved performance. The results of these tests will be incorporated into future versions of the arm and improve future usability tests.

This paper introduces a new generation wall-climbing robots named as City-climber, which has the capabilities to climb walls, walk on ceilings, and transit between different surfaces. Unlike the traditional wall-climbing robots, the Cityclimber robots use aerodynamic attraction which achieves good balance between strong adhesion force and high mobility. Since the City-climber robots don't require perfect sealing as the vacuum suction technique does, the robots can move on virtually any kinds of smooth or rough surfaces. The other novelties of the City-climber robots are the modular design and high-performance on-board processing unit. The former feature achieves booth fast motion of each module on planar surfaces and smooth transition between the surfaces by a set of two modules. The latter feature makes the real-time signal processing and autonomous operation possible. We envision that the City-climber robots be used in urban environments for search and rescue, weapon/tool delivery, inspection and surveillance purposes. To increase the hardware and software reconfigurability, the self-contained City-climber robots use system-on-programmable-chip (SoPC) technology for on-board perception and motion control. The video display several versions of the City-Climber prototypes, illustrating the main areas of functionality and results of several key experimental tests, including 4.2kg payload, operation on rough surfaces, locomotion over surface gaps, and inverted operation on ceiling, to name a few.

Evolution has produced organisms whose locomotive agility and adaptivity mock the difficulty faced by robotic scientists. The problem of locomotion, which nature has solved so well, is surprisingly complex and difficult. We explore the ability of an artificial eight-legged arachnid, or animat, to autonomously learn a locomotive gait in a three-dimensional environment. We take a physics-based approach at modeling the world and the virtual body of the animat. The arachnid-like animat learns muscular control functions using simulated annealing techniques, which attempts to maximize forward velocity and minimize energy expenditure. We experiment with varying the weight of these parameters and the resulting locomotive gaits. We perform two experiments in which the first is a naive physics model of the body and world which uses point-masses and idealized joints and muscles. The second experiment is a more realistic simulation using rigid body elements with distributed mass, friction, motors, and mechanical joints. By emphasizing physical aspects we wish to minimize, a number of interesting gaits emerge.

This paper describes the development of the octopus biology inspired OctArm series of soft robot manipulators. Each OctArm is constructed using air muscle extensors with three control channels per section that provide two axis bending and extension. Within each section, mesh and plastic coupler constraints prevent extensor buckling. OctArm IV is comprised of four sections connected by endplates, providing twelve degrees of freedom. Performance of OctArm IV is characterized in a lab environment. Using only 4.13 bar of air pressure, the dexterous distal section provides 66% extension and 380° of rotation in less than .5 seconds. OctArm V has three sections and, using 8.27 bar of air pressure, the strong proximal section provides 890 N and 250 N of vertical and transverse load capacity, respectively. In addition to the in-lab testing, OctArm V underwent a series of field trials including open-air and in-water field tests. Outcomes of the trials, in which the manipulator demonstrated the ability for adaptive and novel manipulation in challenging environments, are described. OctArm VI is designed and constructed based on the in-lab performance, and the field testing of its predecessors. Implications for the deployment of soft robots in military environments are discussed.

In this paper we present ideas toward solving constrained optimization problems in a spatially-distributed mobile sensor/actuator network using decentralized computation. Notionally, each node of the network is considered to be a distributed computational unit that evolves its state according to a pre-defined rule. First, we show how to design coordination rules to ensure that the global state of the network evolves to the solution of a prescribed constrained optimization problem. Our strategy uses a recurrent neural network structure to solve the optimization problem in a global way. Next, we introduce ideas for the case when there is an absence of an allto- all communication topology. Assuming each node can only communicate locally with its 'nearest neighbors,' our approach is to use the notion of a consensus variable protocol that implements a distributed observer, enabling local nodes to asymptotically obtain global information using only nearest neighbor communications. Finally, we suggest superimposing the neural network structure on top of this distributed observer to solve the global optimization problem using only local and nearest neighbor communications.

Much work has been undertaken recently toward the development of low-power, high-performance sensor networks. There are many static remote sensing applications for which this is appropriate. The focus of this development effort is applications that require higher performance computation, but still involve severe constraints on power and other resources. Toward that end, we are developing a reconfigurable computing platform for miniature robotic and human-deployed sensor systems composed of several mobile nodes. The system provides static and dynamic reconfigurability for both software and hardware by the combination of CPU (central processing unit) and FPGA (field-programmable gate array) allowing on-the-fly reprogrammability. Static reconfigurability of the hardware manifests itself in the form of a "morphing bus" architecture that permits the modular connection of various sensors with no bus interface logic. Dynamic hardware reconfigurability provides for the reallocation of hardware resources at run-time as the mobile, resource-constrained nodes encounter unknown environmental conditions that render various sensors ineffective. This computing platform will be described in the context of work on chemical/biological/radiological plume tracking using a distributed team of mobile sensors. The objective for a dispersed team of ground and/or aerial autonomous vehicles (or hand-carried sensors) is to acquire measurements of the concentration of the chemical agent from optimal locations and estimate its source and spread. This requires appropriate distribution, coordination and communication within the team members across a potentially unknown environment. The key problem is to determine the parameters of the distribution of the harmful agent so as to use these values for determining its source and predicting its spread. The accuracy and convergence rate of this estimation process depend not only on the number and accuracy of the sensor measurements but also on their spatial distribution over time (the sampling strategy). For the safety of a human-deployed distribution of sensors, optimized trajectories to minimize human exposure are also of importance. The systems described in this paper are currently being developed. Parts of the system are already in existence and some results from these are described.

Robot and sensor networks are needed for safety, security, and rescue applications such as port security and reconnaissance during a disaster. These applications rely on real-time transmission of images, which generally saturate the available wireless network infrastructure. Knowledge-based compression is a method for reducing the video frame transmission rate between robots or sensors and remote operators. Because images may need to be archived as evidence and/or distributed to multiple applications with different post processing needs, lossy compression schemes, such as MPEG, H.26x, etc., are not acceptable. This work proposes a lossless video server system consisting of three classes of filters (redundancy, task, and priority) which use different levels of knowledge (local sensed environment, human factors associated with a local task, and relative global priority of a task) at the application layer of the network. It demonstrates the redundancy and task filters for a realistic robot search scenario. The redundancy filter is shown to reduce the overall transmission bandwidth by 24.07% to 33.42%, and, when combined with the task filter, reduces overall transmission bandwidth by 59.08%to 67.83%. By itself, the task filter has the capability to reduce transmission bandwidth by 32.95% to 33.78%. While knowledge-based compression generally does not reach the same levels of reduction as MPEG, there are instances where the system outperforms MPEG encoding.

We present a method for estimating the global uncertainty of epipolar geometry with applications to autonomous vehicle navigation. Such uncertainty information is necessary for making informed decisions regarding the confidence of a motion estimate, since we must otherwise accept the estimate without any knowledge of the probability that the estimate is in error. For example, we may wish to fuse visual estimates with information from GPS and inertial sensors, but without uncertainty information, we have no principled way to do so. Ideally, we would perform a full search over the 7-dimensional space of fundamental matrices to yield an estimate and its related uncertainty. However, searching this space is computationally infeasible. As a compromise between fully representing posterior likelihood over this space and producing a single estimate, we represent the uncertainty over the space of translation directions in a calibrated framework. In contrast to finding a single estimate, representing the posterior likelihood is always a well-posed problem, albeit an often computationally challenging one. Given the posterior likelihood, we derive a confidence interval around the motion estimate. We verify the correctness of the confidence interval using synthetic data and show examples of uncertainty estimates using vehicle-mounted camera sequences.

Unmanned Ground Vehicles (UGVs) have advantages over people in a number of different applications, ranging from sentry duty, scouting hazardous areas, convoying goods and supplies over long distances, and exploring caves and tunnels. Despite recent advances in electronics, vision, artificial intelligence, and control technologies, fully autonomous UGVs are still far from being a reality. Currently, most UGVs are fielded using tele-operation with a human in the control loop. Using tele-operations, a user controls the UGV from the relative safety and comfort of a control station and sends commands to the UGV remotely. It is difficult for the user to issue higher level commands such as patrol this corridor or move to this position while avoiding obstacles. As computer vision algorithms are implemented in hardware, the UGV can easily become partially autonomous. As Field Programmable Gate Arrays (FPGAs) become larger and more powerful, vision algorithms can run at frame rate. With the rapid development of CMOS imagers for consumer electronics, frame rate can reach as high as 200 frames per second with a small size of the region of interest. This increase in the speed of vision algorithm processing allows the UGVs to become more autonomous, as they are able to recognize and avoid obstacles in their path, track targets, or move to a recognized area. The user is able to focus on giving broad supervisory commands and goals to the UGVs, allowing the user to control multiple UGVs at once while still maintaining the convenience of working from a central base station. In this paper, we will describe a novel control system for the control of semi-autonomous UGVs. This control system combines a user interface similar to a simple tele-operation station along with a control package, including the FPGA and multiple cameras. The control package interfaces with the UGV and provides the necessary control to guide the UGV.

Space and Naval Warfare Systems Center, San Diego (SSC San Diego) has developed an unmanned vehicle and sensor operator control interface capable of simultaneously controlling and monitoring multiple sets of heterogeneous systems. The modularity, scalability and flexible user interface of the Multi-robot Operator Control Unit (MOCU) accommodates a wide range of vehicles and sensors in varying mission scenarios. MOCU currently controls all of the SSC San Diego developmental vehicles (land, air, sea, and undersea), including the SPARTAN Advanced Concept Technology Demonstration (ACTD) Unmanned Surface Vehicle (USV), the iRobot PackBot, and the Family of Integrated Rapid Response Equipment (FIRRE) vehicles and sensors. This paper will discuss how software and hardware modularity has allowed SSC San Diego to create a single operator control unit (OCU) with the capability to control a wide variety of unmanned systems.

The Networked Intelligence, Surveillance, and Reconnaissance (NISR) project integrates robotic resources into Composeable FORCEnet to control and exploit unmanned systems over extremely long distances. The foundations are built upon FORCEnet-the U.S. Navy's process to define C4ISR for net-centric operations-and the Navy Unmanned Systems Common Control Roadmap to develop technologies and standards for interoperability, data sharing, publish-and-subscribe methodology, and software reuse. The paper defines the goals and boundaries for NISR with focus on the system architecture, including the design tradeoffs necessary for unmanned systems in a net-centric model. Special attention is given to two specific scenarios demonstrating the integration of unmanned ground and water surface vehicles into the open-architecture web-based command-and-control information-management system of Composeable FORCEnet. Planned spiral development for NISR will improve collaborative control, expand robotic sensor capabilities, address multiple domains including underwater and aerial platforms, and extend distributive communications infrastructure for battlespace optimization for unmanned systems in net-centric operations.

No single UxV is perfectly suited to all task assignments. A homogeneous UxV team, for example, a troop of identical UGVs, brings redundancy and reliability to a specific class of tasks. Heterogeneous UxV teams, for example, a troop of UGVs, a flight of low flying rotorcraft, and a high flying UAV, provide increased capability. They can tackle multiple tasks simultaneously through cooperative decision making, distributed task allocation, and collective mapping. Together, they can convoy payloads, provide communications, observe targets, shield troops, and, ultimately, deliver munitions. nUxVs have the potential to share, learn, and adapt information between like platforms and across platform types, to produce expanded capability and greater reliability. Current research exploits simple vehicle state exchange, communications relay and formation keeping. Our near-term research areas include map sharing and integration, task coordination, and heterogeneous nUxV teaming. Future research will address military nUxV C2; nUxV capability definition and understanding; behaviour-based and reactive nUxVs, emergence and stigmergy; and collaboration and interaction between human-robot teams.

There are many challenges in the area of interoperability of unmanned systems: increasing levels of autonomy, teaming and collaboration, long endurance missions, integration with civilian and military spaces. Several currently available methods and technologies may aid in meeting these and other challenges: consensus standards development, formal methods, model-based engineering, knowledge and ontology representation, agent-based systems, and plan language research. We believe the future of unmanned systems interoperability depends on the integration of these methods and technologies into a domain-independent plan language for unmanned systems.

We have developed PRIDE (Prediction In Dynamic Environments), a hierarchical multi-resolutional framework for moving object prediction that incorporates multiple prediction algorithms into a single, unifying framework. PRIDE incorporates two approaches for the prediction of the future location of moving objects at various levels of resolution at the frequency and level of abstraction necessary for planners at different levels within the hierarchy. These approaches, termed long-term (LT) and short-term (ST) predictions, respectively, are based on situation recognition and vehicle models for moving object prediction using sensor data. Our recent efforts have demonstrated the ability to use the results of the short-term prediction algorithms to strengthen/weaken the estimates of the long-term prediction algorithms. Based on previous experiments, we have found that the short-term prediction algorithms perform best when predicting on the order of a few seconds into the future and that the longer-term prediction algorithms are best at predicting on the order of several seconds into the future. In this paper, we explore the time window in which both the short-term and the long-term prediction algorithms provide reasonable results. Additionally, we describe a methodology by which we can determine the time point at which the short-term prediction algorithm no longer provides results within an acceptable predefined error threshold. We provide experimental results in an autonomous on-road driving scenario using AutoSim, a high-fidelity simulation tool that models details about road networks, including individual lanes, lane markings, intersections, legal intersection traversability, etc.

This paper will highlight the standardization work product (to date) developed by Committees F38 and F41, with specific focus on the electronic tools employed during the development process and the impact of the standards activity upon the UAV and UUV industries. Sub-topics will include the scope and structure of the committees, the status of new and existing work items, and the process by which ASTM International standards are developed.

Urban Search and Rescue Simulation (USARSim) is an open source package that provides a high-resolution, physics based simulation of robotic platforms. The package provides models of several common robotic platforms and sensors as well as sample worlds and a socket interface into a commonly used commercial-off-the-shelf (COTS) simulation package. Initially introduced to support the development of search and rescue robots, USARSim has proved to be a tool with a broader scope, from robot education to human robot interfaces, including cooperation, and more. During Robocup 2006, a new competition based on USARSim will be held in the context of the urban search and rescue competitions. The Mobility Open Architecture Simulation and Tools (MOAST) is a framework that builds upon the 4-D Real-time Control Systesm (4D/RCS) architecture to analyze the performance of autonomous vehicles and multiagent systems. MOAST provides controlled environments that allow for the transparent transference of data between a matrix of real and virtual components. This framework is glued together through well-defined interfaces and communications protocols, and detailed specifications on individual subsystem input/output (IO). This allows developers to freely swap components and analyze the effect on the overall system by means of comparison to baseline systems with a limited set of functionality. When taken together, the combined USARSim/MOAST system may be used to provide a comprehensive development and testing environment for complex robotic systems. This paper will provide an overview of each system and describe how the combined system may be used for stand-alone simulated development and test, or hardware-in-the-loop development and testing of autonomous mobile robot systems.

This paper describes and evaluates a vision system that accurately segments unstructured, non-homogeneous roads of arbitrary shape under various lighting conditions. The idea behind the road following algorithm is the segmentation of road from background through the use of color models. Data are collected from a video camera mounted on a moving vehicle. In each frame, color models of the road and background are constructed. The color models are used to calculate the probability that each pixel in a frame is a member of the road class. Temporal fusion of these road probabilities helps to stabilize the models, resulting in a probability map that can be thresholded to determine areas of road and non-road. Performance evaluation follows the approach described in Hong et al1. We evaluate the algorithm's performance with annotated frames of video data. This allows us to compute the false positive and false negative ratios. False positives refer to non-road areas in the image that were classified by the system as road, while false negatives refer to road areas classified as non-road. We use the sum of false positives and false negatives as an overall classification error calculated for each frame of the video sequence. After the error is calculated for each frame, we determine the statistics of the classification error throughout the whole video sequence. The overall classification error per frame allows us to compare the performance of several algorithms on the same frame, and we can analyze the overall performance of individual algorithms using their classification statistics.

In this paper, we describe work in performance standards for urban search and rescue (USAR) robots begun in 2004 by the Department of Homeland Security. This program is being coordinated by the National Institute of Standards and Technology and will result in consensus standards developed through ASTM International, under the Operational Equipment Subcommittee of their Homeland Security Committee. The first phase of the program involved definition of requirements by subject matter experts. Responders participated in a series of workshops to identify deployment categories for robots, performance categories, and ranges of acceptable or target performance in the various categories. Over one hundred individual requirements were identified, within main categories such as Human-System Interaction, Logistics, Operating Environment, and System (which includes Chassis, Communications, Mobility, Payload, Power, and Sensing). To ensure that the robot developers and eventual end users work closely together, "responders meet robots" events at situationally relevant sites are being held to refine and extend the performance requirements and develop standard test methods. The results of these standard performance tests will be captured in a compendium of existing and developmental robots with classifications and descriptors to differentiate particular robotic capabilities. This, along with ongoing efforts to categorize situational USAR constraints such as building collapse types or the presence of hazardous materials, will help responders match particular robotic capabilities to response needs. In general, these efforts will enable responders to effectively use robotic tools to enhance their effectiveness while reducing risk to personnel during disasters.

As unmanned ground vehicles take on more and more intelligent tasks, determination of potential obstacles and accurate estimation of their position become critical for successful navigation and path planning. The performance analysis of obstacle mapping and unmanned vehicle positioning in outdoor environments is the subject of this paper. Recently, the National Institute of Standards and Technology's (NIST) Intelligent Systems Division has been a part of the Defense Advanced Research Project Agency LAGR (Learning Applied to Ground Robots) Program. NIST's objective for the LAGR Project is to insert learning algorithms into the modules that make up the NIST 4D/RCS (Four Dimensional/Real-Time Control System) standard reference model architecture which has been successfully applied to many intelligent systems. We detail world modeling techniques used in the 4D/RCS architecture and then analyze the high precision maps generated by the vehicle world modeling algorithms as compared to ground truth obtained from an independent differential GPS system operable throughout most of the NIST campus. This work has implications, not only for outdoor vehicles but also, for indoor automated guided vehicles where future systems will have more and more onboard intelligence requiring non-contact sensors to provide accurate vehicle and object positioning.

In this paper, we present solutions for tracking the 3D pose (location and orientation) of robot or vehicle undergoing general motion (6 degrees of freedom, rotation and translation) based on video streams captured by distributed aperture passive sensor system. A novel algorithm for multi-camera visual odometry is described. Previous published methods for visual odometry have used video streams from 1, 2 or 3 cameras in a monocular binocular or trinocular configurations. In this paper, we present general methods and results for visual odometry for a fixed configuration or known configuration of an arbitrary number of cameras. The images from the different cameras may have no overlap what so ever. The relative pose and configuration of the cameras comprising the distributed aperture system is assumed to be pre-calibrated and known at any time instant. We demonstrate that we can very accurately and robustly track the vehicle pose using the distributed aperture system.

MDARS is a Semi-autonomous unmanned ground vehicle with intrusion detection & assessment, product & barrier assessment payloads. Its functions include surveillance, security, early warning, incident first response and product and barrier status primarily focused on a depot/munitions security mission at structured/semi-structured facilities. MDARS is in Systems Development and Demonstration (SDD) under the Product Manager for Force Protection Systems (PM-FPS). MDARS capabilities include semi-autonomous navigation, obstacle avoidance, motion detection, day and night imagers, radio frequency tag inventory/barrier assessment and audio challenge and response. Four SDD MDARS Patrol Vehicles have been undergoing operational evaluation at Hawthorne Army Depot, NV (HWAD) since October 2004. Hawthorne personnel were trained to administer, operate and maintain the system in accordance with the US Army Military Police School (USAMPS) Concept of Employment and the PM-FPS MDARS Integrated Logistic Support Plan. The system was subjected to intensive periods of evaluation under the guidance and control of the Army Test and Evaluation Center (ATEC) and PM-FPS. Significantly, in terms of User acceptance, the system has been under the "operational control" of the installation performing security and force protection missions in support of daily operations. This evaluation is intended to assess MDARS operational effectiveness in an operational environment. Initial observations show that MDARS provides enhanced force protection, can potentially reduce manpower requirements by conducting routine tasks within its design capabilities and reduces Soldier exposure in the initial response to emerging incidents and situations. Success of the MDARS program has been instrumental in the design and development of two additional robotic force protection programs. The first was the USAF Force Protection Battle Lab sponsored Remote Detection Challenge & Response (REDCAR) concept demonstration executed by the Air Force Robotics Lab (AFRL). The REDCAR used an MDARS PUV as the central robotic technology and expanded the concept to incorporate a smaller high speed platform (SCOUT) equipped with lethal, non-lethal and challenge components as an engagement platform and, in a marsupial configuration on the MDARS, a small UGV that can be deployed to investigate close quarters areas. The Family of Integrated Rapid Response Equipment (FIRRE) program further expands these concepts by incorporating and adapting other mobile/tactical force protection equipment with a more robust Unmanned Ground Vehicle into an "Expeditionary" configuration to provide the current force with a rapidly deployable force protection system that can operate in austere less structured and protected environments. A USAMPS/ MANCEN sponsored "FIRRE System Demonstration" in Iraq is scheduled to begin in FY '07.

With the large number of Improvised Explosive Devices (IEDs) and Unexploded Ordnance (UXO) being encountered during recent military operations, there exists a need for Explosive Ordnance Disposal (EOD) mobile robots. These robots are predominately used for surveillance and neutralization of these explosive threats from a safe distance. The nature of the mission means that these vehicles are prone to being damaged or destroyed. Current commercially available systems, although capable of performing the mission, are costly and in too short of supply to be lost or damaged in large numbers. At last year's SPIE conference the NAVEODTECHDIV proposed an alternative: a low cost, mobile robot which used commercial off-the-shelf (COTS) parts and was tailored to the types of missions that EOD soldiers commonly perform. The prototype of this low-cost robot, the RAMBOT (Readily Available Maintainable Robot), has been continuously improved over the past year. There have been significant improvements to the original design in the areas of communication, manipulation, and electronics. The result of this work is a final prototype design, which is currently undergoing extensive testing to characterize its capabilities. Some of these tests include vehicle characteristics such as vehicle speed and mobility, vehicle weight and size, as well as maximum effective communication range, susceptibility to temperature, manipulator load limitations, and battery longevity. This conference paper will present the design changes to the robot and fully report on the results from the test series conducted thus far.

The Family of Integrated Rapid Response Equipment (FIRRE) is an advanced technology demonstration program intended to develop a family of affordable, scalable, modular, and logistically supportable unmanned systems to meet urgent operational force-protection needs and requirements worldwide. The near-term goal is to provide the best available unmanned ground systems to the warfighter in Iraq and Afghanistan. The overarching long-term goal is to develop a fully-integrated, layered force-protection system of systems for our forward deployed forces that is networked with the future force C4ISR systems architecture. The intent of the FIRRE program is to reduce manpower requirements, enhance force-protection capabilities, and reduce casualties through the use of unmanned systems. FIRRE is sponsored by the Office of the Under Secretary of Defense, Acquisitions, Technology and Logistics (OUSD AT&L), and is managed by the Product Manager, Force Protection Systems (PM-FPS), Fort Belvior, VA. The command-and-control element of FIRRE is the Joint Battlespace Command and Control System (JBC2S) for manned and unmanned assets, which is based upon the Mobile Detection Assessment Response System (MDARS) Multiple Resource Host Architecture (MRHA), modified to operate as a single application program using standard DoD mapping and data distribution services. JBC2S is an evolution of the MRHA that leverages over 10 years of development in unmanned systems command-and-control. It implements the functionality of the MRHA under the dynamically configurable and highly modular architecture of the Multi-Robot Operator Control Unit (MOCU). JBC2S is a network-centric, geospatial command and control system that allows the field commander and above to plan and execute missions utilizing multiple and disparate manned and unmanned assets. It utilizes standard map formats (GeoTIFF, DNC, CADRG) for displaying map data and for tracking asset placement and movement.

The Technology Transfer project employs a spiral development process to enhance the functionality and autonomy of mobile robot systems in the Joint Robotics Program (JRP) Robotic Systems Pool by converging existing component technologies onto a transition platform for optimization. An example of this approach is the implementation of advanced computer vision algorithms on small mobile robots. We demonstrate the implementation and testing of the following two algorithms useful on mobile robots: 1) object classification using a boosted Cascade of classifiers trained with the Adaboost training algorithm, and 2) human presence detection from a moving platform. Object classification is performed with an Adaboost training system developed at the University of California, San Diego (UCSD) Computer Vision Lab. This classification algorithm has been used to successfully detect the license plates of automobiles in motion in real-time. While working towards a solution to increase the robustness of this system to perform generic object recognition, this paper demonstrates an extension to this application by detecting soda cans in a cluttered indoor environment. The human presence detection from a moving platform system uses a data fusion algorithm which combines results from a scanning laser and a thermal imager. The system is able to detect the presence of humans while both the humans and the robot are moving simultaneously. In both systems, the two aforementioned algorithms were implemented on embedded hardware and optimized for use in real-time. Test results are shown for a variety of environments.

Many envisioned applications of mobile robotic systems require the robot to navigate in complex urban environments. This need is particularly critical if the robot is to perform as part of a synergistic team with human forces in military operations. Historically, the development of autonomous navigation for mobile robots has targeted either outdoor or indoor scenarios, but not both, which is not how humans operate. This paper describes efforts to fuse component technologies into a complete navigation system, allowing a robot to seamlessly transition between outdoor and indoor environments. Under the Joint Robotics Program's Technology Transfer project, empirical evaluations of various localization approaches were conducted to assess their maturity levels and performance metrics in different exterior/interior settings. The methodologies compared include Markov localization, global positioning system, Kalman filtering, and fuzzy-logic. Characterization of these technologies highlighted their best features, which were then fused into an adaptive solution. A description of the final integrated system is discussed, including a presentation of the design, experimental results, and a formal demonstration to attendees of the Unmanned Systems Capabilities Conference II in San Diego in December 2005.

The Family of Integrated Rapid Response Equipment (FIRRE) is an advanced technology demonstration program intended to develop a family of affordable, scalable, modular, and logistically supportable unmanned systems to meet urgent operational force protection needs and requirements worldwide. The near-term goal is to provide the best available unmanned ground systems to the warfighter in Iraq and Afghanistan. The overarching long-term goal is to develop a fully-integrated, layered force protection system of systems for our forward deployed forces that is networked with the future force C4ISR systems architecture. The intent of the FIRRE program is to reduce manpower requirements, enhance force protection capabilities, and reduce casualties through the use of unmanned systems. FIRRE is sponsored by the Office of the Under Secretary of Defense, Acquisitions, Technology and Logistics (OUSD AT&L), and is managed by the Product Manager, Force Protection Systems (PM-FPS). The FIRRE Command and Control (C2) Station supports two operators, hosts the Joint Battlespace Command and Control Software for Manned and Unmanned Assets (JBC2S), and will be able to host Mission Planning and Rehearsal (MPR) software. The C2 Station consists of an M1152 HMMWV fitted with an S-788 TYPE I shelter. The C2 Station employs five 24" LCD monitors for display of JBC2S software [1], MPR software, and live video feeds from unmanned systems. An audio distribution system allows each operator to select between various audio sources including: AN/PRC-117F tactical radio (SINCGARS compatible), audio prompts from JBC2S software, audio from unmanned systems, audio from other operators, and audio from external sources such as an intercom in an adjacent Tactical Operations Center (TOC). A power distribution system provides battery backup for momentary outages. The Ethernet network, audio distribution system, and audio/video feeds are available for use outside the C2 Station.

The Space and Naval Warfare Systems Center, San Diego (SSC San Diego) is conducting a number of robotic research, development, evaluation, fielding, and combat-support missions and projects in support of Joint Robotics Program (JRP) goals. These include: Man-Portable Robotic System, Unmanned Surface Vessel, Automatically Deployed Communication Relays, Autonomous UAV Mission System, Robotic Systems Pool, Family of Integrated Rapid Response Equipment, and the Technology Transfer project. This paper summarizes the recent accomplishments and current status of these efforts, many of which are individually presented in more detail elsewhere at this conference.

The paper presents a robust nonlinear skid-steering control design concept. The control concept is based on the recursive/backstepping control design technique and is capable of compensating for uncertainties associated with sensor noise measurements and/or system dynamic state uncertainties. The objective of this control design is to demonstrate the performance of the nonlinear controller under uncertainty associate with road traction (rough off-road and on-road terrain). The MULE vehicle is used in the simulation modeling and results.

Snowfields are challenging terrain for lightweight (<50 kg) unmanned ground vehicles. Deep sinkage, high snowcompaction resistance, traction loss while turning and ingestion of snow into the drive train can cause immobility within a few meters of travel. However, for suitably designed vehicles, deep snow offers a smooth, uniform surface that can obliterate obstacles. Key requirements for good over-snow mobility are low ground pressure, large clearance relative to vehicle size and a drive system that tolerates cohesive snow. A small robot will invariably encounter deep snow relative to its ground clearance. Because a single snowstorm can easily deposit 30 cm of fresh snow, robots with ground clearance less than about 10 cm must travel over the snow rather than gain support from the underlying ground. This can be accomplished using low-pressure tracks (< 1.5 kPa). Even still, snow-compaction resistance can exceed 20% of vehicle weight. Also, despite relatively high traction coefficients for low track pressures, differential or skid steering is difficult because the outboard track can easily break traction as the vehicle attempts to turn against the snow. Short track lengths (relative to track separation) or coupled articulated robots offer steering solutions for deep snow. This paper presents preliminary guidance to design lightweight robots for good mobility over snow based on mobility theory and tests of PackBot, Talon and SnoBot, a custom-designed research robot. Because many other considerations constrain robot designs, this guidance can help with development of winterization kits to improve the over-snow performance of existing robots.

Walking robots hold great potential for the future of military robotics. Their natural agility in rough, unstructured terrain make them ideal for military applications but their power requirements do not. Passive dynamic walkers offer a potentially low-power solution. This class of legged robots utilize the natural inverted pendular dynamics that humans rely on to locomote. The most basic of these systems uses gravity as its power source and has no control system therefore its stability is heavily reliant on its initial conditions. The VICON Motion Capture System was used to record the motions of Coleman and Ruina's¹ TinkertoyWalker© . The initial angles and angular velocities of the various trials were extracted from the motion capture data and used as inputs to a multi-body dynamics model of the walker. The model was created to provide insight into passive-dynamic walkers and the interactions between the walker and the ground surface. Several trials were performed to quantify the stability space of the experimental walker and improve the correlation of the dynamics model to the physical robot.

This paper describes the application of an Army-standard legacy off-road mobility model to cross-country route planning and negotiation by unmanned ground vehicles. A planned route is created from a movement map generated from existing terrain data. An unmanned ground vehicle negotiates the planned route and makes local routing adjustments based on a trafficability assessment of terrain features which are observed from the platform. This research leverages results from other work investigating the scalability of the existing legacy off-road mobility model to small vehicles (<500 kg). The legacy mobility model is the NATO Reference Mobility Model II (NRMM II), a standard for combat mobility modeling and procurement since the mid-90's.

The propulsion systems employed on unmanned ground vehicle platforms in Future Force Units of Action will likely involve electric or hybrid-electric drive. Power storage systems for these platforms will therefore be driven largely by expected power depletion rates. Resistances that propulsion systems must overcome during maneuvers will be a major factor affecting power depletion rates, and the resistance forces will vary drastically depending on the mission. Therefore, realistic mission-related considerations need to be applied when defining power storage requirements. The US Army has developed numerous models and simulations that use terra-mechanics algorithms to predict maneuver capability for ground vehicles as limited by terrain and environmental factors, and the algorithms employed for predicting maneuver capability in most of these models and simulations are founded on the terra-mechanics algorithms contained in the NATO Reference Mobility Model. The NATO Reference Mobility Model uses physics-based force balancing algorithms with terra-mechanics relationships that were empirically derived from decades of vehicle-terrain interaction research, and it also incorporates proven methodologies for assessing mission effectiveness in terms of maneuver capabilities. The terra-mechanics algorithms and methodologies for assessing mission effectiveness that are implemented in this model and in other related software tools, such as those used for route analysis, can be used to generate realistic mission-related resistance profiles for defining power storage requirements.

The USA Engineer Research and Development Center (ERDC) has conducted on-/off-road experimental field testing with full-sized and scale-model military vehicles for more than fifty years. Some 4000 acres of local terrain are available for tailored field evaluations or verification/validation of future robotic designs in a variety of climatic regimes. Field testing and data collection procedures, as well as techniques for quantifying terrain in engineering terms, have been developed and refined into algorithms and models for predicting vehicle-terrain interactions and resulting forces or speeds of military-sized vehicles. Based on recent experiments with Matilda, Talon, and Pacbot, these predictive capabilities appear to be relevant to most robotic systems currently in development. Utilization of current testing capabilities with sensor-based vehicle drivers, or use of the procedures for terrain quantification from sensor data, would immediately apply some fifty years of historical knowledge to the development, refinement, and implementation of future robotic systems. Additionally, translation of sensor-collected terrain data into engineering terms would allow assessment of robotic performance a priori deployment of the actual system and ensure maximum system performance in the theater of operation.

In planetary or hazardous environment exploration, there will be unforseen environmental circumstances which can not be planned. To overcome telerobotic control issues due to communication delays, autonomous robot control becomes necessary. Autonomously controlled landers and instrumentation can be used in exploration, such as lunar and martian missions. However, wheeled robots have difficulty in exploring uneven terrain; thus, legged robots can be used in such situations. This research develops intelligent and adaptive control of mobile robots to perform functions such as environmental exploration in coordination and obstacle avoidance. The coordinated control is demonstrated in simulations.

Global path planning (GPP) is the generation of an optimal trajectory to efficiently move from one position to specified target position with known environment. Most of GPP methodologies offer an optimal 2D-shortest path without considering vehicle parameters on the plain environments. However, it is motivated to consider 3D terrain and vehicle parameters to enhance traversability on the rough terrain. In this paper, we propose a novel approach of GPP method for unmanned ground vehicles (UGVs) by applying distance transform (3D to 2D) based on the slope of terrain. In addition, the generated path is modified by smoothing process based on the local path planning method which considers vehicle stability on the specified candidate curve and speed. The proposed methodology is tested by simulations and shows enhanced performance.

Development of software for autonomous ground vehicles has been a mission critical issue for army. For the last two decades, different definitions of software complexity have been proposed. However, no definition has been found to be highly satisfactory. So in this report, a new definition of software complexity based on the rank of a matrix of a finite sequential machine has been proposed. Software has been developed to determine the complexity, so that it could be used in the early stages of software development from the data flow architecture to save cost and development efforts. We have shown the examples of data flow architecture and then complexity calculation based on our software. It is hoped that the results in this paper will help software development to address reliability and complexity issues of autonomous ground vehicle in a better way to assist research in defense and security for such missions.

This paper describes the latest efforts to develop an Automated UAV Mission System (AUMS) for small vertical takeoff and landing (VTOL) unmanned air vehicles (UAVs). In certain applications such as force protection, perimeter security, and urban surveillance a VTOL UAV can provide far greater utility than fixed-wing UAVs or ground-based sensors. The VTOL UAV can operate much closer to an object of interest and can provide a hover-and-stare capability to keep its sensors trained on an object, while the fixed wing UAV would be forced into a higher altitude loitering pattern where its sensors would be subject to intermittent blockage by obstacles and terrain. The most significant disadvantage of a VTOL UAV when compared to a fixed-wing UAV is its reduced flight endurance. AUMS addresses this disadvantage by providing forward staging, refueling, and recovery capabilities for the VTOL UAV through a host unmanned ground vehicle (UGV), which serves as a launch/recovery platform and service station. The UGV has sufficient payload capacity to carry UAV fuel for multiple launch, recovery, and refuel iterations. The UGV also provides a highly mobile means of forward deploying a small UAV into hazardous areas unsafe for personnel, such as chemically or biologically contaminated areas. Teaming small UAVs with large UGVs can decrease risk to personnel and expand mission capabilities and effectiveness. There are numerous technical challenges being addressed by these development efforts. Among the challenges is the development and integration of a precision landing system compact and light enough to allow it to be mounted on a small VTOL UAV while providing repeatable landing accuracy to safely land on the AUMS. Another challenge is the design of a UGV-transportable, expandable, self-centering landing pad that contains hardware and safety devices for automatically refueling the UAV. A third challenge is making the design flexible enough to accommodate different types of VTOL UAVs, such as the AAI iSTAR ducted-fan vehicle and small helicopter UAVs. Finally, a common command-and-control architecture which supports the UAV, UGV, and AUMS must be developed and interfaced with these systems to allow fully autonomous collaborative behaviors. Funded by the Joint Robotics Program, AUMS is part of a joint effort with the Air Force Research Laboratory and the Army Missile Research Development and Engineering Command. The objective is to develop and demonstrate UGVUAV teaming concepts and work with the warfighter to ensure that future upgrades are focused on operational requirements. This paper describes the latest achievements in AUMS development and some of the military program and first responder situations that could benefit from this system.

The 2005 DARPA Grand Challenge (DCG) was a 'Huge Leap Forward for Robotics R&D' according to the DARPA Grand Challenge tracking website. Similar to the transatlantic flight competition that spurred commercial flights all over the world, the Grand Challenge was a step forward in the area of navigation for unmanned ground vehicles. However, questions like 'What are the important technologies brought forth by the Grand Challenge?' and 'How can these technologies assist our soldiers in the field?' need to be addressed. This paper will look at the 2005 DARPA Grand Challenge from the perspective of individuals involved in some of the Army's unmanned ground vehicle programs. Information will be presented contrasting this year's competition to the one held in 2004. Details of the enabling technologies from many of the competitors will be discussed along with problems they encountered at the National Qualification Event (NQE) and on Race Day. Finally, thoughts will be presented on how these technologies may be harvested in commercial and DOD research and development for current and future systems.

The Family of Integrated Rapid Response Equipment (FIRRE) is an advanced technology demonstration program intended to develop a family of affordable, scalable, modular, and logistically supportable unmanned systems to meet urgent operational force protection needs and requirements worldwide. The near-term goal is to provide the best available unmanned ground systems to the warfighter in Iraq and Afghanistan. The overarching long-term goal is to develop a fully-integrated, layered force protection system of systems for our forward deployed forces that is networked with the future force C4ISR systems architecture. The intent of the FIRRE program is to reduce manpower requirements, enhance force protection capabilities, and reduce casualties through the use of unmanned systems. FIRRE is sponsored by the Office of the Under Secretary of Defense, Acquisitions, Technology and Logistics (OUSD AT&L), and is managed by the Product Manager, Force Protection Systems (PM-FPS). The Remote Sensor Station (RSS) provides FIRRE with the ability to remote (or extend the range of) manned/unmanned sensors. The RSS consists of three primary components: (1) an actively cooled and hermetically sealed (NEMA-4X) electronics enclosure, (2) a 22' telescoping tower, (3) and the PM-MEP 531A 2KW GENSET. The current configuration supports a Digital Imaging Infrared (DII) DI-5000 thermal imaging system/visual imaging system (TIS/VIS), a Syracuse Research Corporation (SRC) PPS-5D ground surveillance radar (GSR), an AN/PRS-9 (BAIS) unmanned ground sensor (UGS) receiver, an Intuicom Military Navigator II (MILNAVII) data link radio, and a DTC Communications Palladium 12000 audio/video (A/V) radio. The electronics box is insulated with a radiant barrier and fitted with a EIC Solutions 1500 BTU solid state thermoelectric cooler (TEC) capable of maintaining a safe operating temperature in extreme conditions (<120° Fahrenheit).

We describe an algorithm that generates a smooth trajectory (position, velocity, and acceleration at uniformly sampled instants of time) for a car-like vehicle autonomously navigating within the constraints of lanes in a road. The technique models both vehicle paths and lane segments as straight line segments and circular arcs for mathematical simplicity and elegance, which we contrast with cubic spline approaches. We develop the path in an idealized space, warp the path into real space and compute path length, generate a one-dimensional trajectory along the path length that achieves target speeds and positions, and finally, warp, translate, and rotate the one-dimensional trajectory points onto the path in real space. The algorithm moves a vehicle in lane safely and efficiently within speed and acceleration maximums. The algorithm functions in the context of other autonomous driving functions within a carefully designed vehicle control hierarchy.

This paper aims at surveying the options of powering an Unmanned Surface Vehicle (USV) with renewable sources that scavenge energy from the environment. Some of the technologies that are discussed include solar power, wind power and energy from waves. Some conclusions and recommendations regarding the state-of-the-art for developing an energy scavenging unmanned surface vehicle are tabulated.

Emergency stop systems are an integral and lifesaving component of large unmanned vehicles. Some E-stop designs may require their own separate data radio link, and passive listening designs can fail due to false carrier signals, or be delayed by buffering of data if no protocol handshake is required. This paper describes an active emergency stop architecture with data handshake that can share a radio data link with primary command and control communications such as using JAUS. Given a data link where packet delivery latency is well below E-stop timeout time, the OCU and vehicle can exchange E-stop keepalive messages actively, with sequence numbers to guard against the possibility of old data deceiving the vehicle and keeping the E-stop from triggering. Since the vehicle and OCU are addressing each other and not merely looking for a carrier signal, E-stop communications can coexist with other data traffic so long as packet delivery time is well below E-stop timeout time. An example implementation is over a computer network link supporting TCP/IP, such as using common off-the-shelf 802.11 equipment, or similar radios that might achieve longer range with somewhat lower data rate. With 802.11, round-trip delivery times are generally below 10 milliseconds, providing margin for many retransmissions within a typical 500 millisecond E-stop timeout time. Another benefit of this active E-stop design is immediate triggering of a stop using an E-stop button. Rather than waiting for an E-stop timeout time to expire, an explicit message triggering a stop can be sent from the OCU-side E-stop button device to the vehicle E-stop circuitry (which can still be independent from the VCU). This will trigger a stop within the packet network delivery time, just 10 milliseconds in our example.

This paper describes a desirable set of features for small mobile robotic vehicles-features that are desirable both in terms of usefulness and versatility. A generic robotic architecture with these desirable features is discussed. The paper concludes by presenting SNEAKY, a commercial product available from M-Bots, that possesses most of the features included in the architecture.

BigDog's goal is to be the world's most advanced quadruped robot for outdoor applications. BigDog is aimed at the mission of a mechanical mule - a category with few competitors to date: power autonomous quadrupeds capable of carrying significant payloads, operating outdoors, with static and dynamic mobility, and fully integrated sensing. BigDog is about 1 m tall, 1 m long and 0.3 m wide, and weighs about 90 kg. BigDog has demonstrated walking and trotting gaits, as well as standing up and sitting down. Since its creation in the fall of 2004, BigDog has logged tens of hours of walking, climbing and running time. It has walked up and down 25 & 35 degree inclines and trotted at speeds up to 1.8 m/s. BigDog has walked at 0.7 m/s over loose rock beds and carried over 50 kg of payload. We are currently working to expand BigDog's rough terrain mobility through the creation of robust locomotion strategies and terrain sensing capabilities.