Small, agile rovers for harsh outdoor environments offer good potential to support rescue teams in emergency operations.
For such exploration purposes the outdoor MERLIN rovers have been developed in tracked and wheeled versions for the
weight class between 10 and 20 kg. Those vehicles can achieve velocities up to 50 km/h. Therefore the drive assistance
system has to provide the functionalities to perform safe and efficient tele-operations in combination with autonomous
reaction capabilities. The tele-operator can select appropriate levels of autonomy, ranging from warning signals to
autonomous reactions of the vehicle's on-board data processing system, if an endangering situation is not anticipated.
Implemented features include detection of obstacles in the path, as well as an adaptation of speed appropriate to terrain
roughness and slope, but also to path curvature. An autonomous return to the initial position is to be realized, when the
telecommunication contact to the tele-operator has been lost. This paper addresses the implemented sensor and data
processing techniques to handle those tasks in a robust way. Results from extensive tests in various environments will be
reported. In particular the results from the C-ELROB 2007 competition, the European Land Robotics trial, will be
reported, where the Outdoor MERLIN was the winner of the urban terrain challenge.
Modern miniaturization technologies allow realization of satellites at very low masses. For the example of the pico-satellite
UWE-1 (University Würzburg's Experimental satellite), design details for such spacecrafts with at a mass below
1 kg will be discussed. UWE-1 was launched in 2005 in order to optimize Internet Protocol parameters in adaptation to
the measured space environment, specifically at significant delays and at higher noise levels compared to terrestrial links.
Such miniaturization often limits the power and data transmission resources, and thus the achievable performance.
Therefore concepts for formations of small satellites have been developed, where multiple pico-satellites complement
each other in swarms to realize a fault-tolerant, robust system. Here observations take advantage of large baselines
between the sensors placed on the different satellites. Thus, due to the different viewing angles, even three-dimensional
structures can be indentified. The limited implementation costs for pico-satellites encourage strategies to store such pico-satellites
for situations, when reconnaissance data are required quickly, as in case of disasters, like earthquakes,
floodings, and forest fires.
KEYWORDS: Sensors, Mobile robots, Navigation systems, Distance measurement, Ranging, Kinematics, Process control, Robotic systems, Control systems, Data fusion
Cooperation between several mobile robots enables to address more complex tasks or to provide more robust performance. Navigation and localisation forms the basis for the coordination and autonomous behaviours of teams of mobile robots. Therefore analyses of the kinematics and of the sensor field of view for the different vehicles are summarized in order to characterize robust formations during movements. In this approach optimized robot formations with respect to the given sensor configurations are maintained by the control system during the joint motion towards the target. The application of this method for guidance by a navigator and for cooperative manipulation tasks is discussed and tested with mobile robot hardware.
From cooperation of multiple mobile robots equipped with different sensor systems for navigation tasks, more accurate localisation estimates can be expected. This paper presents strategies to control within such robot teams relative positions between the vehicles in order to derive by an ultrasonic ranging system also information on locations of robots with limited navigation sensor systems. These algorithms have been tested in experiments with the MERLIN rover hardware.
KEYWORDS: Sensors, Control systems, Ultrasonics, Magnetic sensors, Navigation systems, Global Positioning System, Distance measurement, Mars, Robots, Internet
In the context of the European Mars Rover development, prototype vehicles for wheeled, tracked and tumbling locomotion have been implemented and tested. As a spin-off the versatile tracked and wheeled MERLIN (Mobile Experimental Rover for Locomotion and Intelligent Navigation) rovers for outdoor applications resulted. In this paper the navigation sensors and the control system are addressed, supporting the locomotion devices to deal with rough terrain, including autonomous obstacle detection and avoidance strategies. In addition an advanced telematics infrastructure for remote sensor data acquisition and tele-operations has been realized for these vehicles.
For planetary surface operations, the European Space Agency initiated a development for teleoperated mini-rovers. Remote control functions related to autonomous reaction capabilities and sensor data processing on-board the vehicle exhibit interesting transfer potential to industrial and educational teleoperation tasks. Similar requirements to the space application arise in particular, when low cost communication links are used for teleservicing. This paper reviews the operational concept for the Mars rover and its operations test environment. The technology transfer to terrestrial teleservicing applications is analyzed, regarding remotely controlled equipment or robots. This is illustrated at the example of pipe inspection robots, industrial transport robots and virtual laboratories for educational purposes.
This paper shows several working steps for updating the `Digitale Landschaftsmodell 200 (DLM 200)' using satellite images. It is based on a two-step approach: verification and classification. First the existing semantic model (DLM 200) is used for the knowledge based object oriented analysis of the satellite images. At the second stage the information gained from the first step serves to prove and update the DLM 200. Since the DLM 200 is produced by digitizing the map layers of the `Topographische Ubersichtskarte (TUK 200),' typical cartographic aspects have to be considered. Some examples illustrating these effects on a representative class of the DLM 200 are shown. After the determination of these geometric relations between the DLM 200 and the images the `knowledge,' based on the DLM 200, backs up the object based analysis of the satellite images. Image areas which do not fit the DLM 200 are examined at the second stage. The classification has to assign the changes detected in the course of the verification to appropriate classes of the DLM 200. This process uses the parameters of the image analysis as additional information.
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