Mr. Mark Moseley Profile

Mark Moseley

at iRobot Corp

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Proceedings Article | 4 February 2013 Paper

LABRADOR: a learning autonomous behavior-based robot for adaptive detection and object retrieval

Brian Yamauchi, Mark Moseley, Jonathan Brookshire

Proceedings Volume 8662, 86620P (2013) https://doi.org/10.1117/12.2011834

KEYWORDS: Sensors, Navigation systems, Cameras, Global Positioning System, Detection and tracking algorithms, Bone, Robotic systems, Machine vision, 3D modeling, RGB color model

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As part of the TARDEC-funded CANINE (Cooperative Autonomous Navigation in a Networked Environment) Program, iRobot developed LABRADOR (Learning Autonomous Behavior-based Robot for Adaptive Detection and Object Retrieval). LABRADOR was based on the rugged, man-portable, iRobot PackBot unmanned ground vehicle (UGV) equipped with an explosives ordnance disposal (EOD) manipulator arm and a custom gripper. For LABRADOR, we developed a vision-based object learning and recognition system that combined a TLD (track-learn-detect) filter based on object shape features with a color-histogram-based object detector. Our vision system was able to learn in real-time to recognize objects presented to the robot. We also implemented a waypoint navigation system based on fused GPS, IMU (inertial measurement unit), and odometry data. We used this navigation capability to implement autonomous behaviors capable of searching a specified area using a variety of robust coverage strategies – including outward spiral, random bounce, random waypoint, and perimeter following behaviors. While the full system was not integrated in time to compete in the CANINE competition event, we developed useful perception, navigation, and behavior capabilities that may be applied to future autonomous robot systems.

Proceedings Article | 29 April 2009 Paper

Integrated long-range UAV/UGV collaborative target tracking

Mark Moseley, Benjamin Grocholsky, Carol Cheung, Sanjiv Singh

Proceedings Volume 7332, 733204 (2009) https://doi.org/10.1117/12.820289

KEYWORDS: Unmanned aerial vehicles, Video, Optical tracking, Telecommunications, Robots, Cameras, Sensors, Target detection, Data communications, Detection and tracking algorithms

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Coordinated operations between unmanned air and ground assets allow leveraging of multi-domain sensing and increase opportunities for improving line of sight communications. While numerous military missions would benefit from coordinated UAV-UGV operations, foundational capabilities that integrate stove-piped tactical systems and share available sensor data are required and not yet available. iRobot, AeroVironment, and Carnegie Mellon University are working together, partially SBIR-funded through ARDEC's small unit network lethality initiative, to develop collaborative capabilities for surveillance, targeting, and improved communications based on PackBot UGV and Raven UAV platforms. We integrate newly available technologies into computational, vision, and communications payloads and develop sensing algorithms to support vision-based target tracking. We first simulated and then applied onto real tactical platforms an implementation of Decentralized Data Fusion, a novel technique for fusing track estimates from PackBot and Raven platforms for a moving target in an open environment. In addition, system integration with AeroVironment's Digital Data Link onto both air and ground platforms has extended our capabilities in communications range to operate the PackBot as well as in increased video and data throughput. The system is brought together through a unified Operator Control Unit (OCU) for the PackBot and Raven that provides simultaneous waypoint navigation and traditional teleoperation. We also present several recent capability accomplishments toward PackBot-Raven coordinated operations, including single OCU display design and operation, early target track results, and Digital Data Link integration efforts, as well as our near-term capability goals.

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