An integrated health and contingency management case study on an autonomous ground robot
DOI: 10.1109/ICCA.2011.6137995 Conference: 9th IEEE International Conference on Control and Automation, ICCA 2011, Santiago, Chile, December 19-21, 2011
Autonomous robotic vehicles are playing an increasingly important role in support of a wide variety of present and future critical missions. Due to the absence of timely operator/pilot interaction and potential catastrophic consequence of unattended faults and failures, a real-time, onboard health and contingency management system is desired. This system would be capable of detecting and isolating faults, predicting fault progression and automatically reconfiguring the system to accommodate faults. This paper presents the implementation of an integrated health and contingency management system on an autonomous ground robot. This case study is conducted to demonstrate the feasibility and benefit of using real-time prognostics and health management (PHM) information in robot control and mission reconfiguration. Several key software modules including a HyDE-based diagnosis reasoner, particle filtering-based prognosis server and a prognostics-enhanced mission planner are presented in this paper with illustrative experimental results.
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ABSTRACT: This paper proposes a recursive receding horizon path planning algorithm for unmanned vehicles in nonuniform environments. In the proposed algorithm, the map is described by grids in which nodes are defined on corners of grids. The planning algorithm considers the map as four areas, namely, implementation, observation, explored, and unknown. The Implementation area is a subset of the Observation area, whereas the Explored area is the union of all the previous Observation areas. The path is planned with a receding horizon planning strategy to generate waypoints and in-between map updates. When a new map update occurs, the path is replanned within the current Observation area if necessary. If no such path exists, the search is extended to the Explored area. Paths can be planned by recursively searching available nodes inside the Explored area that can be connected to available nodes on the boundary of the Explored area. A robot platform is employed to conduct a series of experiments in a laboratory environment to verify the proposed path planning algorithm.IEEE Transactions on Industrial Electronics 05/2015; 62(5):2912-2920. DOI:10.1109/TIE.2014.2363632 · 6.50 Impact Factor
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