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|a Luders, Brandon Douglas
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|a Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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|a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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|a How, Jonathan P.
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|a Luders, Brandon Douglas
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|a Karaman, Sertac
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|a Frazzoli, Emilio
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|a How, Jonathan P.
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|a Karaman, Sertac
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|a Frazzoli, Emilio
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|a How, Jonathan P.
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|a Bounds on tracking error using closed-loop rapidly-exploring random trees
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|b Institute of Electrical and Electronics Engineers,
|c 2010-10-06T14:42:45Z.
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|z Get fulltext
|u http://hdl.handle.net/1721.1/58893
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|a This paper considers the real-time motion planning problem for autonomous systems subject to complex dynamics, constraints, and uncertainty. Rapidly-exploring random trees (RRT) can be used to efficiently construct trees of dynamically feasible trajectories; however, to ensure feasibility, it is critical that the system actually track its predicted trajectory. This paper shows that under certain assumptions, the recently proposed closed-loop RRT (CL-RRT) algorithm can be used to accurately track a trajectory with known error bounds and robust feasibility guarantees, without the need for replanning. Unlike open-loop approaches, bounds can be designed on the maximum prediction error for a known uncertainty distribution. Using the property that a stabilized linear system subject to bounded process noise has BIBO-stable error dynamics, this paper shows how to modify the problem constraints to ensure long-term feasibility under uncertainty. Simulation results are provided to demonstrate the effectiveness of the closed-loop RRT approach compared to open-loop alternatives.
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|a United States. Air Force Office of Scientific Research (grant FA9550-08-1-0086)
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|a en_US
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|a Article
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|t Proceedings of the American Control Conference, 2010
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