Bounds on tracking error using closed-loop rapidly-exploring random trees
Author(s)Luders, Brandon Douglas; Karaman, Sertac; Frazzoli, Emilio; How, Jonathan P.
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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.
DepartmentMassachusetts Institute of Technology. Department of Aeronautics and Astronautics; Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Proceedings of the American Control Conference, 2010
Institute of Electrical and Electronics Engineers
Luders, B.D. et al. “Bounds on tracking error using closed-loop rapidly-exploring random trees.” American Control Conference (ACC), 2010. 2010. 5406-5412. ©2010 IEEE.
Final published version
INSPEC Accession Number: 11509326