Anytime Motion Planning using the RRT*
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Teller_Anytime motion.pdf
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Author(s)
Karaman, Sertac
Walter, Matthew R.
Perez, Alejandro
Frazzoli, Emilio
Teller, Seth
Date Issued
May 2011
Journal
IEEE International Conference on Robotics and Automation. ICRA 2011
Publisher
Institute of Electrical and Electronics Engineers
Citation
Karaman, Sertac et al. "Anytime Motion Planning using the RRT*." 2011 IEEE International Conference on Robotics and Automation (ICRA) May 9-13, 2011, Shanghai International Conference Center, Shanghai, China.
Version
Author's final manuscript
Abstract
The Rapidly-exploring Random Tree (RRT) algorithm,
based on incremental sampling, efficiently computes
motion plans. Although the RRT algorithm quickly produces
candidate feasible solutions, it tends to converge to a solution
that is far from optimal. Practical applications favor “anytime”
algorithms that quickly identify an initial feasible plan, then,
given more computation time available during plan execution,
improve the plan toward an optimal solution. This paper
describes an anytime algorithm based on the RRT* which (like
the RRT) finds an initial feasible solution quickly, but (unlike
the RRT) almost surely converges to an optimal solution. We
present two key extensions to the RRT*, committed trajectories
and branch-and-bound tree adaptation, that together enable
the algorithm to make more efficient use of computation
time online, resulting in an anytime algorithm for real-time
implementation. We evaluate the method using a series of
Monte Carlo runs in a high-fidelity simulation environment,
and compare the operation of the RRT and RRT* methods. We
also demonstrate experimental results for an outdoor wheeled
robotic vehicle.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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DOI of Published Version
https://ras.papercept.net/conferences/scripts/abstract.pl?ConfID=34&Number=1887
