Chance-Constrained Optimal Path Planning With Obstacles
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Williams_Chance-constrained.pdf
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570.64 KB
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Author(s)
Blackmore, Lars
Ono, Masahiro
Williams, Brian Charles
Date Issued
December 2011
Journal
IEEE Transactions on Robotics
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Blackmore, Lars, Masahiro Ono, and Brian C. Williams. “Chance-Constrained Optimal Path Planning With Obstacles.” IEEE Transactions on Robotics 27, no. 6 (December 2011): 1080-1094.
Version
Author's final manuscript
Abstract
Autonomous vehicles need to plan trajectories to a specified goal that avoid obstacles. For robust execution, we must take into account uncertainty, which arises due to uncertain localization, modeling errors, and disturbances. Prior work handled the case of set-bounded uncertainty. We present here a chance-constrained approach, which uses instead a probabilistic representation of uncertainty. The new approach plans the future probabilistic distribution of the vehicle state so that the probability of failure is below a specified threshold. Failure occurs when the vehicle collides with an obstacle or leaves an operator-specified region. The key idea behind the approach is to use bounds on the probability of collision to show that, for linear-Gaussian systems, we can approximate the nonconvex chance-constrained optimization problem as a disjunctive convex program. This can be solved to global optimality using branch-and-bound techniques. In order to improve computation time, we introduce a customized solution method that returns almost-optimal solutions along with a hard bound on the level of suboptimality. We present an empirical validation with an aircraft obstacle avoidance example.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1109/tro.2011.2161160
