Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns
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How_Probabilistically safe.pdf
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Author(s)
Aoude, Georges
Roy, Nicholas
How, Jonathan P.
Luders, Brandon Douglas
Joseph, Joshua Mason
Date Issued
May 2013
Journal
Autonomous Robots
Publisher
Springer-Verlag
Citation
Aoude, Georges S., Brandon D. Luders, Joshua M. Joseph, Nicholas Roy, and Jonathan P. How. “Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns.” Autonomous Robots 35, no. 1 (July 3, 2013): 51-76.
Version
Author's final manuscript
Abstract
This paper presents a real-time path planning algorithm that guarantees probabilistic feasibility for autonomous robots with uncertain dynamics operating amidst one or more dynamic obstacles with uncertain motion patterns. Planning safe trajectories under such conditions requires both accurate prediction and proper integration of future obstacle behavior within the planner. Given that available observation data is limited, the motion model must provide generalizable predictions that satisfy dynamic and environmental constraints, a limitation of existing approaches. This work presents a novel solution, named RR-GP, which builds a learned motion pattern model by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation. Obstacle trajectory GP predictions are conditioned on dynamically feasible paths identified from the reachability analysis, yielding more accurate predictions of future behavior. RR-GP predictions are integrated with a robust path planner, using chance-constrained RRT, to identify probabilistically feasible paths. Theoretical guarantees of probabilistic feasibility are shown for linear systems under Gaussian uncertainty; approximations for nonlinear dynamics and/or non-Gaussian uncertainty are also presented. Simulations demonstrate that, with this planner, an autonomous vehicle can safely navigate a complex environment in real-time while significantly reducing the risk of collisions with dynamic obstacles.
MIT Department
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1007/s10514-013-9334-3
