NDLI: Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns

Content Provider	Springer Nature Link
Author	Aoude, Georges S. Luders, Brandon D. Joseph, Joshua M. Roy, Nicholas How, Jonathan P.
Copyright Year	2013
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.
Starting Page	51
Ending Page	76
Page Count	26
File Format	PDF
ISSN	09295593
Journal	Autonomous Robots
Volume Number	35
Issue Number	1
e-ISSN	15737527
Language	English
Publisher	Springer US
Publisher Date	2013-05-03
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Planning under uncertainty Trajectory prediction Gaussian processes Robotics and Automation Artificial Intelligence (incl. Robotics) Computer Imaging, Vision, Pattern Recognition and Graphics Control, Robotics, Mechatronics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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