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Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns

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dc.contributor.author Luders, Brandon D.
dc.contributor.author Aoude, Georges S.
dc.contributor.author Joseph, Joshua M.
dc.contributor.author Roy, Nicholas
dc.contributor.author How, Jonathan P.
dc.date.accessioned 2011-07-04T21:36:27Z
dc.date.available 2011-07-04T21:36:27Z
dc.date.issued 2011-07-04
dc.identifier.uri http://hdl.handle.net/1721.1/64738
dc.description.abstract This paper presents a real-time path planning algorithm which can guarantee probabilistic feasibility for autonomous robots subject to process noise and an uncertain environment, including dynamic obstacles with uncertain motion patterns. The key contribution of the work is the integration of a novel method for modeling dynamic obstacles with uncertain future trajectories. The method, denoted as RR-GP, uses a learned motion pattern model of the dynamic obstacles to make long-term predictions of their future paths. This is done by combining the flexibility of Gaussian processes (GP) with the efficiency of RRT-Reach, a sampling-based reachability computation method which ensures dynamic feasibility. This prediction model is then utilized within chance-constrained rapidly-exploring random trees (CC-RRT), which uses chance constraints to explicitly achieve probabilistic constraint satisfaction while maintaining the computational benefits of sampling-based algorithms. With RR-GP embedded in the CC-RRT framework, theoretical guarantees can be demonstrated for linear systems subject to Gaussian uncertainty, though the extension to nonlinear systems is also considered. Simulation results show that the resulting approach can be used in real-time to efficiently and accurately execute safe paths. en_US
dc.relation.ispartofseries ;ACL11-02
dc.rights Attribution-NonCommercial-NoDerivs 3.0 United States en
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/us/ en
dc.subject probabilistic path planning en_US
dc.subject intention prediction en_US
dc.subject gaussian processes en_US
dc.subject uncertainty in predictability en_US
dc.subject collision avoidance en_US
dc.subject dynamic obstacles en_US
dc.subject probabilistic constraint satisfaction en_US
dc.subject sampling-based reachability en_US
dc.title Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns en_US
dc.type Technical Report en_US


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