Robotics: Science and Systems XIII

Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning

Edward Schmerling, Marco Pavone

Abstract:

This paper presents a tool for addressing a key component in many algorithms for planning robot trajectories under uncertainty: evaluation of the safety of a robot whose actions are governed by a closed-loop feedback policy near a nominal planned trajectory. We describe an adaptive importance sampling Monte Carlo framework that enables the evaluation of a given control policy for satisfaction of a probabilistic collision avoidance constraint which also provides an associated certificate of accuracy (in the form of a confidence interval). In particular this adaptive technique is well-suited to addressing the complexities of rigid-body collision checking applied to non-linear robot dynamics. As a Monte Carlo method it is amenable to parallelization for computational tractability, and is generally applicable to a wide gamut of simulatable systems, including alternative noise models. Numerical experiments demonstrating the effectiveness of the adaptive importance sampling procedure are presented and discussed.

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Bibtex:

  
@INPROCEEDINGS{Schmerling-RSS-17, 
    AUTHOR    = {Edward Schmerling AND Marco Pavone}, 
    TITLE     = {Evaluating Trajectory Collision Probability through Adaptive Importance Sampling for Safe Motion Planning}, 
    BOOKTITLE = {Proceedings of Robotics: Science and Systems}, 
    YEAR      = {2017}, 
    ADDRESS   = {Cambridge, Massachusetts}, 
    MONTH     = {July}, 
    DOI       = {10.15607/RSS.2017.XIII.068} 
}