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Paper WeD01.6

Patil, Sachin (University of North Carolina at Chapel Hill), van den Berg, Jur (University of Utah), Alterovitz, Ron (University of North Carolina at Chapel Hill)

Estimating Probability of Collision for Safe Motion Planning under Gaussian Motion and Sensing Uncertainty

Scheduled for presentation during the Regular Session "Non-Holonomic Motion Planning" (WeD01), Wednesday, May 16, 2012, 17:45−18:00, Meeting Room 1 (Mini-sota)

2012 IEEE International Conference on Robotics and Automation, May 14-18, 2012, RiverCentre, Saint Paul, Minnesota, USA

This information is tentative and subject to change. Compiled on June 19, 2018

Keywords Motion and Path Planning, Nonholonomic Motion Planning, Collision Avoidance

Abstract

We present a fast, analytical method for estimating the probability of collision of a motion plan for a mobile robot operating under the assumptions of Gaussian motion and sensing uncertainty. Estimating the probability of collision is an integral step in many algorithms for motion planning under uncertainty and is crucial for characterizing the safety of motion plans. Our method is computationally fast, enabling its use in online motion planning, and provides conservative estimates to promote safety. To improve accuracy, we use a novel method to truncate estimated a priori state distributions to account for the fact that the probability of collision at each stage along a plan is conditioned on the previous stages being collision free. Our method can be directly applied within a variety of existing motion planners to improve their performance and the quality of computed plans. We apply our method to a car-like mobile robot with second order dynamics and to a steerable medical needle in 3D and demonstrate that our method for estimating the probability of collision is orders of magnitude faster than naive Monte Carlo sampling methods and reduces estimation error by more than 25% compared to prior methods.

 

 

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