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Published August 2011 | public
Journal Article

Probabilistic Collision Checking With Chance Constraints

Abstract

Obstacle avoidance, and by extension collision checking, is a basic requirement for robot autonomy. Most classical approaches to collision-checking ignore the uncertainties associated with the robot and obstacle's geometry and position. It is natural to use a probabilistic description. of the uncertainties. However, constraint satisfaction cannot be guaranteed, in this case, and collision constraints must instead be converted to chance constraints. Standard results for linear probabilistic constraint evaluation have been applied to probabilistic collision evaluation; however, this approach ignores the uncertainty associated with the sensed obstacle. An alternative formulation of probabilistic collision checking that accounts for robot and obstacle uncertainty is presented which allows for dependent object distributions (e.g., interactive robot-obstacle models). In order to efficiently enforce the resulting collision chance constraints, an approximation is proposed and the validity of this approximation is evaluated. The results presented here have been applied to robot-motion planning in dynamic, uncertain environments.

Additional Information

© 2011 IEEE. Manuscript received June 17, 2010; revised November 19, 2010; accepted February 9, 2011. Date of publication March 24, 2011; date of current version August 10, 2011. This paper was recommended for publication by Associate Editor S. Carpin and Editor L. Parker upon evaluation of the reviewers' comments.

Additional details

Created:
August 22, 2023
Modified:
October 24, 2023