ICRA 2012 Paper Abstract


Paper WeC04.5

Shah, Shridhar (University of Delaware), Pahlajani, Chetan (University of Delaware), Lacock, Nicholaus (University of Delaware), Tanner, Herbert G. (University of Delaware)

Stochastic Receding Horizon Control for Robots with Probabilistic State Constraints

Scheduled for presentation during the Regular Session "Stochastic Motion Planning" (WeC04), Wednesday, May 16, 2012, 15:30−15:45, Meeting Room 4 (Chief Wabasha)

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 20, 2018

Keywords Collision Avoidance, Motion and Path Planning


This paper deals with the problem of receding horizon control of a robot subject to stochastic uncertainty within a constrained environment. We deviate from the conventional approach that minimizes expectation of a cost functional while ensuring satisfaction of probabilistic state constraints. Instead, we reduce the problem into a particular form of stochastic optimal control where the path that minimizes the cost functional is planned deterministically and a local stochastic optimal controller with exit constraints ensures satisfaction of probabilistic state constraints while following the planned path. This control design strategy ensures boundedness of errors around the reference path and collision-free convergence to the goal with probability one under the assumption of unbounded inputs. We show that explicit expressions for the control law are possible for certain cases. We provide simulation results for a point robot moving in a constrained two-dimensional environment under Brownian noise. The method can be extended to systems with bounded inputs, if a small nonzero probability of failure can be accepted.



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