ScholarlyCommons

Title

A drift-diffusion model for robotic obstacle avoidance

Author(s)

Paul B. Reverdy, University of PennsylvaniaFollow
B. Deniz Ilhan, University of Pennsylvania
Daniel E. Koditschek, University of PennsylvaniaFollow

Image

Fig. 1. Typical geometry of robot interaction with a concave obstacle.

Abstract

We develop a stochastic framework for modeling and analysis of robot navigation in the presence of obstacles. We show that, with appropriate assumptions, the probability of a robot avoiding a given obstacle can be reduced to a function of a single dimensionless parameter which captures all relevant quantities of the problem. This parameter is analogous to the Peclet number considered in the literature on mass transport in advection-diffusion fluid flows. Using the framework we also compute statistics of the time required to escape an obstacle in an informative case. The results of the computation show that adding noise to the navigation strategy can improve performance. Finally, we present experimental results that illustrate these performance improvements on a robotic platform.

For more information: Kod*Lab

Document Type

Conference Paper

Subject Area

CPS Embedded Control, GRASP, Kodlab

Date of this Version

8-2015

Publication Source

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems

Comments

To appear.