Stochastic Artificial Potentials for Online Safe Navigation

TL;DR

This paper introduces a stochastic approximation method for safe navigation around convex obstacles, ensuring convergence to a goal with obstacle avoidance guarantees under certain geometric conditions.

Contribution

It develops a stochastic gradient approach for Rimon-Koditschek navigation functions, extending deterministic guarantees to stochastic and biased estimates.

Findings

Unbiased estimates lead to convergence and obstacle avoidance.

Obstacle avoidance is guaranteed with probability one under specific geometric conditions.

Numerical examples demonstrate practical effectiveness of the theoretical results.

Abstract

Consider a convex set of which we remove an arbitrarily number of disjoints convex sets -- the obstacles -- and a convex function whose minimum is the agent's goal. We consider a local and stochastic approximation of the gradient of a Rimon-Koditschek navigation function where the attractive potential is the convex function that the agent is minimizing. In particular we show that if the estimate available to the agent is unbiased convergence to the desired destination while obstacle avoidance is guaranteed with probability one under the same geometrical conditions than in the deterministic case. Qualitatively these conditions are that the ratio of the maximum over the minimum eigenvalue of the Hessian of the objective function is not too large and that the obstacles are not too flat or too close to the desired destination. Moreover, we show that for biased estimates a similar result holds under some assumptions on the bias. These assumptions are motivated by the study of the estimate of the gradient of a Rimon-Koditschek navigation function for sensor models that fit circles or ellipses around the obstacles. Numerical examples explore the practical value of these theoretical results.