Adversarial Fence Patrolling: Non-Uniform Policies for Asymmetric Environments

Abstract

Robot teams are very useful in patrol tasks, where the robots are required to repeatedly visit a target area in order to detect an adversary. In this work we examine the Fence Patrol problem, in which the robots must travel back and forth along an open polyline and the adversary is aware of the robots' patrol strategy. Previous work has suggested non-deterministic patrol schemes, characterized by a uniform policy along the entire area, guaranteeing that the minimal probability of penetration detection throughout the area is maximized. We present a patrol strategy with a non-uniform policy along different points of the fence, based on the location and other properties of the point. We explore this strategy in different kinds of tracks and show that the minimal probability of penetration detection achieved by this non-uniform (variant) policy is higher than former policies. We further consider applying this model in multi-robot scenarios, exploiting robot cooperation to enhance patrol efficiency. We propose novel methods for calculating the variant values, and demonstrate their performance empirically.