A Visibility Roadmap Sampling Approach for a Multi-Robot Visibility-Based Pursuit-Evasion Problem

TL;DR

This paper introduces a new sampling-based algorithm for multi-robot pursuit-evasion in polygonal environments, ensuring termination and improved efficiency over existing methods.

Contribution

It proposes a sequential graph construction approach that guarantees a solution for complex environments, addressing limitations of prior sampling-based algorithms.

Findings

Significant reduction in execution time compared to existing algorithms.

High success rate in complex polygonal environments.

Algorithm guarantees termination regardless of environment complexity.

Abstract

Given a two-dimensional polygonal space, the multi-robot visibility-based pursuit-evasion problem tasks several pursuer robots with the goal of establishing visibility with an arbitrarily fast evader. The best known complete algorithm for this problem takes time doubly exponential in the number of robots. However, sampling-based techniques have shown promise in generating feasible solutions in these scenarios. One of the primary drawbacks to employing existing sampling-based methods is that existing algorithms have long execution times and high failure rates for complex environments. This paper addresses that limitation by proposing a new algorithm that takes an environment as its input and returns a joint motion strategy which ensures that the evader is captured by one of the pursuers. Starting with a single pursuer, we sequentially construct Sample-Generated Pursuit-Evasion Graphs to create such a joint motion strategy. This sequential graph structure ensures that our algorithm will always terminate with a solution, regardless of the complexity of the environment. We describe an implementation of this algorithm and present quantitative results that show significant improvement in comparison to the existing algorithm.