Robust-by-Design Plans for Multi-Robot Pursuit-Evasion

TL;DR

This paper introduces a robust pursuit-evasion planning method for multi-robot systems that ensures evader detection even if one pursuer fails, by designing inherently fault-tolerant plans using new data structures and sampling techniques.

Contribution

It presents a novel formulation for pursuit-evasion that guarantees detection despite single robot failures, and develops a new data structure and sampling method for robust plan generation.

Findings

Plans are inherently tolerant to single robot failures.

Simulation results demonstrate improved robustness over existing methods.

The approach reduces the need for replanning during failures.

Abstract

This paper studies a multi-robot visibility-based pursuit-evasion problem in which a group of pursuer robots are tasked with detecting an evader within a two dimensional polygonal environment. The primary contribution is a novel formulation of the pursuit-evasion problem that modifies the pursuers' objective by requiring that the evader still be detected, even in spite of the failure of any single pursuer robot. This novel constraint, whereby two pursuers are required to detect an evader, has the benefit of providing redundancy to the search, should any member of the team become unresponsive, suffer temporary sensor disruption/failure, or otherwise become incapacitated. Existing methods, even those that are designed to respond to failures, rely on the pursuers to replan and update their search pattern to handle such occurrences. In contrast, the proposed formulation produces plans that are inherently tolerant of some level of disturbance. Building upon this new formulation, we introduce an augmented data structure for encoding the problem state and a novel sampling technique to ensure that the generated plans are robust to failures of any single pursuer robot. An implementation and simulation results illustrating the effectiveness of this approach are described.