Game-theoretic Utility Tree for Multi-Robot Cooperative Pursuit Strategy

TL;DR

This paper introduces the Game-theoretic Utility Tree (GUT), a hierarchical model for multi-robot cooperation in pursuit-evasion scenarios, demonstrating improved performance over traditional strategies through experiments on the Robotarium platform.

Contribution

The paper presents a novel hierarchical game-theoretic model, GUT, for multi-robot cooperative pursuit, extending existing models to enhance coordination in adversarial environments.

Findings

GUT outperforms conventional pursuit strategies in experiments.

GUT effectively organizes cooperative strategies among robots.

Fewer advantaged robots can achieve higher success rates with GUT.

Abstract

Underlying relationships among multiagent systems (MAS) in hazardous scenarios can be represented as game-theoretic models. In adversarial environments, the adversaries can be intentional or unintentional based on their needs and motivations. Agents will adopt suitable decision-making strategies to maximize their current needs and minimize their expected costs. This paper proposes and extends the new hierarchical network-based model, termed Game-theoretic Utility Tree (GUT), to arrive at a cooperative pursuit strategy to catch an evader in the Pursuit-Evasion game domain. We verify and demonstrate the performance of the proposed method using the Robotarium platform compared to the conventional constant bearing (CB) and pure pursuit (PP) strategies. The experiments demonstrated the effectiveness of the GUT, and the performances validated that the GUT could effectively organize cooperation strategies, helping the group with fewer advantages achieve higher performance.