FG-PE: Factor-graph Approach for Multi-robot Pursuit-Evasion

TL;DR

This paper introduces a factor graph-based method for multi-robot pursuit-evasion that improves evader tracking accuracy, robustness to communication issues, and reduces capture time and distance traveled.

Contribution

It presents a novel factor graph approach for pursuit-evasion, enhancing estimation accuracy and robustness in multi-robot scenarios compared to existing methods.

Findings

Outperforms traditional pursuit-evasion techniques in key metrics

Reduces evader capture time and pursuit distance

Maintains robustness despite communication message loss

Abstract

With the increasing use of robots in daily life, there is a growing need to provide robust collaboration protocols for robots to tackle more complicated and dynamic problems effectively. This paper presents a novel, factor graph-based approach to address the pursuit-evasion problem, enabling accurate estimation, planning, and tracking of an evader by multiple pursuers working together. It is assumed that there are multiple pursuers and only one evader in this scenario. The proposed method significantly improves the accuracy of evader estimation and tracking, allowing pursuers to capture the evader in the shortest possible time and distance compared to existing techniques. In addition to these primary objectives, the proposed approach effectively minimizes uncertainty while remaining robust, even when communication issues lead to some messages being dropped or lost. Through a series of comprehensive experiments, this paper demonstrates that the proposed algorithm consistently outperforms traditional pursuit-evasion methods across several key performance metrics, such as the time required to capture the evader and the average distance traveled by the pursuers. Additionally, the proposed method is tested in real-world hardware experiments, further validating its effectiveness and applicability.