Multi-Robot Pursuit in Parameterized Formation via Imitation Learning

TL;DR

This paper introduces a parameterized formation control strategy for multi-robot pursuit, using imitation learning and model predictive control to improve capture success against faster attackers with limited communication.

Contribution

It presents a novel combination of parameterized formation control and imitation learning with model predictive control for multi-robot pursuit tasks.

Findings

Robots rapidly learn effective pursuit strategies.

Learned strategies are robust across different team sizes.

Experimental validation confirms real-world applicability.

Abstract

This paper studies the problem of multi-robot pursuit of how to coordinate a group of defending robots to capture a faster attacker before it enters a protected area. Such operation for defending robots is challenging due to the unknown avoidance strategy and higher speed of the attacker, coupled with the limited communication capabilities of defenders. To solve this problem, we propose a parameterized formation controller that allows defending robots to adapt their formation shape using five adjustable parameters. Moreover, we develop an imitation-learning based approach integrated with model predictive control to optimize these shape parameters. We make full use of these two techniques to enhance the capture capabilities of defending robots through ongoing training. Both simulation and experiment are provided to verify the effectiveness and robustness of our proposed controller. Simulation results show that defending robots can rapidly learn an effective strategy for capturing the attacker, and moreover the learned strategy remains effective across varying numbers of defenders. Experiment results on real robot platforms further validated these findings.