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.

翻译：本文研究了多机器人追捕问题，即如何协调一组防御机器人，在速度更快的攻击者进入保护区之前将其捕获。由于攻击者规避策略未知且速度更快，加之防御机器人通信能力有限，此类防御操作具有挑战性。为解决该问题，我们提出一种参数化编队控制器，使防御机器人能够通过五个可调参数调整其编队形态。此外，我们开发了一种与模型预测控制相结合的模仿学习方法，以优化这些形态参数。我们充分利用这两种技术，通过持续训练提升防御机器人的捕获能力。本文提供了仿真与实验验证所提控制器的有效性与鲁棒性。仿真结果表明，防御机器人能够快速学习捕获攻击者的有效策略，且所学策略在不同防御机器人数量下均保持有效。真实机器人平台上的实验结果进一步验证了这些发现。