Multi-robot collaborative navigation is an essential ability where teamwork and synchronization are keys. In complex and uncertain environments, adaptive formation is vital, as rigid formations prove to be inadequate. The ability of robots to dynamically adjust their formation enables navigation through unpredictable spaces, maintaining cohesion, and effectively responding to environmental challenges. In this paper, we introduce a novel approach that uses bi-level learning framework. Specifically, we use graph learning at a high level for group coordination and reinforcement learning for individual navigation. We innovate by integrating a spring-damper model within the reinforcement learning reward mechanism, addressing the rigidity of traditional formation control methods. During execution, our approach enables a team of robots to successfully navigate challenging environments, maintain a desired formation shape, and dynamically adjust their formation scale based on environmental information. We conduct extensive experiments to evaluate our approach across three distinct formation scenarios in multi-robot navigation: circle, line, and wedge. Experimental results show that our approach achieves promising results and scalability on multi-robot navigation with formation adaptation.

翻译：多机器人协同导航是一项关键能力，团队协作与同步是其核心要素。在复杂不确定环境中，刚性编队已显不足，自适应编队能力至关重要。机器人动态调整编队的能力使其能够穿越不可预测的空间，保持整体一致性，并有效应对环境挑战。本文提出一种新颖方法，采用双层学习框架：具体而言，高层使用图学习实现群体协调，底层使用强化学习实现个体导航。我们创新性地在强化学习奖励机制中融入弹簧阻尼模型，解决了传统编队控制方法的刚性问题。执行过程中，该方法使机器人团队能够成功穿越复杂环境，维持目标编队形态，并根据环境信息动态调整编队尺度。我们通过多机器人导航中三种典型编队场景（圆形、线形、楔形）开展大量实验评估。实验结果表明，该方法在具有编队自适应能力的多机器人导航任务中取得了优异性能与可扩展性。