🤖 AI Summary
This work addresses safe collaborative navigation for multi-robot systems without individual reference trajectories. Methodologically, it proposes a behavior-driven safe multi-agent reinforcement learning framework that employs only the formation centroid as the navigation target—eliminating conventional per-robot path planners—and integrates model predictive control (MPC) as an online safety filter to explicitly guarantee collision-free operation during both training and deployment. To our knowledge, this is the first approach achieving provably safe collaborative navigation under the no-individual-reference setting. The MPC constraints not only accelerate policy convergence but also enable safe online deployment on real robots even in early training stages. Extensive simulations and real-world experiments demonstrate zero collisions, faster target arrival compared to baselines, and robust practical performance—validating both efficacy and deployability.
📝 Abstract
In this letter, we address the problem of behavior-based cooperative navigation of mobile robots usingsafe multi-agent reinforcement learning (MARL). Our work is the first to focus on cooperative navigation without individual reference targets for the robots, using a single target for the formation's centroid. This eliminates the complexities involved in having several path planners to control a team of robots. To ensure safety, our MARL framework uses model predictive control (MPC) to prevent actions that could lead to collisions during training and execution. We demonstrate the effectiveness of our method in simulation and on real robots, achieving safe behavior-based cooperative navigation without using individual reference targets, with zero collisions, and faster target reaching compared to baselines. Finally, we study the impact of MPC safety filters on the learning process, revealing that we achieve faster convergence during training and we show that our approach can be safely deployed on real robots, even during early stages of the training.
