Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats
2026-07-22 • Robotics
Robotics
AI summaryⓘ
The authors developed a system to help small swimming robots team up and form shapes safely without bumping into each other. They combined two techniques: one that plans robot paths by predicting future moves (MPC), and another that acts like a safety guard to avoid collisions (CBFs). Their method lets each robot decide its own path while sharing information with others, making the group work smoothly. They tested this with computer simulations of many robots and real tests with four robots, showing it works well and can handle different group sizes.
Aquatic robotsSelf-reconfigurable robotsModel Predictive Control (MPC)Control Barrier Functions (CBFs)Distributed controlMulti-agent systemsCollision avoidanceAlternating Direction Method of Multipliers (ADMM)Trajectory planningShape formation
Authors
Alejandro Gonzalez-Garcia, Wei Wang, Wei Xiao, Wilm Decre, Jan Swevers, Carlo Ratti, Daniela Rus
Abstract
Aquatic self-reconfigurable robots must assemble into desired shapes while ensuring safe interactions among multiple agents. This paper proposes a hybrid framework that combines distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs) for multi-agent shape formation and reconfiguration. Given a desired shape and target assignment, a distributed MPC scheme, solved via the Alternating Direction Method of Multipliers (ADMM), computes coordinated trajectories through local optimization and information exchange. To ensure safety in real time, distributed CBF-based filters are applied to enforce inter-agent collision avoidance. The proposed approach leverages the predictive capabilities of MPC to mitigate local minima, while CBFs provide formal safety guarantees despite the nonconvexity of the underlying optimization problem. Simulation results with up to 25 agents and experimental validation with four physical robots demonstrate the effectiveness and scalability of the framework.