Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats
Authors: Alejandro Gonzalez-Garcia, Wei Wang, Wei Xiao, Wilm Decre, Jan Swevers, Carlo Ratti, Daniela Rus
Organizations: MECO Research Team, Department of Mechanical Engineering, KU Leuven, Belgium · Marine Robotics Lab, Department of Mechanical Engineering, College of Engineering, University of Wisconsin-Madison, Madison, WI 53706 USA · School of Electrical and Electronic Engineering, Nanyang Technological University, and with M3S, SMART, Singapore · SENSEable City Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139 USA · Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology, Cambridge, MA 02139 USA
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.