Paper Detail
Alejandro Gonzalez-Garcia, Wei Wang, Wei Xiao, Wilm Decre, Jan Swevers, Carlo Ratti, Daniela Rus
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.
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@article{gonzalezgarcia2026distributed,
title = {Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats},
author = {Alejandro Gonzalez-Garcia and Wei Wang and Wei Xiao and Wilm Decre and Jan Swevers and Carlo Ratti and Daniela Rus},
year = {2026},
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 op},
url = {https://arxiv.org/abs/2607.20352},
keywords = {cs.RO},
eprint = {2607.20352},
archiveprefix = {arXiv},
}
{}