Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats

2026-07-22

AI Summary

This work addresses the coupled challenge of target configuration assembly and collision avoidance in underwater multi-agent self-reconfiguration by proposing a hybrid framework that integrates distributed Model Predictive Control (MPC) with Control Barrier Functions (CBFs). The approach leverages the Alternating Direction Method of Multipliers (ADMM) to enable scalable distributed trajectory planning, exploits MPC’s predictive capabilities to mitigate local minima, and employs CBFs to provide formal safety guarantees within non-convex optimization. Extensive simulations demonstrate the method’s efficacy with up to 25 agents, while real-world experiments successfully validate its performance on a team of four autonomous underwater vehicles, confirming its effectiveness, safety, and scalability in complex environments.

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.

Problem

• self-reconfiguring robots
• multi-agent safety
• collision avoidance
• shape formation
• distributed motion planning

Innovation

• Distributed MPC
• Control Barrier Functions
• Self-Reconfiguring Robots
• Collision Avoidance
• ADMM