多智能体水上机器人可重构形态，实时保证安全避障。
Distributed Motion Planning with Safety Guarantees for Self-Reconfiguring Robotic Boats
- 分布式模型预测控制结合屏障函数，实现协同轨迹规划。
- 25个仿真机器人和4个实体机器人验证有效性和可扩展性。
- 适合需要动态重组与实时安全的群体机器人系统研究者。
水下自重构机器人需在组装成目标形状时确保多智能体间的安全交互。本文提出一种混合框架，将分布式模型预测控制（MPC）与控制屏障函数（CBFs）相结合，用于多智能体形状形成与重构。给定目标形状和任务分配后，通过交替方向乘子法（ADMM）求解分布式MPC，实现局部优化与信息交换的协同轨迹计算。为实现实时安全性，采用分布式CBF滤波器强制执行智能体间避撞。该方法利用MPC的预测能力缓解局部极小问题，同时在非凸优化条件下提供形式化安全保证。仿真结果涵盖最多25个智能体，实验验证使用4个物理机器人，证明了该框架的有效性与可扩展性。
原文摘要 · Abstract (English)
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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