发表机构
KU Leuven; University of Wisconsin-Madison; Nanyang Technological University; SMART; SENSEable City Laboratory, Massachusetts Institute of Technology; Computer Science and Artificial Intelligence Lab (CSAIL), Massachusetts Institute of Technology（鲁汶大学; 威斯康星大学麦迪逊分校; 南洋理工大学; 新加坡制造技术研究院; 麻省理工学院可感知城市实验室; 麻省理工学院计算机科学与人工智能实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究水生自重构机器人多智能体形状形成与重构问题，提出将分布式MPC与CBF相结合的混合框架，利用ADMM求解MPC方案，通过局部优化和信息交换计算轨迹，并应用基于CBF的滤波器保障安全，经仿真和实验验证了框架的有效性与可扩展性。
AI 中文摘要
水生自重构机器人必须在确保多个智能体之间安全交互的同时组装成所需形状。本文提出了一种混合框架，将分布式模型预测控制（MPC）与控制障碍函数（CBF）相结合，用于多智能体形状形成和重构。给定所需形状和目标分配，通过交替方向乘子法（ADMM）求解的分布式MPC方案通过局部优化和信息交换计算协调轨迹。为实时确保安全，应用基于分布式CBF的滤波器来强制避免智能体间碰撞。该方法利用MPC的预测能力减轻局部极小值，而CBF尽管基础优化问题非凸仍提供形式上的安全保障。多达25个智能体的仿真结果和四个物理机器人的实验验证证明了该框架的有效性和可扩展性。
英文摘要
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.
CommentsSubmitted to IEEE
