无需通信的多智能体避障新方法，保障安全与持续可行。
Decentralized Contingency MPC based on Safe Sets for Nonlinear Multi-agent Collision Avoidance
- 每智能体独立求解优化问题，结合主轨迹与应急备份方案。
- 通过安全集更新机制，确保连续时间步间不丢失可行性。
- 适用于无通信或动态加入的复杂场景，理论保证安全收敛。
去中心化避障在缺乏轨迹信息共享时仍具挑战性。现有方法或过于保守，或难以保证递归可行性与收敛性。本文提出一种针对非线性多智能体系统的去中心化应急模型预测控制框架，在仅依赖状态信息条件下实现无碰撞运动。各智能体遵循统一共识规则，无需通信即可安全规划。每个智能体求解一个局部优化问题，耦合名义轨迹与应急验证证书，确保滚动时域操作下的可行备份动作。提出一种新颖的几何化、去中心化的安全集更新机制，防止连续时间步间可行性丧失。所提方法保证递归可行性（含避障），并建立类李雅普诺夫收敛性结果至可接受的安全均衡点。仿真结果表明其在稀疏与密集环境中的性能，包括复杂狭窄通道场景及即插即用操作下的有效性。
原文摘要 · Abstract (English)
Decentralized collision avoidance remains challenging, particularly when agents do not communicate any information related to planned trajectories. Most existing approaches either rely on conservative coordination mechanisms or provide limited guarantees on recursive feasibility and convergence. This paper develops a decentralized contingency MPC framework for multi-agent systems with nonlinear dynamics that achieves collision-free motion under a state-only information pattern. Each agent follows the same consensual rule set, enabling safe decentralized planning without communication. Each agent solves a local optimization problem that couples a nominal trajectory with a contingency certificate ensuring a feasible backup maneuver under receding-horizon operation. A novel geometric and decentralized safe-set update mechanism prevents feasibility loss between consecutive time steps. The resulting scheme guarantees recursive feasibility, including collision avoidance, and establishes a Lyapunov-type convergence result to an admissible safe equilibrium. Simulation results demonstrate performance in both sparse and dense multi-agent environments, including cluttered bottleneck scenarios and under plug-and-play operation.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。