改进几何规划，让多机器人在对称场景中不撞车、不僵死。
Forecast-Driven MPC for Decentralized Multi-Robot Collision Avoidance
- 用碰撞时间优化威胁排序，提升决策效率。
- 引入成本引导的路径点选择，减少轨迹振荡。
- 融合模型预测控制，确保动态可行性与稳定运行。
迭代预测规划器（IFP）是一种轻量级、可扩展且响应迅速的几何规划方法，适用于去中心化、无通信的多机器人路径规划。然而，在对称配置中，镜像交互常导致碰撞和死锁。本文提出eIFP-MPC，对IFP进行优化与扩展，显著提升密集动态环境中的鲁棒性与路径一致性。该方法通过碰撞时间启发式优化威胁优先级，利用基于成本的路径点选择稳定路径生成，并将模型预测控制（MPC）融入规划流程以保证动态可行性。这些改进紧密集成于IFP框架，保持其高效性的同时增强适应性与稳定性。大量仿真结果表明，eIFP-MPC在对称及高密度场景中显著降低轨迹振荡，实现无碰撞运动，并提升轨迹效率。结果证明，通过优化可使几何规划器在复杂多智能体环境中实现可扩展的鲁棒性能。
原文摘要 · Abstract (English)
The Iterative Forecast Planner (IFP) is a geometric planning approach that offers lightweight computations, scalable, and reactive solutions for multi-robot path planning in decentralized, communication-free settings. However, it struggles in symmetric configurations, where mirrored interactions often lead to collisions and deadlocks. We introduce eIFP-MPC, an optimized and extended version of IFP that improves robustness and path consistency in dense, dynamic environments. The method refines threat prioritization using a time-to-collision heuristic, stabilizes path generation through cost-based via-point selection, and ensures dynamic feasibility by incorporating model predictive control (MPC) into the planning process. These enhancements are tightly integrated into the IFP to preserve its efficiency while improving its adaptability and stability. Extensive simulations across symmetric and high-density scenarios show that eIFP-MPC significantly reduces oscillations, ensures collision-free motion, and improves trajectory efficiency. The results demonstrate that geometric planners can be strengthened through optimization, enabling robust performance at scale in complex multi-agent environments.
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