标题： 通过分布式模型预测控制的同伦感知多智能体导航 显示英文标题

标题： Homotopy-aware Multi-agent Navigation via Distributed Model Predictive Control

摘要： 多智能体轨迹规划需要确保安全性和效率，但死锁仍然是一个重大挑战，尤其是在障碍物密集的环境中。 这种死锁经常发生在多个智能体同时尝试穿过同一段长而狭窄的走廊时。 为了解决这个问题，我们提出了一种新的分布式轨迹规划框架，弥合了全局路径和局部轨迹协作之间的差距。 在全局层面，提出了一种具有同伦意识的最优路径规划算法，该算法充分利用了环境的拓扑结构。 通过考虑其空间和时间特性，从不同的同伦类中选择一条参考路径，从而提高了智能体之间的全局协调性。 在局部层面，采用基于模型预测控制的轨迹优化方法来生成动态可行且无碰撞的轨迹。 此外，在线重规划策略确保了其对动态环境的适应性。 仿真和实验验证了我们方法在缓解死锁方面的有效性。 消融研究表明，通过将时间感知的同伦性质纳入底层全局路径，我们的方法可以显著减少死锁，并将随机生成的密集场景中的平均成功率从4%-13%提高到90%以上。 显示英文摘要

摘要： Multi-agent trajectory planning requires ensuring both safety and efficiency, yet deadlocks remain a significant challenge, especially in obstacle-dense environments. Such deadlocks frequently occur when multiple agents attempt to traverse the same long and narrow corridor simultaneously. To address this, we propose a novel distributed trajectory planning framework that bridges the gap between global path and local trajectory cooperation. At the global level, a homotopy-aware optimal path planning algorithm is proposed, which fully leverages the topological structure of the environment. A reference path is chosen from distinct homotopy classes by considering both its spatial and temporal properties, leading to improved coordination among agents globally. At the local level, a model predictive control-based trajectory optimization method is used to generate dynamically feasible and collision-free trajectories. Additionally, an online replanning strategy ensures its adaptability to dynamic environments. Simulations and experiments validate the effectiveness of our approach in mitigating deadlocks. Ablation studies demonstrate that by incorporating time-aware homotopic properties into the underlying global paths, our method can significantly reduce deadlocks and improve the average success rate from 4%-13% to over 90% in randomly generated dense scenarios.