MC-Swarm：面向四旋翼集群的最小通信多智能体轨迹规划与死锁解决
MC-Swarm: Minimal-Communication Multi-Agent Trajectory Planning and Deadlock Resolution for Quadrotor Swarm
AI总结：
提出MC-Swarm分布式四旋翼集群轨迹规划算法，仅初始阶段通信，借助协调状态更新与轨迹优化实现异步避碰和无死锁，并通过仿真与真实实验验证。
摘要：
为了实现有效的多智能体轨迹规划，必须考虑轻量级通信及其潜在的异步性。本文提出一种用于四旋翼集群的分布式轨迹规划算法，该算法异步运行，并且除初始规划阶段外不需要通信。此外，我们的算法保证在异步更新和飞行期间无通信的情况下不会发生死锁。为有效确保这些要点，我们构建了两个主要模块：协调状态更新器和轨迹优化器。协调状态更新器为每个智能体计算朝向其目标的航点，并在考虑死锁以及相对邻近智能体和障碍物的安全约束的同时执行子目标优化。随后，轨迹优化器生成一条即使在邻近智能体异步规划更新的情况下也能确保避碰的轨迹。我们提供了带死锁解决的避碰理论保证，并在包括随机森林和窄缝迷宫在内的复杂仿真环境中评估了方法的有效性。此外，为减少总任务时间，我们利用轻量级通信设计了更快的协调状态更新。最后，通过在杂乱环境场景下的大量仿真和真实世界实验验证了我们的方法。
英文摘要：
For effective multi-agent trajectory planning, it is important to consider lightweight communication and its potential asynchrony. This paper presents a distributed trajectory planning algorithm for a quadrotor swarm that operates asynchronously and requires no communication except during the initial planning phase. Moreover, our algorithm guarantees no deadlock under asynchronous updates and absence of communication during flight. To effectively ensure these points, we build two main modules: coordination state updater and trajectory optimizer. The coordination state updater computes waypoints for each agent toward its goal and performs subgoal optimization while considering deadlocks, as well as safety constraints with respect to neighbor agents and obstacles. Then, the trajectory optimizer generates a trajectory that ensures collision avoidance even with the asynchronous planning updates of neighboring agents. We provide a theoretical guarantee of collision avoidance with deadlock resolution and evaluate the effectiveness of our method in complex simulation environments, including random forests and narrow-gap mazes. Additionally, to reduce the total mission time, we design a faster coordination state update using lightweight communication. Lastly, our approach is validated through extensive simulations and real-world experiments with cluttered environment scenarios.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
