四旋翼集群无需飞行中通信,可自动避障防死锁。
MC-Swarm: Minimal-Communication Multi-Agent Trajectory Planning and Deadlock Resolution for Quadrotor Swarm
- 异步分布式规划,仅初始阶段需通信。
- 理论证明无碰撞且无死锁,支持动态避障。
- 适合复杂环境的无人机集群任务,实测有效。
为实现高效的多智能体轨迹规划,需考虑轻量级通信及其潜在异步性。本文提出一种分布式四旋翼集群轨迹规划算法,可在异步更新下运行,且飞行期间无需通信。算法保证在无通信情况下不产生死锁。核心包含两个模块:协调状态更新器和轨迹优化器。协调状态更新器计算各智能体至目标的路径点,并进行子目标优化,同时考虑死锁、邻近智能体与障碍物的安全约束。轨迹优化器生成的轨迹能抵御邻居智能体异步规划带来的碰撞风险。我们提供了碰撞避免与死锁解决的理论保障,并在随机森林和窄缝迷宫等复杂仿真环境中验证方法有效性。为缩短任务总时长,设计了轻量通信的快速协调状态更新机制。最后,通过大量仿真与真实世界实验,在杂乱环境场景中验证了该方法的实用性。
原文摘要 · Abstract (English)
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.
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