分层规划让上百无人机在复杂环境实时避障，避免死锁。
Hierarchical Trajectory (Re)Planning for a Large Scale Swarm
- 分层规划：划分子区域并行计算路径
- 142架无人机仿真中成功避障，24架物理无人机验证有效
- 兼顾集中式效率与分布式实时性，适合大规模集群
针对复杂环境中大规模无人机集群的轨迹重规划问题，本文提出一种分层规划方法。将工作空间划分后，在每个子区域内并行计算无碰撞路径。通过分布式轨迹优化生成无死锁的可行轨迹，即使优化失败也能保持控制可行性。该方法融合集中式与分布式优势，在保证高任务成功率的同时实现实时重规划。相比去中心化方法，显著减少死锁与碰撞。算法在最多142架无人机的仿真中验证了实时性能，并通过24架Crazyflie微型四旋翼的实物实验进行了验证。
原文摘要 · Abstract (English)
We consider the trajectory replanning problem for a large-scale swarm in a cluttered environment. Our path planner replans for robots by utilizing a hierarchical approach, dividing the workspace, and computing collision-free paths for robots within each cell in parallel. Distributed trajectory optimization generates a deadlock-free trajectory for efficient execution and maintains the control feasibility even when the optimization fails. Our hierarchical approach combines the benefits of both centralized and decentralized methods, achieving a high task success rate while providing real-time replanning capability. Compared to decentralized approaches, our approach effectively avoids deadlocks and collisions, significantly increasing the task success rate. We demonstrate the real-time performance of our algorithm with up to 142 robots in simulation, and a representative 24 physical Crazyflie nano-quadrotor experiment.
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