多机器人实时路径规划与轨迹控制一体化，支持动态环境下的长期稳定导航。
LF: Online Multi-Robot Path Planning Meets Optimal Trajectory Control
- 分层架构：离散全局规划+连续局部控制，异步高频运行
- 15架真实无人机在动态环境中完成随机目标更新任务
- 无需重设计，快速重规划即可适应变化场景，适合复杂应用
我们提出一种多机器人控制范式，解决具有全局环境信息的全向机器人团队的点对点导航任务。该框架以高频率异步运行两个过程：(i) 中心化、离散、全时域规划器，利用最新的多智能体路径规划（MAPF）技术快速计算无碰撞、无死锁的路径；(ii) 动力学感知、机器人级最优轨迹控制器，确保各机器人独立可靠地沿分配路径运动。这种从离散耦合到连续解耦的规划表示转变，使系统具备长期可扩展的运动合成能力。作为该思想的实例，我们提出LF，结合了先进的MAPF求解器LaCAM与鲁棒反馈控制栈Freyja，实现敏捷机动。LF在异步和部分目标更新下仍能提供鲁棒、通用的终身多机器人导航，并通过快速重规划即可适应动态工作空间。我们展示了多种多旋翼和地面机器人的实验结果，包括15架真实多旋翼在有人穿行的操作空间中完成连续随机目标更新的部署。
原文摘要 · Abstract (English)
We propose a multi-robot control paradigm to solve point-to-point navigation tasks for a team of holonomic robots with access to the full environment information. The framework invokes two processes asynchronously at high frequency: (i) a centralized, discrete, and full-horizon planner for computing collision- and deadlock-free paths rapidly, leveraging recent advances in multi-agent pathfinding (MAPF), and (ii) dynamics-aware, robot-wise optimal trajectory controllers that ensure all robots independently follow their assigned paths reliably. This hierarchical shift in planning representation from (i) discrete and coupled to (ii) continuous and decoupled domains enables the framework to maintain long-term scalable motion synthesis. As an instantiation of this idea, we present LF, which combines a fast state-of-the-art MAPF solver (LaCAM), and a robust feedback control stack (Freyja) for executing agile robot maneuvers. LF provides a robust and versatile mechanism for lifelong multi-robot navigation even under asynchronous and partial goal updates, and adapts to dynamic workspaces simply by quick replanning. We present various multirotor and ground robot demonstrations, including the deployment of 15 real multirotors with random, consecutive target updates while a person walks through the operational workspace.
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