Recently, centralized receding horizon online multi-robot coverage path planning algorithms have shown remarkable scalability in thoroughly exploring large, complex, unknown workspaces with many robots. In a horizon, the path planning and the path execution interleave, meaning when the path planning occurs for robots with no paths, the robots with outstanding paths do not execute, and subsequently, when the robots with new or outstanding paths execute to reach respective goals, path planning does not occur for those robots yet to get new paths, leading to wastage of both the robotic and the computation resources. As a remedy, we propose a centralized algorithm that is not horizon-based. It plans paths at any time for a subset of robots with no paths, i.e., who have reached their previously assigned goals, while the rest execute their outstanding paths, thereby enabling concurrent planning and execution. We formally prove that the proposed algorithm ensures complete coverage of an unknown workspace and analyze its time complexity. To demonstrate scalability, we evaluate our algorithm to cover eight large $2$D grid benchmark workspaces with up to 512 aerial and ground robots, respectively. A comparison with a state-of-the-art horizon-based algorithm shows its superiority in completing the coverage with up to 1.6x speedup. For validation, we perform ROS + Gazebo simulations in six 2D grid benchmark workspaces with 10 quadcopters and TurtleBots, respectively. We also successfully conducted one outdoor experiment with three quadcopters and one indoor with two TurtleBots.

翻译：近期，集中式滚动时域在线多机器人覆盖路径规划算法在利用大量机器人充分探索大型、复杂、未知工作空间方面表现出显著的可扩展性。在时域内，路径规划与路径执行交替进行，即当为尚无路径的机器人进行规划时，拥有未完成路径的机器人暂停执行；随后当拥有新路径或未完成路径的机器人执行并抵达各自目标时，尚未获得新路径的机器人则无法进行规划，导致机器人与计算资源双重浪费。为解决此问题，我们提出一种非时域型集中式算法。该算法可随时为无路径（即已抵达先前分配目标的）机器人子集规划路径，同时其余机器人继续执行未完成路径，从而实现规划与执行的并行化。我们从理论上证明该算法能确保未知工作空间的完全覆盖，并分析其时间复杂度。为验证可扩展性，我们在8个大型二维栅格基准工作空间中分别对多达512架空中机器人和地面机器人进行覆盖评估。与最新时域基算法对比表明，该算法能以高达1.6倍加速比完成覆盖任务。通过ROS+Gazebo仿真，我们在6个二维栅格基准工作空间中分别使用10台四旋翼飞行器和10台TurtleBot机器人进行验证；同时成功开展室外实验（3台四旋翼飞行器）与室内实验（2台TurtleBot机器人）。