🤖 AI Summary
To address robot and computational resource idling caused by receding-horizon frameworks in online concurrent multi-robot coverage path planning (CPP), this paper proposes the first non-horizon-constrained centralized online algorithm enabling truly concurrent path planning and execution. Our method employs a centralized architecture coupled with a lightweight concurrent scheduling mechanism, theoretically guaranteeing complete coverage while supporting real-time re-planning for dynamic robot subsets. Evaluated on eight large-scale 2D grid benchmarks—up to 512 robots—our approach achieves 1.6× faster coverage than state-of-the-art methods. We further validate it via ROS/Gazebo simulations and real-world indoor/outdoor experiments using quadcopters and TurtleBots, completing six simulation and two physical deployments. Results demonstrate significant improvements in planning efficiency and resource utilization for large-scale unknown environments.
📝 Abstract
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.