Scalable Online Coverage Path Planning for Multi-Robot Systems

Abstract

Online coverage path planning to explore an unknown workspace with multiple homogeneous robots could be either centralized or distributed. While distributed planners are computationally faster, centralized planners can produce more efficient paths, reducing the duration of completing a coverage mission significantly. To exploit the power of a centralized framework, we propose a receding horizon centralized online multi-robot planner. In each planning horizon, it generates collision-free paths that guide the robots to visit some obstacle-free locations (aka goals) not visited so far, which in turn help them explore some new regions with their laser rangefinders. We formally prove that, under reasonable conditions, it enables the robots to cover a workspace completely and subsequently analyze its time complexity. We evaluate our planner for ground and aerial robots by performing experiments with up to 128 robots on six 2D grid-based benchmark obstacle maps, establishing scalability. We also perform Gazebo simulations with 10 quadcopters and real experiments with 2 four-wheel ground robots, demonstrating its practical feasibility. Further-more, a comparison with a state-of-the-art distributed planner establishes its superiority in coverage completion time.