Rhocop: Receding Horizon Multi-Robot Coverage

Coverage of a partially known workspace for information gathering is the core problem for several applications, such as search and rescue, precision agriculture and monitoring of critical infrastructures. We propose a planning framework for the coverage of a partially known environment employing multiple robots. To cope with the limitation of having incomplete information, our planner adopts a receding horizon planning strategy where the safe trajectories of the robots are generated optimally for a short duration based on the currently available information about the workspace. Moreover, as multi-robot motion planning for coverage is a computationally complex problem, our framework clusters the robots into small groups to increase the planning efficiency dynamically. In each time horizon, the robots follow the motion plans provided by the planner, gather information about the workspace while executing their plans and update the global knowledge base about the workspace. The planning algorithm manages the activities of the robots in such a way that the energy consumption by the robots and the total time required for the complete coverage of the workspace get minimized. Simulation results show that the proposed hierarchical framework efficiently ensures the coverage quality of a partially known workspace, as well as scales up effectively with the number of robots and the size of the workspace.