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We investigate time-optimal Multi-Robot Coverage Path Planning (MCPP) forboth unweighted and weighted terrains, which aims to minimize the coveragetime, defined as the maximum travel time of all robots. Specifically, we focuson a reduction from MCPP to Min-Max Rooted Tree Cover (MMRTC). For the firsttime, we propose a Mixed Integer Programming (MIP) model to optimally solveMMRTC, resulting in an MCPP solution with a coverage time that is provably atmost four times the optimal. Moreover, we propose two suboptimal yet effectiveheuristics that reduce the number of variables in the MIP model, thus improvingits efficiency for large-scale MCPP instances. We show that both heuristicsresult in reduced-size MIP models that remain complete (i.e., guaranteed tofind a solution if one exists) for all MMRTC instances. Additionally, weexplore the use of model optimization warm-startup to further improve theefficiency of both the original MIP model and the reduced-size MIP models. Wevalidate the effectiveness of our MIP-based MCPP planner through experimentsthat compare it with two state-of-the-art MCPP planners on various instances,demonstrating a reduction in the coverage time by an average of 27.65% and23.24% over them, respectively.

Mixed Integer Programming for Time-Optimal Multi-Robot Coverage Path Planning with Efficient Heuristics