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In this paper, we present a learning approach to goal assignment andtrajectory planning for unlabeled robots operating in 2D, obstacle-filledworkspaces. More specifically, we tackle the unlabeled multi-robot motionplanning problem with motion constraints as a multi-agent reinforcementlearning problem with some sparse global reward. In contrast with previousworks, which formulate an entirely new hand-crafted optimization cost ortrajectory generation algorithm for a different robot dynamic model, ourframework is a general approach that is applicable to arbitrary robot models.Further, by using the velocity obstacle, we devise a smooth projection thatguarantees collision free trajectories for all robots with respect to theirneighbors and obstacles. The efficacy of our algorithm is demonstrated throughvaried simulations.

Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints