In this paper, we present a learning approach to goal assignment and trajectory planning for unlabeled robots operating in 2D, obstacle-filled workspaces. More specifically, we tackle the unlabeled multi-robot motion planning problem with motion constraints as a multi-agent reinforcement learning problem with some sparse global reward. In contrast with previous works, which formulate an entirely new hand-crafted optimization cost or trajectory generation algorithm for a different robot dynamic model, our framework is a general approach that is applicable to arbitrary robot models. Further, by using the velocity obstacle, we devise a smooth projection that guarantees collision free trajectories for all robots with respect to their neighbors and obstacles. The efficacy of our algorithm is demonstrated through varied simulations.
@article{arxiv.1907.05300,
title = {Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints},
author = {Arbaaz Khan and Chi Zhang and Shuo Li and Jiayue Wu and Brent Schlotfeldt and Sarah Y. Tang and Alejandro Ribeiro and Osbert Bastani and Vijay Kumar},
journal= {arXiv preprint arXiv:1907.05300},
year = {2019}
}