Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints
Arbaaz Khan, Chi Zhang, Shuo Li, Jiayue Wu, Brent Schlotfeldt, Sarah Y. Tang, Alejandro Ribeiro, Osbert Bastani
Abstract
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. A video describing our method and results can be found here.
BibTeX
@inproceedings{iros2019_learningsafeunla,
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},
booktitle = {IROS 2019},
year = {2019}
}