運動制約付きの安全なラベルなしマルチロボット計画の学習
Learning Safe Unlabeled Multi-Robot Planning with Motion Constraints
障害物のある2D空間で、ラベルなしロボット群の目標割り当てと軌道計画をマルチエージェント強化学習で解き、速度障害物を用いた射影で衝突回避を保証する汎用的手法を提案した。
著者: Arbaaz Khan, Chi Zhang, Shuo Li, Jiayue Wu, Brent Schlotfeldt, Sarah Y. Tang, Alejandro Ribeiro, Osbert Bastani, Vijay Kumar
分類: cs.RO
原文アブストラクト
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.
