群衆環境での自律型マップレスナビゲーション
CrowdMove: Autonomous Mapless Navigation in Crowded Scenarios
マルチロボット・マルチシナリオ・マルチステージの3M学習フレームワークで、歩行者群衆のような動的環境でも安全に動けるマップレスナビゲーション方策を強化学習で獲得した研究。
著者: Tingxiang Fan, Xinjing Cheng, Jia Pan, Dinesh Manocha, Ruigang Yang
分類: cs.RO
原文アブストラクト
Navigation is an essential capability for mobile robots. In this paper, we propose a generalized yet effective 3M (i.e., multi-robot, multi-scenario, and multi-stage) training framework. We optimize a mapless navigation policy with a robust policy gradient algorithm. Our method enables different types of mobile platforms to navigate safely in complex and highly dynamic environments, such as pedestrian crowds. To demonstrate the superiority of our method, we test our methods with four kinds of mobile platforms in four scenarios. Videos are available at https://sites.google.com/view/crowdmove.
