家庭内支援・見守りにおける全方向移動ロボットの人物追従のためのRL-DWAモーションプランニング
RL-DWA Omnidirectional Motion Planning for Person Following in Domestic Assistance and Monitoring
全方向移動ロボットで人を追従するため、並進速度は従来のDWAで、旋回速度は深層強化学習で予測する手法を提案し、実機実験で有効性を示した。
著者: Andrea Eirale, Mauro Martini, Marcello Chiaberge
分類: cs.RO, cs.AI
原文アブストラクト
Robot assistants are emerging as high-tech solutions to support people in everyday life. Following and assisting the user in the domestic environment requires flexible mobility to safely move in cluttered spaces. We introduce a new approach to person following for assistance and monitoring. Our methodology exploits an omnidirectional robotic platform to detach the computation of linear and angular velocities and navigate within the domestic environment without losing track of the assisted person. While linear velocities are managed by a conventional Dynamic Window Approach (DWA) local planner, we trained a Deep Reinforcement Learning (DRL) agent to predict optimized angular velocities commands and maintain the orientation of the robot towards the user. We evaluate our navigation system on a real omnidirectional platform in various indoor scenarios, demonstrating the competitive advantage of our solution compared to a standard differential steering following.
