深層強化学習による国際標準不整地上でのレスキューロボットの自律走行

抄録

Robots that perform rescue and search operations at disaster sites are called rescue robots. These robots should be able to be navigated operate autonomously because remote control is difficult. The objective of this research is to enable rescue robots to be navigated autonomously on international standard rough terrain. To achieve this objective， we built a learning environment in a simulator using Unity，a physics engine，and conducted deep reinforcement learning using machine learning framework of Unity，ML-Agents. Comparative verification with remote control showed that autonomous navigation was superior to remote control in both time and success rate. The reason for this result was found to be the difference in the motion of the robot.