Host: The Japan Society of Mechanical Engineers
Name : [in Japanese]
Date : June 06, 2021 - June 08, 2021
In this paper, we propose a jerk-suppressed action planning method for mobility robots or transfer robots using deep reinforcement learning by considering human-to-human and human-to-robot interactions. In a dynamic environment such as a station or an airport, surrounding situations are complex, so there is also a situation where a rule-based planning has difficulties in dealing with. We aim for safe and smooth action planning by using deep reinforcement. In this paper, we propose a jerk-suppressed action planning method for mobility robots or transfer robots using deep reinforcement learning by considering human-to-human and human-to-robot interactions. In a dynamic environment such as a station or an airport, surrounding situations are complex, so there is also a situation where a rule-based planning has difficulties in dealing with. We aim for safe and smooth action planning by using deep reinforcement.
