A New Criterion of Human Comfort Assessment for Wheelchair Robots by Q-Learning Based Accompanist Tracking Fuzzy Controller

Abstract

Wheelchair users often struggle to drive safely and accompanied by caregivers. Wheelchair robots can employ the autonomous functions to avoid obstacles and reduce the workload of the caregivers. To achieve the goal, the accompanist needs to be steadily recognized and tracked by the robots. By using Q-learning based accompanist tracking fuzzy controller, wheelchair robots can peacefully follow the accompanist. Meanwhile, it is important to make the people on wheelchair robots feel comfortable. Based on ISO 2631-1, the ride qualities are obtained by averaging the acceleration data in the frequency bands, and the critical thresholds utilizing to assess the ride comfort are also determined inside. To make the level of bumpiness be as multiple as possible, four kinds of pavements are chosen in the experiments. The results show that the feelings of the occupants on the wheelchair robot are quite different from the ride comfort defined in ISO 2631-1, so a new standard is proposed to assess the ride comfort in this study. The accuracy of the proposed standard is 90.67 %, which is higher than that of ISO 2631-1, 42.48 %. Furthermore, to the best of our knowledge, this paper is thought to be the first one to present the ISO 2631-1-based comfort criterion for wheelchair robots.