| Summary: | 碩士 === 國立交通大學 === 電控工程研究所 === 106 === When the wheelchair is riding in the outdoor environment, the varied types of pavement materials may make people on the wheelchair feel uncomfortable. This paper proposes an intelligent wheelchair controller with pavement recognition and the riding comfort estimation. The deep learning method is used to train a pavement recognition model which can distinguish the current pavement type during the riding process. Meanwhile, it’s necessary to ensure the riding comfort judged by a wheelchair comfort standard defined by the weighted root-mean-square acceleration in ISO 2631-1 and the riding experiment questionnaires. A Q-Learning based Adaptive Network-based Fuzzy Interface System (ANFIS) controller uses the speed command received from the user, the riding comfort index, and the pavement type to adjust the output speed command for the wheelchair. Over 84.24% of the experiment surveys reported good riding comfort from the harshest riding environment. At the same time, the excellent riding comfort feedback percentage from using the controller with the pavement recognition is increased by 25% compared to the percentage from using the controller without the pavement recognition. The intelligent wheelchair is able to effectively distinguish the pavement type and adjust the speed command according to the user requirement of the riding comfort.
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