Experimental Study of Tracked Vehicle Velocity Using Estimated Disturbance and Machine Learning for Application to Environments Different from Those in Training
Experimental Study of Tracked Vehicle Velocity Using Estimated Disturbance and Machine Learning for Application to Environments Different from Those in Training
カテゴリ: 論文誌(論文単位)
グループ名: 【D】産業応用部門(英文)
発行日: 2024/03/01
タイトル(英語): Experimental Study of Tracked Vehicle Velocity Using Estimated Disturbance and Machine Learning for Application to Environments Different from Those in Training
著者名: Hiroaki Kuwahara (Shibaura Institute of Technology), Toshiyuki Murakami (Keio University)
著者名(英語): Hiroaki Kuwahara (Shibaura Institute of Technology), Toshiyuki Murakami (Keio University)
キーワード: tracked vehicle,velocity estimation,slippage,disturbance observer,machine learning
要約(英語): This study examines the training policies and environmental robustness of a neural network used in velocity estimation for a tracked vehicle with slippage. In the proposed method, the velocity is estimated by a neural network whose input is an estimated disturbance to the driving axle that includes slippage information. First, we experimentally clarify the proposed method's scope of applicability and effectiveness under different environmental conditions in training and estimation. Subsequently, we experimentally confirm that the estimated disturbance is robust to environmental changes and complementary to environmental information. Finally, the neural network trained on a flat surface is validated in combination with gravity compensation for acceleration to apply it to driving on a slope.
本誌掲載ページ: 146-154 p
原稿種別: 論文/英語
電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejjia/13/2/13_23004760/_article/-char/ja/
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