強化学習によるクルーズ型AUVのピッチ角選択アルゴリズム~高速かつ低高度な海底追従を目指して~

抄録

Low altitude survey of seafloor is important in various fields, such as biological research or resource survey. An AUV (Autonomous Underwater Vehicle) plays an important role. In this paper, we propose a new terrain following method, in which a reinforcement learning agent sets an appropriate pitch reference. We expect the new method results in self-adaptive to the environment. We conducted a simulation based analysis, in which the AUV traveled at a high surge speed (~2 m/s) in various sonar echo level environment.