TY - GEN
T1 - Depth Control of a Biomimetic Manta Robot via Reinforcement Learning
AU - Zhang, Daili
AU - Pan, Guang
AU - Cao, Yonghui
AU - Huang, Qiaogao
AU - Cao, Yong
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - This paper proposes a model-free biomimetic manta robot depth control method based on reinforcement learning. Different from the traditional control method, the reinforcement learning method does not need to establish a mathematical model of the control object, and autonomously learns the control law through data training. Based on the classical Q algorithm, the state space, the action space, and reward function of the depth control of the bionic manta robot are designed. The state-action function is trained offline using the experience replay mechanism and random sampling strategy. Finally, the trained function is transplanted to the biomimetic manta robot prototype to establish a controller. The effectiveness of the proposed control method is verified by experiments.
AB - This paper proposes a model-free biomimetic manta robot depth control method based on reinforcement learning. Different from the traditional control method, the reinforcement learning method does not need to establish a mathematical model of the control object, and autonomously learns the control law through data training. Based on the classical Q algorithm, the state space, the action space, and reward function of the depth control of the bionic manta robot are designed. The state-action function is trained offline using the experience replay mechanism and random sampling strategy. Finally, the trained function is transplanted to the biomimetic manta robot prototype to establish a controller. The effectiveness of the proposed control method is verified by experiments.
KW - Autonomous underwater vehicle
KW - Biomimetic manta robot
KW - Depth control
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85149893062&partnerID=8YFLogxK
U2 - 10.1007/978-981-99-0617-8_5
DO - 10.1007/978-981-99-0617-8_5
M3 - 会议稿件
AN - SCOPUS:85149893062
SN - 9789819906161
T3 - Communications in Computer and Information Science
SP - 59
EP - 69
BT - Cognitive Systems and Information Processing - 7th International Conference, ICCSIP 2022, Revised Selected Papers
A2 - Sun, Fuchun
A2 - Cangelosi, Angelo
A2 - Zhang, Jianwei
A2 - Yu, Yuanlong
A2 - Liu, Huaping
A2 - Fang, Bin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 7th International Conference on Cognitive Systems and Information Processing, ICCSIP 2022
Y2 - 17 December 2022 through 18 December 2022
ER -