A Modified ALOS Method of Path Tracking for AUVs with Reinforcement Learning Accelerated by Dynamic Data-Driven AUV Model

Path tracking has a significant impact on the success of long-term autonomous underwater vehicle (AUV) missions in terms of safety, energy-saving, and efficiency. However, it is a challenging problem due to the model uncertainty, and ocean current disturbance. Moreover, the widely used line of sight...

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Vydáno v:	Journal of intelligent & robotic systems Ročník 104; číslo 3; s. 49
Hlavní autoři:	Wang, Dianrui, He, Bo, Shen, Yue, Li, Guangliang, Chen, Guanzhong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Dordrecht Springer Netherlands 01.03.2022 Springer Springer Nature B.V
Témata:	Algorithms Analysis Artificial Intelligence Autonomous underwater vehicles Control Distance learning Dynamic models Electrical Engineering Energy conservation Engineering Error analysis Line of sight Machine learning Mechanical Engineering Mechatronics Methods Neural networks Ocean currents Path tracking Performance enhancement Remote submersibles Robotics Short Paper Topical collection on Unmanned Systems Tracking errors Path tracking Line-of-sight (LOS) Data-driven (DD) Deterministic policy gradient (DPG) Autonomous underwater vehicle (AUV) Reinforcement learning (RL) Long short-term memory (LSTM)
ISSN:	0921-0296, 1573-0409
On-line přístup:	Získat plný text
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