Multi-AUV Inspection for Process Monitoring of Underwater Oil Transportation
Jingyi He, Jiabao Wen, Shuai Xiao, Jiachen Yang
Abstract
Dear Editor, This letter presents an inspection method for process monitoring of underwater oil transportation via multiple autonomous underwater vehicles (AUV). To improve the adaptability of our method in practice, we introduce the dynamic complex ocean current data to the previously mentioned case by using regional ocean modeling system (ROMS) for the first time, and elaborately design AUV's local information, as well as the deep reinforcement learning (DRL) tuple <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$(S_{t}, A_{t}, R_{t}, S_{t+l})$</tex> . Specifically, according to the local information of each AUV, including captured pipeline image, ocean current velocity, position coordinates and pose angles, an agent can be well trained to control the AUV for underwater oil pipeline tracking through vision-hydrology-motion-based soft actor critic (VHM-SAC) mechanism. Experimental results show that the proposed method can provide stable oil pipeline tracking performance in dynamic complex underwater environment.