A Control Algorithm for Sea–Air Cooperative Observation Tasks Based on a Data-Driven Algorithm

• Published in: Journal of marine science and engineering 2021-11, Vol.9 (11), p.1189
• Main Authors: Hu, Kai, Chen, Xu, Xia, Qingfeng, Jin, Junlan, Weng, Liguo
• Format: Article
• Language: English
• Subjects: Algorithms; Autonomous underwater vehicles; Control algorithms; Control theory; Cooperative control; data-driven; Decision making; Deep learning; deep reinforcement learning; Distance learning; Drone aircraft; Isotherms; Marine environment; Markov analysis; Motion control; multi-agent collaboration; Multiagent systems; Ocean circulation; Reagents; sea and air observation; Stability; Surface vehicles; Unknown environments; Unmanned aerial vehicles; Unmanned vehicles
• ISSN: 2077-1312; 2077-1312
• DOI: 10.3390/jmse9111189

There is tremendous demand for marine environmental observation, which requires the development of a multi-agent cooperative observation algorithm to guide Unmanned Surface Vehicles (USVs) and Unmanned Aerial Vehicles (UAVs) to observe isotherm data of the mesoscale vortex. The task include two steps: firstly, USVs search out the isotherm, navigate independently along the isotherm, and collect marine data; secondly, a UAV takes off, and in its one round trip, the UAV and USVs jointly perform the task of the UAV reading the observation data from USVs. In this paper, aiming at the first problem of the USV following the isotherm in an unknown environment, a data-driven Deep Deterministic Policy Gradient (DDPG) control algorithm is designed that allows USVs to navigate independently along isotherms in unknown environments. In addition, a hybrid cooperative control algorithm based on a multi-agent DDPG is adopted to solve the second problem, which enables USVs and a UAV to complete data reading tasks with the shortest flight distance of the UAV. The experimental simulation results show that the trained system can complete this tas, with good stability and accuracy.