In view of the vulnerability of ocean unmanned sailboats to the large lateral velocities due to wind and waves during navigation, this paper proposes a Gaussian Process Model Predictive Control (GPMPC) method based on data-driven learning technique to improve the navigation tracking accuracy of unmanned sailboats. The feature model of the sailing course change subject to the wind and waves is learned from the efficient sampling data. It is then combined with the model predictive control to form the course controller. To reduce the influence of wind and waves disturbances, an adaptive weight term is designed in the object function to improve the tracking accuracy of the model predictive control. The guidance commands received by the model predictive controller take into account the path deviation caused by the current and lateral motion of the ship. The results show that GPMPC has the advantages of fast response time and less overshoot; the unmanned sailboat can better achieve waypoint tracking by learning navigation data.
Path planning is the precondition for Hybrid Autonomous Underwater Vehicles (HAUV) to enter the submerged area to undertake a mission. The influence of ocean currents on HAUV should be further investigated to obtain a time-optimal path. The improved A* algorithm and the neural network model are employed in this paper to plan a time-optimal path for the vehicle. The HAUV in glider mode is capable of traveling forward mainly through the zigzag motion in vertical plane. Since the vehicle can only receive the command orders when it surfaces from the water, the path is expected to include a series of discrete waypoints in the water surface. At the same time, the presence of submerged riverbeds is also taken into account to avoid hazards for HAUVs when it navigates in the water. It can be demonstrated that ocean currents can be used to decrease the operating time. The comparison results of the two methods verify that the size of the map affects the calculation time. In addition, the neural node represented method surpasses the modified A* method, especially when the map is too large.
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