Research On AUV Path Planning Algorithm Tor Offshore Cage Culture Area

Offshore cage culture has become the main development trend of mariculture industry in the future.However,the offshore cage culture area covers a wide area and is far from the shore.It is difficult to meet the needs of modern large-scale farming by relying on traditional manual methods in the management of seawater sampling and testing.In response to this problem,it is proposed to assist the management of offshore cage culture areas with the aid of Autonomous Underwater Vehicle(AUV),and to implement timed autonomous cruise monitoring by equipped with water quality detection sensors and wireless communication systems.The AUV path planning algorithms are mainly studied in this paper.The specific research contents and innovations are as follows:1.After analyzing the demand for the offshore cage culture area,establish the TSP(Traveling Salesman Problem)model,set the cruise point and formulate the classification cruise strategy,and equate the shortest cruising path problem of the cage culture area with the TSP problem.The deficiencies of the ant colony algorithm are analyzed,and the problems of initial pheromone deficiency and easy to fall into local optimum are improved.Based on the identification and classification of detection data,a classification cruise strategy based on historical data is proposed,and the improved ant colony algorithm and cruise strategy are used to simulate the simulated cage culture environment.The effectiveness of the improved ant colony algorithm is verified by comparing its shortest cruising path obtained with the traditional ant colony algorithms.2.Using the improved A*algorithm to complete the static path planning of the AUV in the culture area.The environment of the offshore cage culture area by coordinate transformation and grid method are modeled,and simulated by the traditional A*algorithm.Aiming at the problems of too many turning points,too large turning angle and not optimum path in traditional A*algorithm,the extended neighborhood A*algorithm and the second smoothing method are adopted to improve the traditional A*algorithm.The simulation verifies that the improved A*algorithm path is shorter and smoother.3.The improved artificial potential field method is used to solve the dynamic path planning problem of AUV.The principle of artificial potential field method is summarized and the potential field model is established.The limitations of traditional artificial potential field method are analyzed.By improving the repulsive potential field function and using the"random angle migration method",the problems of the traditional artificial potential field method such as the inaccessibility of the target,the inability to escape the local minimum and the inadaptability to avoid obstacles in motion are solved.The simulation results show that the improved artificial potential field method is more suitable for dynamic path planning.