Path Planning And Tracking For AUV In Large-scale Environment

Autonomous Underwater Vehicle (AUV) is a kind of important underwater equipment with certain autonomous navigation and programming capacity, which usually can active in very dangerous or inaccessible sea area of manpower, greatly expanding the human capacity for underwater work and detection range. As one of key technology for intelligent underwater vehicle, path planning and path tracking means that AUV can find an optimal path from starting position to the end in the environment with obstacles while meet some optimum conditions. Thus, path planning and path tracking ability is the key to achieve autonomous navigation for autonomous underwater vehicle.This paper researches two key technology of AUV independent navigation—the path planning and tracking, and lays the foundation for achieving autonomous navigation.Firstly, this paper introduces the research status on AUV at home and abroad, application prospects, development trends, summary of the path planning and tracking technology and research significance of this task. Secondly, this paper discusses detailedly the theory of using Fast Marching Method for path planning and simulation result, modeling and realization of the path tracking algorithm. Then according to a large scale static unknown or partially unknown environment, this paper proposes a new path planning method which dynamically divides the global environment and uses multiple local optimizations instead of one-time global optimization. The main idea behind this method is to make full use of the local real-time environment information, partition global working environment dynamically and optimize each local area using Fast Marching algorithm (FM), finally generate a global quasi-optimal path. On this basis, according to the kinematics and dynamic model of AUV, we traced on an arbitrary path based on the PID controller AUV tracking technology in Simulink. The MATLAB simulation result proves that the path planning method is known to be simple implementation, real-time, efficient for finding cost optimal path, and meets the experimental requirement. Path tracing simulation results shows the method is fast, accurate and global stable properties. Lastly, this paper focuses on the application of the algorithm and achieves it in VC.