Design And Implementation Of Autonomous Navigation Unmanned Vehicle In Complex Water Environment

With the continuous advancement of technology and increasing application demands,the prospects of research and application of autonomous navigation unmanned surface vehicles are extremely broad.In-depth exploration of the relevant technologies and applications of autonomous navigation unmanned surface vehicles not only has important theoretical research significance but also has extensive social and economic value.However,the research on autonomous navigation unmanned surface vehicles faces many challenges and difficulties,such as complex marine environments,unstable maritime communications,and high equipment and vessel costs.This thesis describes a path planning algorithm based on multi-path search and risk assessment to ensure that unmanned surface vehicles can autonomously navigate safely and efficiently in obstacle-dense complex water environments.In addition,this thesis utilizes an improved particle swarm algorithm based on differential evolution to optimize the path tracking strategy of the Active Disturbance Rejection Controller,thereby achieving precise control of unmanned surface vehicles.The main research contents of the thesis are as follows:First,the requirements of design and implementation of autonomous navigation unmanned surface vehicle are analyzed,and the overall design of autonomous navigation unmanned surface vehicle is carried out according to the practical application requirements,performance requirements and technical limitations,which provides guidance and framework for the development of unmanned surface vehicle system.Secondly,the navigation environment model of unmanned surface vehicle is established,and the traditional A* algorithm is searched and optimized.The global optimal path is determined by using multi-path search algorithm and risk assessment function,and the effectiveness of the algorithm is determined by simulation.Thirdly,the kinematic model of the unmanned surface vehicle is established,and the method of combining the mutation of differential evolution algorithm with the idea of particle swarm optimization is studied.Based on the idea of tracking the population type and the optimal particle of samples,the parameters of the active disturbance rejection controller are globally optimized.Simulation results show that the performance of the improved algorithm is better than that of the traditional algorithm.Fourth,the software and hardware design of the autonomous navigation unmanned surface vehicle is completed,and the navigation control system and platform control system of the unmanned surface vehicle are designed to realize the autonomous navigation function of the unmanned surface vehicle.An information management platform and a remote command plane are also designed to realize the data transmission and motion control functions of the unmanned surface vehicle.The experimental results show that the autonomous navigation unmanned surface vehicle has accurate positioning,safe and efficient path planning,accurate path tracking,and the maximum absolute error of the straight path is 0.2 m.The information management platform and remote command aircraft can effectively carry out data acquisition and functional control of the unmanned surface vehicle.