Research On AUV Path Planning And Trajectory Tracking In Complex Ocean Current Environmen

Autonomous Underwater Vehicles(AUVs)have received significant attention in recent years.They have promising commercial,scientific,and military applications for subsea rescue,data collection,monitoring marine pollution,mapping,and more.However,AUV navigation in complex underwater environments is affected by factors such as ocean currents,underwater topography,and time-varying obstacles,which place greater demands on the AUV’s ability to plan and control itself.This paper investigates global path planning for AUVs in complex ocean currents,local path planning considering time-varying obstacles,and 3D trajectory tracking control.The details of the research are as follows:Firstly,the global path planning method for AUVs in complex underwater environments is investigated.Two-dimensional and three-dimensional underwater ocean current and terrain models construct the complex aquatic simulation environment.A fusion of the Sequential Quadratic Programming(SQP)algorithm and Genetic Algorithm(GA)is proposed to implement a GA-SQP algorithm for global path planning of AUVs in complex underwater environments.This method has the strong robustness and global optimality of the GA method,while effectively retaining the advantages of the SQP algorithm in terms of convergence speed and computational efficiency.Simulation results show that the GA-SQP algorithm can plan the shortest path for the AUV’s navigation distance in complex underwater environments,while satisfying the underwater safety constraints and minimum energy loss requirements.Secondly,the local path planning method for AUVs considering time-varying obstacles is investigated.Traditional Model Predictive Control(MPC)algorithms have fixed model parameters and cannot adapt to time-varying obstacle planning scenarios.To solve this difficulty,this paper uses Recursive Least Squares(RLS)to design an Adaptive Model Predictive Control(AMPC)based on the obtained global paths,and a local obstacle avoidance path planning method based on the obstacle avoidance function and adaptive MPC.When an obstacle is detected,the movement of the obstacle is predicted for a future period,and the path is re-planned during the course of the voyage.When this local planning is completed,the global path is returned until the next obstacle is encountered and local planning is re-executed.Simulation results show that the AMPC method can overcome the disadvantages of poor adaptiveness of traditional MPC,enabling the AUV to avoid real-time obstacles during navigation.Finally,the tracking control method for the feasible obstacle avoidance trajectory of the AUV is investigated.An Adaptive Second Order Terminal Sliding Mode Control(ASOTSMC)method is proposed to address the effects of external time-varying disturbances and model parameter uncertainties.The method treats external time-varying disturbances and parameter uncertainties as concentrated disturbances and designs an adaptive disturbance observer to achieve accurate observation of the model parameters,while a second-order terminal sliding-mode controller is designed to eliminate the jittering phenomenon in conventional sliding-mode control,and the formulation is rigorously analyzed and proved by Lyapunov stability theory.Simulation results show that the ASOTSMC method has good stability and robustness.Ultimately,the underwater robot’s hardware and software are developed,written,and physically verified in an experimental field to further validate the effectiveness of the proposed method in this paper.