Research On Local Path Planning And Motion Control Algorithms For Unmanned Surface Vehicles

As an autonomous waterborne platform,unmanned surface vehicles play a vital role in marine monitoring,environmental protection,and emergency rescue.Since different missions will deploy unmanned surface vehicles to different environments,effective and reliable navigation technology is required to guide surface vehicles.A complete navigation system is usually divided into mapping,planning,and control,and it is necessary to design corresponding algorithms for each module and establish the connection between the modules.Among them,the local path planning algorithm and motion control algorithm are important components of the autonomous navigation system,which directly determine the navigation performance.In order to further improve the autonomous operation capability of surface vehicles,this dissertation focuses on the local path planning and motion control algorithms of unmanned surface vehicles,and conducts simulation and physical experiments.Specifically,the main research contents of this dissertation are as follows.1.This dissertation proposed a local path planning algorithm for surface vehicles based on deep reinforcement learning,and designs a dynamics-free environment for training deep reinforcement learning models,which can reduce the convergence time while avoiding the tediousness of modeling waterborne environments.In addition,for the sim-to-real problem,domain randomization is introduced to environmental information to improve the generalizability and transferability.However,training in complex and variable scenarios will cause convergence difficult.Therefore,this dissertation designs adaptive curriculum learning to accelerate the convergence of the neural network.Also,a consistency strategy is proposed to improve the feasibility and trajectory smoothness of navigation commands by considering stability and actuator constraints.2.In this dissertation,we model the kinematics and dynamics of the surface vehicle,and design two controllers for surface vehicles,the conventional algorithm based on adaptive control and the intelligent algorithm based on reinforcement learning,respectively.Then we compare the performance of the two controllers in following different input signals.3.In order to evaluate the effectiveness of the proposed method,extensive experiments are conducted in simulation environments to demonstrate the superiority of the proposed method in terms of convergence speed,generalization performance,robustness,and trajectory smoothness.Further,the autonomous navigation system is developed on a physical platform for testing,and through qualitative and quantitative analysis,it is verified that our method can be directly deployed in a real surface vehicle without additional tuning as well as training and has a shorter navigation path as well as better trajectory smoothness compared to the artificial potential field method.