| In recent years,the deep integration of computer,control and other disciplines has promoted the development and improvement of intelligent robotics.Mobile robots have been widely used in logistics,catering and medical industries,and their movement technologies have received extensive attention from researchers,resulting in a number of useful research results.However,for the mobile robot trajectory planning and trajectory tracking control problems,there are still problems such as slow motion speed and low tracking accuracy.These problems are solved by improving the robot mechanical structure and power device and increasing the mechanical efficiency to enhance the speed,while improving the robot motion accuracy by increasing the sensor accuracy and enhancing the computer computing performance.The optimization of trajectory planning and control algorithms is an effective way to achieve the above goals,and at the same time can reduce the development cost.This thesis studies the four-wheeled wheeled mobile robot with Ackermann steering structure,and the specific work is as follows.Firstly,the software and hardware platform of the mobile robot studied in this paper is introduced,as well as the basic functions to realize the robot’s map building,navigation and movement.In order to plan feasible trajectories in the environmental map and improve the trajectory tracking accuracy,the robot system is mathematically modeled.This includes building a kinematic model under nonholonomic constraints,as well as building dynamics models for the steering subsystem(servo)and the power subsystem(motor),respectively.The complete motor model is obtained through experimental identification.Secondly,a hybrid trajectory planning algorithm based on global planning algorithm and local planning algorithm is implemented.The global planning adopts A* algorithm and Dijkstra algorithm respectively,and the experimental comparison shows that both shortest path generation effects are comparable,but the A* algorithm is more efficient.Considering the feasibility of the mobile robot along the shortest path and achieving dynamic obstacle avoidance,the TEB local planning algorithm is introduced to modify the shortest path.the TEB algorithm constructs the constraints such as the maximum velocity and angular velocity of the mobile robot,the minimum turning radius,and the distance to obstacles as cost functions,forming a weighted multi-objective optimization problem,and solves it by using the hypergraph theory and graph optimization algorithm.Thirdly,in order to improve the accuracy of the actual trajectory tracking of the robot,a trajectory tracking control algorithm based on a dual closed-loop model is proposed.The outer-loop controller is designed based on Lyapunov function method,the controller input is the reference trajectory,the output is the virtual velocity and angle control law,and it is used as the reference input of the inner-loop controller;the steering subsystem design in the inner-loop controller uses the idea of sliding mode variable structure control,and the power subsystem uses the discrete system controller design method based on the attraction law.The effectiveness of the above control algorithm is verified by stability analysis proof and simulation experiments.Finally,the effectiveness of the hybrid trajectory planning algorithm is verified by physical simulation and physical experiments in the ROS-based mobile robot platform,and the effectiveness of the dual closed-loop trajectory tracking control algorithm is verified in the physical platform and in the real environment,respectively.The results of the whole paper are summarized and summarized,and shortcomings and outlooks are presented.There are 54 figures,2 tables,and 52 references. |
