Research On Key Technologies Of Robot Path Planning And Path Tracking

Autonomous mobile robot is an important part of robot research,and the related theory and technology has been a research focus in robotics.when carrying out the task in the practical application of the process,which involves a series of key technologies,the autonomous navigation is the primary problem to be solved,it includes mainly two aspects: path planning and path tracking.;On the existing research results,the rationality of path planning,the safety and reliability of obstacle avoidance,the smoothness of path and the real-time of path tracking control And the accuracy and so there is still much room for improvement.In view of the existing research results,there is still much more to be done for improvement in the rationality of path planning,the safety and reliability of obstacle avoidance,the smoothness of the path and the real-time and accuracy in path tracking control.Therefore,the article mainly takes the typical wheeled mobile robot as the subject,and makes a further exploration and Research on the path planning and path tracking problems.The path planning of mobile robot is the process of establishing the model to solve the optimal path for the robot's scheduled tasks in the actual environment,which directly determines whether the scheduled tasks successfully or not.For robot path planning,according to the grasp of the environment,it can be divided into global path planning and local path planning in unknown environment.The article focuses on the global path planning in the context of global map.In view of the typical global path planning algorithm artificial potential field(Artificial Potential Field,APF)for path planning,there will be some problems such as local oscillation,unreachable target,poor smoothness of path and so on,In this paper,Firefly Algorithm(FA)is proposed,which combines the firefly algorithm with the traditional artificial potential field method to optimize the global path planning(FA-APF).Firstly,according to the known global map,the artificial potential field method,which is easy to describe and easy to program,is used to initialize the parameters of the firefly algorithm,and then the polar coordinatesystem is used to instead of the Cartesian coordinate system to solve the planning path by using the firefly algorithm.The polar coordinates method will be automatically discard the redundant points on the planning path,and enhance the smoothness of the planned path;at the same time,considering the shortcomings of the traditional firefly algorithm,in this paper,the firefly algorithm is improved:the adaptive step size is introduced to improve the random step size,and the convergence of the algorithm is accelerated,and the chaotic logic is used to improve the absorption coefficient of the firefly algorithm,avoiding late maturity,to a certain extent,the problem of local oscillation and target unreachable in the artificial potential field method is solved.The simulation and experimental results show that the path planning algorithm which is proposed in this paper combining the improvement firefly algorithm and artificial potential field method(IFA-APF)will effectively improve the efficiency of path planning,the planning of safety and reliability,rationality and smoothness of the path was significantly improved,which is more close to the ideal path.After obtaining the well-planned global optimal path,the key problem to be solved is how to control the moving path of the mobile robot along the planned path,that is,the path tracking problem,the path tracking control performance is directly related to the robot autonomous navigation problem to be effectively resolved or not.In this paper,the typical wheeled mobile robot is taken as the research object,and its kinematics and dynamics model is established;the path following control law is designed by using Back-Stepping;considering the low precision of the conventional PID control,control and adjustment process is longer,the introduction of CMAC neural network(Cerebellar Model Articulation Controller,CMAC),CMAC neural network is designed with PID composite(CMAC-PID)controller for path tracking problem of wheeled robot.The feedforward control of CMAC neural network can greatly improve the stability of path following control and effectively restrain the interference.but the traditional CMAC neural network internal recursive learning algorithm approximate minimum deviation model has certain deficiencies in the local minimum convergence rate slow,shaped in the search gradient likely todecline,which fell not direct path,To this end,this paper introduces a new,simple algorithm of intelligent optimization algorithm-intelligent Algorithm algorithm(Intelligent Water Drops,IWD)to guide the learning process of the neural network,On the basis of increasing the calculation amount of the control algorithm,the accuracy and real-time performance of the robot path tracking control are improved to some extent.The simulation and experimental results show that CMAC-PID composite control algorithm drop algorithm proposed in this paper for the intelligent mobile robot path tracking adjustment in less time,higher tracking accuracy and the stability of the algorithm is enhanced.The article chose a specific scene in the laboratory,building a wheeled mobile robot path planning and path tracking experiment platform of path planning algorithm is proposed in this paper and the path tracking control algorithm to carry out the experimental study on the performance of the algorithm is verified by experiments fully,which also provides a reference for the related theoretical research to practical application.