Research On Path Planning Of Mobile Robot Based On Improved Sampling Algorithm

With the rapid development of science and technology,various types of mobile robots are widely used in human daily life and production,and path planning is a key indicator that affects the performance of mobile robots,and it is also one of the key technologies in robotics research.In complex environments,traditional path planning algorithms often have some shortcomings that prevent the robot from successfully completing the established tasks,aiming at this problem,based on the MATLAB simulation environment,this paper research the problems of the RRT-Connect and RRT* two typical algorithms in the sampling algorithm in the path planning of mobile robots,and proposes corresponding improvement strategies.The main research contents are as follows:This article first introduces the research background and significance of the path planning of mobile robots,make an overview of domestic and foreign development situation of mobile robot and the current state of path planning algorithms.based on the comparative analysis of various algorithms,RRTConnect and RRT* in the sampling algorithm are selected for improvement research.For the RRT-Connect algorithm,the new node is introduced to re-select the father node considering the ancestor point,and the triangle inequality principle is used to optimize the part of the path length,and the processing is improved by setting the corner constraint,dynamic step strategy,connection processing,etc,the simulation results show that the improved algorithm not only reduces the length of the planned path,but also shortens the convergence time.One-way expansion of random trees is the basis for two-way expansion and improvement,so an improved one-way RRT* algorithm is proposed.for the original RRT*,the target bias sampling strategy is used to speed up the algorithm convergence speed,and then the target gravity is used for the secondary bias,and pruning and smoothing are performed,so that the improved algorithm planning time is not increased,and the path quality is more obvious improvement.The purpose of the improvement of the two algorithms is to make the algorithm planning path quality better,and take into account the consideration of convergence speed,the two different improvement methods have certain reference significance for the optimization ideas of the sampling algorithm.Finally,aiming at the problems of mobile robots in complex environments,a hybrid algorithm combining improved RRT* and improved artificial potential field method is proposed.First use the improved RRT* to plan the global initial path,set the global path node as the sub-goal,and then use the improved artificial potential field method to make the robot pass through the sub-goals in turn,and the improved artificial potential field is corrected by the repulsion potential field function and the relative velocity is introduced.improvements such as the repulsive force field,setting virtual sub-targets to escape,etc.,it can avoid the dynamic obstacles while overcoming the traditional shortcomings.Through the fusion of the two algorithms,the robot's path is relatively smooth and can effectively avoid dynamic obstacles.