Path Planning Of Mobile Robot Based On RRT* Algorithm In Dynamic Environment

Nowadays,with the continuous development of robot technology,the field of mobile robot is growing,but in practical application,seeking better path planning and obstacle avoidance method is still one of the hot issues of robot research.Based on the analysis and comparison of the existing path planning methods,this paper chooses RRT * algorithm with strong randomness,flexible use and good dynamic performance to study the path planning problem in dynamic environment.Based on the analysis of RRT *algorithm and the principle of various improved algorithms,aiming at the obvious problems existing in the application of RRT * algorithm,such as the sharp increase of computation in uniform random sampling,the inconsistency of multiple path planning results in the same environment and so on,the idea of combining the target bias sampling and path caching is proposed to limit the sampling interval of the algorithm and improve the performance at the same time In order to reduce the number of nodes used in the sampling,the dynamic performance and real-time performance of the algorithm are improved.In this paper,a path planning strategy based on RRT * algorithm is proposed.Firstly,the initial path planning is carried out in a static environment.In order to reduce the randomness of RRT * algorithm,the target bias sampling is introduced for operation.Then,the path is applied in the dynamic environment,and the initially planned path is used as the path cache.When new obstacles appear in the path,the invalid path is trimmed and the path is replanning.At this time,when sampling nodes,there is a certain probability to select the points in the path cache as the new nodes,so that the new path maintains the trend of the original path to a greater extent.Then,MATLAB is used to simulate the initial planning and re planning in three typical environments.Compared with the traditional RRT * algorithm,in sparse environment,the number of nodes is reduced by 42.5%,the path length is reduced by 58.79%,and the operation time is reduced by13.71%;in narrow environment,the number of nodes is reduced by 37.4%,and the path length is reduced by 13.71% In the concave environment,the number of nodes is reduced by 49.7%,the path length is reduced by 65.83%,and the operation time is reduced by 8.73%.The simulation results verify the effectiveness of the new improved algorithm.Finally,the improved algorithm is transplanted to the REVV-B32 crawler mobile robot to do experiments.After comparative analysis,the experimental results show that the improved algorithm is effective.The experimental and simulation results show that the proposed improvement strategy improves the dynamic performance of the algorithm to some extent,improves the efficiency of node utilization,and has some practical value in engineering.