Research On Dynamic Path Planning Fusion Algorithm For Mobile Robots

With the development of computer technology and the rise of unmanned topics,mobile robotics has seen rapid development.Path planning,as one of the important topics in the field of mobile robotics research,has been widely used in smart homes,unmanned ports,underwater exploration and other fields in recent years,thus requiring higher capability for mobile robot path planning.In this paper,we optimize the standard A* algorithm and apply the optimized A* algorithm for global path planning,and combine the global path planning data with the optimized potential field method for local path planning.The fusion algorithm proposed in this paper complements the advantages of both algorithms to effectively accomplish the path planning task of mobile robots.The research in this paper has three main parts:(1)The path planning principle and algorithm structure of the standard A*algorithm are studied in depth.In response to the problem that the standard A*algorithm will calculate a large number of useless nodes in path planning,resulting in low path planning efficiency and poor possibility of the mobile robot due to the close proximity of the planned path to obstacles,we propose to apply the contour map principle combined with the raster map to build the environment model,and map the height corresponding to each node On the basis of this,the structure of A* algorithm is improved,and the standard A* algorithm uses evaluation function to calculate the optimal child nodes instead of using the node corresponding height data for screening,and the standard A* algorithm evaluation function is used as a supplement.Through simulation comparison experiments,the optimized A* algorithm effectively reduces the calculation of useless nodes,and the final obtained path is farther from the obstacle,which proves the effectiveness and robustness of the optimized A* algorithm.(2)For the problem of unreachable target point and easy to fall into local optimum of standard potential field method,this paper proposes an interval point search strategy based on the path planning result of optimized A* algorithm to solve the local optimum problem,and proposes a range judgment strategy for the problem of unreachable target point,and finally adds the least squares method to fit the path.The optimized potential field method is simulated and analyzed by setting up different static obstacle experimental environments and dynamic experimental environments.The experimental results show that the fusion algorithm combining the optimized A* algorithm and the optimized potential field method effectively solves the problems existing in the path planning of the standard potential field method,and effectively improves the performance of the mobile robot.(3)This paper builds a mobile robot platform based on the ROS platform,sets up a static obstacle and dynamic obstacle environment,calibrates the acceleration and angular velocity parameters of the mobile robot according to the actual environment,and uses the ROS experimental platform to scan and model the environment.The experimental results show that the fusion algorithm based on the optimized A*algorithm and the optimized potential field method is effective in avoiding static and dynamic obstacles,which proves the practicality of the fusion algorithm.