Research On Path Planning Of Mobile Robot Based On Improved A* Algorithm Fusion Dynamic Window Method

As the application scenarios of mobile robots become more and more complex,people have put forward higher requirements for the obstacle avoidance capabilities of mobile robots.In order to allow the mobile robot to plan the collision-free optimal path from the starting point to the target point in a complex environment,this paper uses lidar as a sensor to conduct research from three aspects: environment map construction,positioning and path planning.In the stage of building the environment map,the Gmapping algorithm is used to combine the information collected by the odometer and lidar to enable the robot to accurately estimate its own pose and create a high-precision grid map.The simulation results show that this method can clearly show the distribution of obstacles in the environment,and can provide accurate environmental maps for subsequent path planning experiments.In the global positioning stage,the adaptive Monte Carlo positioning algorithm is used.The algorithm is scattered all over the map with particles.According to the observation information of the surrounding environment by lidar,the incorrect particles are gradually eliminated,leaving particles with real poses,so as to realize the global positioning of the robot.Simulation experiments show that as the robot continues to move,the algorithm can converge the robot’s pose to a small range,and realize the robot’s global positioning on a known map.In the path planning stage,a random obstacle avoidance method for robots that combines the improved A* algorithm with the dynamic window method is proposed.In the improved A* algorithm,the search point selection strategy and the evaluation function are optimized to improve the search efficiency of the A* algorithm;then the redundant point deletion strategy is proposed to eliminate the redundant nodes in the path,reducing the path length,and the dynamic window method is used for the local planning between every two adjacent nodes to ensure that on the basis of the global optimal path,random obstacle avoidance is achieved in real time,so that the robot can reach the target point successfully.The simulation experiment results show that the improved A* algorithm proposed in this paper can reduce the path length by 4.39% and the calculation time by 65.56% on average compared with the traditional A*algorithm.After fusing the dynamic window method,on the global path basis the local path can be modified to achieve random obstacle avoidance.Finally,algorithm implementation and system integration were carried out on the autonomously built robot platform to verify the feasibility and effectiveness of the map construction algorithm,positioning algorithm and path planning algorithm.The experimental results show that the mobile robot built in this paper can move from the starting point to the target point without collision under the condition that the environment map remains unchanged,the environment map obstacles are reduced,and the environment map has random obstacles.The theoretical analysis of the scheme is consistent with the experimental verification results,which proves the correctness of the scheme design.This system has certain practical significance,and has certain reference value for the research of mobile robots in path planning.