| In recent years,mobile robots have increasingly emerged in people’s vision,such as cleaning mobile robots for home use,logistics and transportation robots,and medical service robots.Their key technologies cannot be separated from path planning of mobile robots.Nowadays,with the diversification of application scenarios,many mobile robots often encounter dynamic obstacles such as pedestrians and various uncertain complex factors in the path planning process.Therefore,studying an algorithm that can plan high-quality routes in complex environments has important practical application value.This paper mainly focuses on the path planning algorithm and obstacle avoidance function of mobile robots for dynamic obstacles in the environment.Due to the low efficiency and multiple inflection points of traditional A * path planning algorithms,which lead to long calculation time and irregular routes,this paper proposes a path planning algorithm that combines the improved A * algorithm with the dynamic window method.The fusion algorithm can accurately avoid obstacles while optimizing the route.The reliability and effectiveness of the proposed algorithm are verified through simulation experiments and actual mobile robot testing.The main research content of this article is as follows:(1)Construction of mobile robot platform and map construction.A mobile robot platform was built,and a two-dimensional grid map was constructed using the SLAM map positioning and construction method as the experimental basis for subsequent path planning.Build a threedimensional space environment on a ROS based robot operating system,and conduct experimental tests on the SLAM algorithm.(2)Research on improved A* global path planning algorithm.Due to the shortcomings of traditional A* path planning algorithms such as a large number of search nodes,low efficiency,and consuming computational resources.This paper proposes an improved A* path planning algorithm,which is based on the jump point search algorithm JPS(Jump Point Search)to remove redundant nodes;The gradient descent method is used to reduce the number of inflection points.In order to verify the effectiveness of its algorithm,the improved A* algorithm is compared and analyzed with the original algorithm through Matlab simulation software,and compared with other global path planning algorithms.Finally,virtual simulation experiments were conducted on a ROS based robot operating system,and the experimental results achieved the expected results.(3)Improve the fusion of A* global path algorithm and local path algorithm.Aiming at the problem of whether mobile robots can accurately avoid obstacles in complex environments,this paper analyzes the performance of two local path algorithms,Dynamic Window Approach(DWA)and Reinforcement Learning,and combines the dynamic window method with the improved A*global path planning algorithm.In order to verify the effectiveness and progressiveness of the fusion algorithm,simulation experiments in different environments are carried out on the ROS based system,and finally the authenticity and effectiveness of the algorithm are verified.(4)Experimental verification of path planning.By building a good mobile robot platform,the algorithm proposed in this article is implanted into the mobile robot navigation function package.Build static and dynamic environments in the real environment to test and verify.Through the experiments of mobile robots in two environments,the experimental results show that the path planning algorithm proposed in this paper has good progressiveness and real-time performance in terms of route smoothness and obstacle avoidance. |
