Research On Path Planning Of Mobile Robot Based On Tree Growth Simulation Algorithm

In recent years,thanks to the joint efforts of computers,sensors,and artificial intelligence,research on robotics has made tremendous progress.Mobile robots have been widely used in various fields such as industrial production,public services,space exploration and military operations.With the continuous development of human society,people have higher expectations for a new generation of mobile robots.How to make mobile robots meet people's needs is the main direction for the development of robots.The first problem to be solved is the path planning problem of mobile robots.In an environment with obstacles,mobile robots can still quickly and safely find an optimal smooth path from the starting position to the target position,which is a common goal for all robotic researchers.Therefore,the research of path planning of mobile robot is of great significance.To address the problem of path planning of mobile robots,this paper focuses on three basic path planning problems of mobile robot environment map modeling theory,path optimization algorithm and path smoothing method and aims to make mobile robots safely and effectively bypass static obstacles in space environment and plan an optimal path to reach the target location.Firstly,when it comes to the environment modeling of mobile robot,Honeycomb Grid Map(HGM)is used to model the environment map of mobile robot.That is,the environment map is divided into hexagonal grid of the same size,which is compared with the traditional grid method.It is proved by mathematical analysis and simulation verification that,when the obstacle is bypassed in the honeycomb grid map,the steering angle of the mobile robot becomes smaller and the path ratio is reduced,which mitigates the collision problem caused by the inertia of the mobile robot to a certain extent.Besides,the walking distance is reduced while bypassing obstacles.Secondly,when it comes to the path search of mobile robot,a branch growth model is established according to the principle of tree-to-photo growth and a Tree Growth Simulation Algorithm(TGSA)is used for global traversal path optimization,which can effectively bypass obstacles and search out the optimal path.Through simulation and comparison experiments,our modal is compared with the ant colony algorithm in the same environment.It can be found that the convergence of the algorithm and the path length are optimized.Finally,as far as the path smoothing of mobile robot is concerned,a third-order Bezier curve is used to smooth the optimal walking path searched out by the mobile robot.A path selection mechanism is established according to whether the smoothed path passes through the obstacle,and finally a relatively optimal smooth path is selected.Compared to the polyline walking path searched out by the mobile robot,its smoothness has increased,further increasing the walking safety of the mobile robot.Finally,the mobile robot hardware simulation experiment platform is built and used to simulate and verify the optimal path for mobile robot.The simulation results are compared with the verification results of the mobile robot experimental platform.The results of the simulation experiment and the test results of the hardware platform are used to verify the feasibility of the relevant theoretical algorithms.The experimental results also verify that the mobile robot can complete the path planning safely and effectively in the static obstacle environment.