Robot Path Panning Based On Geometry Algorithm And Genetic Algorithm

For mobile robots,moving in a space environment and accomplishing more advanced tasks,planning the moving path is the most basic function,which is the path planning problem in robotics research.Path planning is a key issue for mobile robot navigation systems.First of all,mobile robots need to independently complete path planning,obstacle avoidance and higher-level tasks,and need to calculate their positions in the external world in real time,that is,global coordinates and posture.The researchers used a variety of positioning sensors,has developed a number of positioning methods.In this paper,a camera-based robot localization technology is proposed,which uses robotic cameras to capture images and extract the robot's own motion parameters through feature extraction,feature matching and tracking,and motion.In order to achieve the positioning of mobile robots.Secondly,before the mobile robot can walk,it is necessary to plan a reasonable path for the robot,that is,a global path planning method,under the condition of known environmental information.This method is based on the global environmental information has been obtained,the robot is planned out from a starting point to the end of the path of movement.The traditional global path planning is mainly based on the graph theory.The method is generally to first construct the environment as a graph and then find the optimal path from the graph.The advantage is that the planning path is relatively simple and easy to implement.The disadvantage is that the obtained path may be the second best path.In this paper,we propose a geometry algorithm based on the principle of ray emission,which can avoid this shortcoming and find the optimal path to the global path planning.Thirdly,when we consider the mobile robot encounters the unknown obstacle while walking in the work environment.In order to avoid unknown obstacles,the local path planning method needs to find the optimal path for robot to walk.Genetic algorithms have unique advantages over other optimization algorithms in the localization of mobile robots.However,when mobile robots face complicated obstacle distribution,the problem of insufficient adaptability of genetic algorithm gradually becomes apparent.In this paper,adaptive crossover operator,mutation operator and extensible fitness function are designed,which make the genetic algorithm more reasonable to use the parameters of the algorithm in the running process.Finally,by comparing the improved genetic algorithm designed in this paper with the traditional genetic algorithm,it is concluded that the algorithm designed in this paper is more efficient.Finally,the main work of this thesis is summarized and some future research directions are put forward.