Research Based On Improved Ant Conlony Alogorithm For Mobible Robot Path Planning

Robot path planning refers to sensing through a sensor in an unknown environment and finding a safe path from the starting point to the end point.As far as orchards are concerned,in order to increase the degree of automation of orchard operations and reduce labor consumption.Research on orchard operating robots for pruning,spraying,and picking in orchards has become an important means of agricultural development.Path planning,as an indispensable module for orchard mobile robots,has always been an important part of people's research.At present,various experts and scholars have made good research results on the research of path planning for mobile robots.Many algorithms used in path planning for mobile robots have been promoted.Ant colony algorithm as a bionic algorithm to imitate the behavior of ant foraging has always been a popular algorithm.It has strong robustness and is easy to combine with other algorithms,and has broad development prospects.However,the common ant colony algorithm has a large amount of calculations,a slow running speed,and local optimization.This paper aims to complete the path planning of mobile robots in orchard environment.First,the orchard environment is modeled,and the three-dimensional space environment model is transformed into a two-dimensional environment model.The grid method is used to define the movement direction of the mobile robot on the grid,the obstacles and information coding,and simulate the establishment of an environmental map model of the orchard.Then Dijkstra's algorithm and A* algorithm are respectively programmed to make orchard robots' global optimal path planning.Secondly,it analyzes and studies the application of ant colony algorithm in robot path planning.For traditional ant colony algorithm,the problem of large amount of calculation and local optimization is prone to occur,and an improved method is proposed.By establishing the direction angle enlightenment factor,ants try to select the node with the smaller angle as the next mobile node,which improves the search speed of the shortest path;Second,by referring to the reciprocal of the more complex diagonal distance as a new heuristic factor,the distance formula does not require square root operation,the solution is simple,and the search efficiency is improved once more.Therefore,the improved ant colony algorithm improves the ergodicity of the algorithm and avoids falling into a local optimum.In addition,the algorithm converges faster,has better search capability,and can more easily meet real-time path planning requirements in complex orchard environments.In order to avoid the mobile robots from moving obstacles in the orchard environment,this paper attempts to apply the improved ant colony algorithm to the dynamic environment.First of all,a dynamic orchard environment map is established through a grid method.Secondly,combining the A* algorithm with the improved ant colony algorithm,A* algorithm is used to plan a global optimal path in the constructed dynamic orchard environment map.When the mobile robot is searching for a dynamic obstacle and it will collide with it,An improved ant colony algorithm is used to establish local target points,and an optimal path that avoids dynamic obstacles is found along the pheromone direction.In order to achieve dynamic avoidance of mobile robots in a complex environment.