Research On Path Planning Of Intelligent Storage Robot Based On Improved Ant Colony Algorithm

Intelligent warehouse robot,as a new type of intelligent equipment in digital factory,has a very wide application prospect in logistics industry and manufacturing industry.Among them,path planning is a key technology of intelligent warehouse robot.In order to improve the ability of robot path planning in flexible intelligent warehouse environment and complete the task of path planning safely and quickly,based on the traditional Ant Colony Optimization(ACO),this thesis studied the path planning problem of intelligent warehouse robot by combining with raster method.The traditional ant colony algorithm is improved by improving heuristic function,search strategy and path Angle optimization,and the influence of unknown factors on path planning is simulated by integrating probability distribution model.The main research work of this thesis is as follows:Firstly,the composition of the intelligent warehouse system is introduced,and the common modeling methods and path planning algorithms in path planning are analyzed.Secondly,according to the problems and demands of the intelligent warehouse system,the environmental modeling method and path search algorithm adopted in this thesis are determined.This thesis studies the cargo distribution route of the intelligent warehouse robot from three aspects: improving the raster map,optimizing the search strategy and image processing.Finally,the feasibility of the proposed method is verified by single factor and multi-factor simulation experiments.For the establishment of the electronic map of the intelligent storage robot,firstly,based on the traditional black-and-white two-color grid method,the obstacles in the storage environment were pretreated by the expansion corrosion algorithm.Secondly,in order to combine the unknown factors with the electronic map,the probability density function is fused with the raster map,and the three-color raster map is obtained based on the Poisson distribution and Gaussian distribution.Finally,the grid particle size is determined to establish an electronic map in accordance with the storage environment in this thesis,which provides a basis for the operation of the algorithm.For the path search algorithm of intelligent warehouse robot,this thesis analyzes the inherent defects of the traditional ant colony algorithm and the shortcomings in the flexible intelligent warehouse environment in this thesis,and makes improvements in three aspects.The heuristic function is optimized,and the path factor,turn factor and safety factor are taken into consideration to construct the heuristic function.In the aspect of pheromone optimization,non-uniform distribution is proposed,which greatly speeds up the convergence speed.In terms of path safety,the planned path is removed and smoothed to ensure the security and stability of the intelligent warehouse robot in operation.In this thesis,the three improvement strategies are respectively carried out a single factor comparison simulation experiment,and the intelligent warehouse robot planning method designed is simulated from the perspective of qualitative analysis,and the feasibility of the three improvement strategies is verified.Then the three improvement strategies are integrated to carry out global path planning for flexible intelligent warehouse environment for the intelligent warehouse robot,which verifies the feasibility of the improvement work in this thesis.