基于改进蚁群算法的自动落布车路径规划OACSTPCD

Aimed at the problems of high convergence times,slow convergence speed and easy to fall into the local optimum in the path planning process of the automatic fabric drop trolley using ant colony algorithm(ACA),an improved ant colony algorithm(IACA)was proposed.Firstly,the pheromone volatilization coefficients ρ were adaptively adjusted to make dynamic changes to o-vercome the excessive number of convergence of the algorithm,to speed up the convergence of the algorithm and to reduce the convergence time of the algorithm.Secondly,the pheromone up-dating formula was improved by introducing the chemotaxis step factor of the chemotaxis opera-tion in the bacterial foraging algorithm,which cuts down the pheromone concentration value at the late stage of the algorithm iteration.This makes the algorithm jump out of the local optimum at the late stage,improving the algorithm's global searching ability.The improved algorithm was validated by simulation in three different grid environments using MATLAB.The simulation re-sults show that compared to the traditional ant colony algorithm,the improved algorithm reduces the number of convergences by 81.1%,the minimum path length by 6.3%,and the convergence time by 20.7%.Finally,the ROS trolley experimental platform was built,and the improved ACO algorithm was experimentally verified in a simulated weaving workshop environment using the ROS robot system.The results show that the improved one reduces the optimization time by 8.6%compared with the traditional ant colony algorithm.