%0 Journal Article
%A ZHANG Tianrui
%A WU Baoku
%A ZHOU Fuqiang
%T Research on Improved Ant Colony Algorithm for Robot Global Path Planning
%D 2022
%R 10.3778/j.issn.1002-8331.2107-0369
%J Computer Engineering and Applications
%P 282-291
%V 58
%N 1
%X The problems of basic ant colony algorithm are solved including too large turning angle, easy to fall into local minimum and slow convergence speed of basic ant colony algorithm in the process of robot path planning. Based on the analysis of robot path planning environment modeling method, firstly, the corner heuristic function is introduced into the node selection probability formula to enhance the directivity of path selection and improve the search speed of the algorithm. Furthermore, the heuristic function is improved by introducing the quadratic sum of the distance between the current node and the next node, the distance between the next node and the target node, which makes the search process more targeted and reduces the probability of falling into local minimum. In addition, the pheromone volatilization factor adaptive update strategy is proposed to expand the search range and improve the convergence speed. Secondly, the crossover operation of genetic algorithm is used to optimize the mobile path twice to enhance the optimization ability of the algorithm. Then the path smoothing operation is introduced based on Floyd algorithm to reduce the number of nodes in the mobile path. Finally, the grid method is used to model the environment. Finally, compared with other algorithms by solving multiple unimodal test functions and multi-modal test functions in MATLAB, and the simulation comparison experiment of robot global path planning in grid environment modeling, so as to verify that the improved algorithm has more advantages in path optimization speed and quality. The simulation results show that the improved ant colony algorithm is feasible and effective.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2107-0369