Research Area:  Metaheuristic Computing

Abstract:

A global path planning method is proposed based on improved ant colony optimization according to the slow convergence speed in mobile service robot path planning. The distribution of initial pheromone is determined by the critical obstacle influence factor. The influence factor is introduced into the heuristic information to improve the convergence speed of the algorithm at an early stage. A new pheromone update rule is presented using fuzzy control to change the value of pheromone heuristic factor and expectation heuristic factor, adjusting the evaporation rate in stages. The method achieves fast convergence and guarantees global search capability. Finally, the simulation results show that the improved algorithm not only shortens the running time of global path planning, but also has a higher probability of obtaining a global optimal solution. The convergence speed of the algorithm is better than the traditional ant colony algorithm.

Keywords:  
ant colony optimization
critical obstacle influence factor
fuzzy control
global path planning
mobile service robot

Author(s) Name:  Yong Tao , He Gao, Fan Ren , Chaoyong Chen , Tianmiao Wang, Hegen Xiong and Shan Jiang

Publisher name:  MDPI

DOI:  10.3390/app11083605

Volume Information:  Volume 11 Issue 8

Paper Link:   https://www.mdpi.com/2076-3417/11/8/3605