Path Planning for Omnidirectional Wheeled Mobile Robot by Improved Ant Colony Optimization

摘要

This paper focuses on the path planning algorithm design for the omnidirectional wheel mobile robot. In view of some advantages of ant colony optimization (ACO), such as distributed computing ability, heuristic search strategy and strong robustness, ACO is adopted in this paper to search for the optimal path for the considered mobile robot. To avoid trapping into local minimum or the slower convergence during the process of path planning, some novel methods are proposed to improve the traditional ACO. Specifically, the distance and obstacle information are included in the heuristic information function to enhance local search ability and obtain faster convergence speed. The pheromone updating rule is also improved by introducing an extra time-varying pheromone updating item to avoid trapping into local minimum easily and increase the search efficiency. Besides, the optimal path obtained from the improved algorithm in each iteration is optimized for a second time by reducing the number of redundant intermediate nodes to make the path smoother and shorter. The simulation experiment is operated with MATLAB to verify the effectiveness and feasibility of the proposed path planning algorithm.