An improved ant colony optimization algorithm in mobile robot path planning

摘要

In order to promote the convergence speed and global optimization effect, an improved ant colony optimization algorithm (IACO) is proposed. Firstly, an initial pheromone unequal rule is constructed, which is based on the starting and ending positions to avoid the blindness of early search. Secondly, the heuristic information function is improved by considering the distance between the starting position and the target position. Thirdly, the state transition pseudo random strategy is utilized to select the next node and the state transition probability coefficient is adaptively adjusted. Finally, the pheromone update rules have been revised, that is, the pheromone volatilization coefficient can be dynamically adjusted and the pheromone threshold can also be set. Simulation results show that compared with traditional ant colony method, the proposed algorithm is valid and effective to settle path planning problems of mobile robot in different contraint environments.