Affiliations: [a] School of Mechanical Engineering, Chongqing Three Gorges University, Wanzhou, China
| [b]
Chongqing Engineering Technology Research Center for Light Alloy and Processing, Chongqing, China
| [c]
Intelligent Manufacturing Pilot Technology Chongqing University Engineering Research Center, Chongqing, China
Correspondence:
[*]
Corresponding author. Qiang Luo, School of Mechanical Engineering, Chongqing Three Gorges University, Wanzhou, 404100, China. E-mail: qluosxxy@bjtu.edu.cn.
Abstract: The traditional ant colony algorithm has some problems, such as low search efficiency, slow convergence speed and local optimum. To solve those problems, an adaptive heuristic function is proposed, heuristic information is updated by using the shortest actual distance, which ant passed. The reward and punishment rules are introduced to optimize the local pheromone updating strategy. The state transfer function is optimized by using pseudo-random state transition rules. By comparing with other algorithms’ simulation results in different simulation environments, the results show that it has effectiveness and superiority on path planning.
Keywords: Mobile robot, path planning, ant colony algorithm, heuristic information, global optimization
