改进蚁群算法及其在机器人避障中的应用

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Title:

Improved ant colony algorithm and its application in obstacle avoidance for robot

作者:

裴振兵¹, 陈雪波²

1. 辽宁科技大学 电子与信息工程学院, 辽宁 鞍山 114051;
2. 辽宁科技大学 研究生院, 辽宁 鞍山 114051

Author(s):

PEI Zhenbing¹, CHEN Xuebo²

1. School of Electronics and Information Engineering, Liaoning University of Science and Technology, Anshan 114051, China;
2. Graduate school, Liaoning University of Science and Technology, Anshan 114051, China

关键词:

改进蚁群算法; 互锁; 机器人; 避障; 栅格法; 建模; 凹形障碍物; 死锁

Keywords:

improved ant colony optimization; interlock; robots; obstacle avoidance; grid method; modeling; concave obstacle; deadlock

分类号:

TP242

DOI:

10.3969/j.issn.1673-4785.201311018

文献标志码:

A

摘要:

提出了一种改进蚁群算法. 首先针对蚁群算法在构造解过程中收敛速度慢且容易陷入局部最优,提出了在蚁群搜索路径过程中,通过建立α(信息素启发式因子)和β(期望启发式因子)的互锁关系,动态自适应调整α、β;其次针对蚁群算法在面对凹形障碍物易陷入死锁,降低搜索效率,提出了广义信息素更新规则;最后利用栅格法进行静态已知环境建模,通过不同规模TSP的仿真验证了该方法的可行性和有效性,同时将其应用到机器人避障并取得了较好实验效果。

Abstract:

An improved ant colony algorithm is proposed in this paper. Firstly, in order to overcome the demerits of the ant colony algorithm, such as low convergence speed and easy to get into the local optimum, α and β are dynamically adaptively adjusted by establishing an interlock between alpha (pheromone heuristic factor) and beta (expected heuristic factor) in the searching route process of ant colony. Secondly, in order to prevent the ant colony algorithm from falling into deadlock when facing concave obstacles, which decreases search efficiency, an update rule of the generalized pheromone is proposed. Finally, static modeling for a known environment is conducted by the grid method. The simulation experiments showed that with different scales of TSP, the improved ant colony algorithm is feasible and efficient. In addition, this algorithm is applied to the obstacle avoidance of robots and the results are effective.