邓小飞,张志刚.基于改进蚁群算法的码垛机器人路径规划应用研究[J].包装工程,2020,41(3):200-205.
DENG Xiao-fei,ZHANG Zhi-gang.Path Planning and Application of Palletizing Robot Based on Improved Ant Colony Algorithm[J].Packaging Engineering,2020,41(3):200-205. |
| 基于改进蚁群算法的码垛机器人路径规划应用研究 |
| Path Planning and Application of Palletizing Robot Based on Improved Ant Colony Algorithm |
| 投稿时间：2019-09-26 修订日期：2020-02-10 |
| DOI：10.19554/j.cnki.1001-3563.2020.03.031 |
| 中文关键词: 人工势场 蚁群算法 路径规划 码垛机器人 |
| 英文关键词: artificial potential field ant colony algorithm path planning palletizing robot |
| 基金项目:2020年度河南省高等学校重点科研项目（20B520020） |
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| 中文摘要: |
| 目的 为解决蚁群算法在码垛机器人路径规划中存在的收敛速度慢、容易陷入局部最优等问题，提出一种人工势场和蚁群算法相结合的方法。方法 首先，根据码垛机器人机械手在人工势场中不同节点所受到的合力，对初始信息素进行不均匀分布，以解决蚁群算法初期由于缺乏信息素导致的无效路径搜索。其次，在启发函数的设计中引入码垛机器人机械手在下一节点所受到的合力，以解决蚁群算法容易陷入局部最优的问题。最后，对信息素的更新策略进行改进。按照寻得路径的长度不同，对每次迭代完成后信息素的增量成比例进行更新，并设置最大、最小值，以解决迭代后期路径上信息素过大而使蚁群算法陷入局部最优的问题。结果 改进后的蚁群算法收敛速度提升了约51%，寻找到的最短路径提升了约10%。和其他改进的蚁群算法相比，在综合性能上也有一定程度上的提高。结论 改进后的蚁群算法收敛更快，寻找的最优路径更短。 |
| 英文摘要: |
| This paper aims to present a method of combining artificial potential field with ant colony algorithm to solve the problems of slow convergence speed and easy to fall into local optimum in path planning of palletizing robot. Firstly, the initial pheromone was distributed unevenly according to the joint forces of different nodes in the artificial potential field, to solve the invalid path search caused by the lack of pheromone in the initial stage of ant colony algorithm. Secondly, the joint force of the robot hand in the next node was introduced in the design of heuristic function to solve the problem that the ant colony algorithm was easy to fall into local optimum. Finally, the strategy of pheromone updating was improved. After each iteration, the increment of pheromone was updated in proportion to the length of the search path, and the maximum and minimum values were set to solve the problem that the pheromone on the path was so large at the later stage of the iteration that the ant colony algorithm fell into the local optimum. The improved algorithm improved the convergence speed by about 51% and the shortest path by about 10%. Compared with other improved ant colony algorithm, it also improved the comprehensive performance to a certain extent. The improved ant colony algorithm converges faster and finds shorter optimal path. |
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