基于改进蚁群算法在机器人路径规划上的研究

Research on Improved Ant Colony Algorithm in Robot Path Planning

摘要: 传统蚁群算法由于参数固定化，在机器人路径规划中，无法发挥算法的优越性。本文提出了一种改进的蚁群算法，用于提高算法在路径研究上的效率。通过分析蚁群各阶段的特点，有针对性地调整信息素因子大小，提高算法的性能。引入自适应平衡因子对启发式信息函数进行改进，维持算法平衡。利用随机概率触发随机状况，提高系统的抗干扰能力。通过提出前瞻概率调整蚂蚁节点状态转移方式，扩大搜索空间，提高路径规划的整体效率。实验仿真采用复杂度不同的地图来对比改进前后的蚁群算法。结果表明，改进后的算法不仅提高了算法性能，还可以稳定应对更为复杂的环境，为研究路径规划提供了参考。

Abstract: Due to the fixed parameters of the traditional ant colony algorithm (ACA), the advantages of the algorithm cannot be exerted in the robot path planning. This paper proposes an improved ACO to improve the efficiency of the algorithm in path research. By analyzing the characteristics of each stage of the ant colony, the size of the pheromone factor is adjusted in a targeted manner to improve the performance of the algorithm. An adaptive balance factor is introduced to improve the heuristic information function to maintain the balance of the algorithm. Use random probability to trigger random conditions to improve the anti-interference ability of the system. By proposing forward-looking probability to adjust the state transition method of ant nodes, the search space is expanded and the overall efficiency of path planning is improved. The experimental simulation uses maps with different complexity to compare the ant colony algorithm before and after improvement. The results show that the improved algorithm not only improves the performance of the algorithm, but also can stably deal with more complex environments, which provides a reference for the study of path planning.