摘要
文章针对电网巡检机器人智能避障问题,提出了一种基于势场蚁群算法的路径规划方法。通过改进传统蚁群算法中的势场函数、信息素更新过程和启发函数,提高了算法在复杂环境中的搜索效率和避障能力。结合改进后的势场蚁群算法,设计了电网巡检机器人智能避障路径规划流程,包括数据采集、路径参数设定、蚁群移动信息搜索、势场力计算、路径信息素更新等步骤。实验结果表明,该方法在仿真环境中能够有效躲避障碍物,显著缩短巡检路径长度并减少巡检时间,为电网巡检机器人的自主导航和智能避障提供了有效的技术支持。
This study addresses the intelligent obstacle avoidance issue for power grid inspection robots by proposing a path planning method based on the potential field ant colony algorithm.Firstly,by enhancing the potential field function,pheromone update process,and heuristic function in the traditional ant colony algorithm,the algorithm’s efficiency and obstacle avoidance capabilities in complex environments are improved.Secondly,a path planning process for intelligent obstacle avoidance of power grid inspection robots is designed,integrating the improved potential field ant colony algorithm.This process includes data collection,path parameter setting,ant colony movement information search,potential force calculation,and path pheromone update.Experimental results show that this method can effectively avoid obstacles in a simulation environment,significantly reducing the inspection path length and time,and providing effective technical support for the autonomous navigation and intelligent obstacle avoidance of power grid inspection robots.
作者
赵明
周政
ZHAO Ming;ZHOU Zheng(State Grid Beijing Tongzhou Electrical Power Supply Company,Beijing 101100,China)
关键词
电网巡检机器人
智能避障
势场蚁群算法
路径规划
避障效果
power grid inspection robot
intelligent obstacle avoidance
potential field ant colony algorithm
path planning
obstacle avoidance effect
