基于改进蚁群算法的监测机器人路径规划方法设计  

Design of Monitoring Robot Path Planning Method Based on Improved Ant Colony Algorithm

作　　者：褚丽娜[1] CHU Lina(Chongqing Science and Technology Innovation Vocational College,Chongqing 402160,China)

机构地区：[1]重庆科创职业学院,重庆402160

出　　处：《流体测量与控制》2023年第2期30-34,共5页Fluid Measurement & Control

基　　金：重庆市教育科学“十四五”规划课题(2021-GX-184);重庆市职业教育教学改革研究项目(GZ223265)。

摘　　要：监测机器人在工业与制造业中得到大规模的应用,对其展开合理的运动路径规划对于机器人的监测效果具有直接影响。考虑到当前监测机器人路径规划方法的障碍物规避环节较为薄弱,导致机器人对于动态障碍物规避能力较差的问题,设计基于改进蚁群算法的监测机器人路径规划方法。使用栅格法对地理信息系统(GIS)地图进行处理,构建监测机器人活动空间模型。选择路径节点自适应参数,确定蚁群算法的信息素强度以及更新次数。增加引导因子对蚁群算法加以改进,并设定动态障碍物规避算法,完成动态障碍物环境下的监测机器人路径规划。构建仿真实验环节,对此方法的路径规划能力展开验证。实验结果表明:此方法可在获取最短运动路径的同时,实现动态障碍物规避。Monitoring robots have been applied on a large scale in the industry and manufacturing industry,and their reasonable motion path planning has a direct impact on the monitoring effect of robots.Considering that the obstacle avoidance link of the current monitoring robot path planning method is relatively weak,which leads to the poor dynamic obstacle avoidance ability,the monitoring robot path planning method based on the improved ant colony algorithm is designed.The geographic information system(GIS)map was processed using the grid method to construct a monitoring robot activity space model.The path node adaptive parameters were selected to determine the pheromone intensity and the number of updates of the ant colony algorithm.Add the guidance factor to improve the ant colony algorithm,and the dynamic obstacle avoidance algorithm is set up to complete the monitoring robot path planning under the dynamic obstacle environment.Build a simulation experiment to verify the path planning ability of this method.The experimental results confirm that this method can realize the dynamic obstacle avoidance while obtaining the shortest motion path.

关 键 词：改进蚁群算法 里程计算 监测机器人 栅格地图 自适应参数调整 启发因子 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]