改进蚁群算法的煤矿巡检机器人路径规划  

Path Planning of Coal Mine Patrol Robot Based on Improved ACO Algorithm

作　　者：宁竞[1] 龙妍[1] NING Jing;LONG Yan(Nanning College for Vocational Technology,Nanning 530008,China)

机构地区：[1]南宁职业技术学院,南宁530008

出　　处：《煤炭技术》2023年第6期235-237,共3页Coal Technology

基　　金：2022年度广西高校中青年教师科研基础能力提升项目(2022KY1020)。

摘　　要：针对当前矿用机器人巡检路径规划方法效率低、巡检用时长、能耗相对较高等不足,利用蚁群算法(ACO)对巡检机器人路径规划方法。经分析巡检机器人作业原理,使用SLAM方案来完成机器人行进中的同步定位与井下环境构图,构建了基于改进ACO算法的路径寻优模型,并在Ubuntu+ROS系统下进行仿真实验。结果表明,SLAM构图结果与实验环境较为一致,精确度高且误差较小;改进的ACO算法规划用时短,巡检路径拐点较少,能够有效降低巡检过程中的使用能耗。Aiming at the inefficiency,long inspection time and relatively high energy consumption of current patrol path planning methods for mining robots,uses ant colony optimization(ACO)to plan the patrol robot path.After analyzing the working principle of patrol robot,the SLAM scheme is used to complete the synchronous positioning and underground environment composition of the robot while it is moving.A path optimization model based on the improved ACO algorithm is constructed and simulated under the Ubuntu+ROS system.The results show that the composition of SLAM is consistent with the experimental environment,with high accuracy and small error.The improved ACO algorithm takes less time to plan and has fewer inflection points in the patrol route,which can effectively reduce the energy consumption in the patrol process.

关 键 词：蚁群算法 巡检机器人 路径规划 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置]