机器人移动路径跟踪与实时避障方法  

Robot Moving Path Tracking and Real-time Obstacle Avoidance

作　　者：陈通 汪祝年 周鹏 李星宇 CHEN Tong;WANG Zhu-nian;ZHOU Peng;LI Xing-yu(State Grid Jiangsu Zhenjiang Electric Power Supply Corporation,Zhenjiang 212000 China)

机构地区：[1]国网江苏省电力有限公司镇江供电分公司,江苏镇江212000

出　　处：《自动化技术与应用》2024年第6期43-45,54,共4页Techniques of Automation and Applications

基　　金：国网江苏省电力有限公司科技项目资助(J2021076)。

摘　　要：针对机器人测距精度低、跟踪误差大等问题,提出机器人移动路径跟踪与实时避障方法。采用扩展卡尔曼滤波算法,匹配激光传感数据与机器人周边环境特征,融合激光测距数据和人工路标,连续更新机器人位置,实现机器人移动路径跟踪;基于所获取的环境信息,采用自适应阈值法,通过阈值求可行方向,选取最大阈值所对应的方向作为机器人的参考行驶方向,并确定线速度控制律。实验结果显示方法可有效实现固定障碍与运动障碍条件下的路径跟踪与避障目的。Aiming at the problems of low ranging accuracy and large tracking error of robot,a method of robot moving path tracking and real-time obstacle avoidance is proposed.The extended Kalman filter algorithm is used to match the laser sensing data with the characteristics of the robot's surrounding environment,fuse the laser ranging data and artificial road signs,continuously update the robot's position,and realize the robot's moving path tracking.Based on the acquired environmental information,the adaptive threshold method is used to find the feasible direction through the threshold value,and the direction corresponding to the maximum threshold value is selected as the reference traveling direction of the robot,and the linear speed control law is determined.The experimental results show that this method can effectively achieve the goal of path tracking and obstacle avoidance under the conditions of fixed obstacles and moving obstacles.

关 键 词：目标跟踪 机器人 移动路径跟踪 实时避障 卡尔曼滤波 自适应阈值 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP183[自动化与计算机技术—控制科学与工程]