摘要
针对传统隧道电缆巡检机器人智能越障控制方法控制能力差的问题,研究了一种新的隧道电缆巡检轨道机器人智能越障控制方法,探究了巡检机器人的工作环境,引用D-H参数法建立巡检机器人越障模型,分析每个机器人的关节变量,得到机器人末端执行器的位置和姿态,使用求近似解值法求出力矩和的最小值,通过逆向求解计算出巡检机器人绕线偏转扭转角度,利用优化算法分析机器人越障运动工作空间,建立三维坐标系,并将分解成X-Y坐标系、Y-Z标系、X-Z坐标系,确定末端夹持器在各轴上弹出的最大距离,判断巡检机器人是否能够实现越障,根据反馈结果,进行智能控制。设计实验验证该控制方法有效性,结果表明,该控制方法得到的巡检机器人电压值更接近理论值,可以有效帮助机器人精准地实现越障操作。
Aiming at the problem of poor control ability of traditional intelligent obstacle crossing control method of tunnel cable inspection robot,a new intelligent obstacle crossing control method of tunnel cable inspection robot is studied,and the working environment of the inspection robot is explored.The DH parameter method is used to establish the obstacle detection model of the inspection robot,analyze the joint variables of each robot,obtain the position and attitude of the robot end effector,use the approximate solution method to find the minimum value of the moment,and calculate the inspection by reverse solution.The robot deflects the torsion angle of the winding,and uses the optimization algorithm to analyze the working space of the obstacle crossing motion of the robot,establishes a three-dimensional coordinate system,and decomposes it into an X-Y coordinate system,a Y-Z standard system,and an X-Z coordinate system,and determines that the end clamper pops up on each axis.The maximum distance is determined whether the inspection robot can achieve obstacles and intelligent control is performed according to the feedback result.The design experiment verifies the effectiveness of the control method.The results show that the voltage of the inspection robot obtained by the control method is closer to the theoretical value,which can effectively help the robot to accurately realize the obstacle operation.
作者
韩宇泽
周平
赵轩
储强
HAN Yu⁃ze;ZHOU Ping;ZHAO Xuan;CHU Qiang(State Grid Nanjing Power Supply Company,Nanjing 210000,China)
出处
《电子设计工程》
2019年第24期79-83,共5页
Electronic Design Engineering
基金
国家社会科学基金项目(16CKS061)
关键词
隧道电缆
巡检轨道
智能机器人
机器人控制
智能越障
tunnel cable
inspection track
intelligent robot
robot control
intelligent obstacle crossing