电力检修机械臂动作学习与控制研究

Research on Motor Learning and Control of Electric Maintenance Manipulator

为实现电力检修机械臂运动轨迹的快速规划,针对图像方式进行人体动作识别时精度低、实时性差的问题,提出一种基于惯性测量方式的机械臂动作学习与控制方案。方案利用9轴惯性传感器测量人体手臂运动参数,经姿态解算后应用李群刚体变换理论进行姿态分解,获取机械臂各舵机控制参数。最后在Labview中进行了机械臂的建模和仿真测试,测试数据和仿真结果表明,本方案成本低、精度高,测量误差小于±1.5°。

In order to realize the fast planning of the motion trajectory of the electric maintenance manipulator,aiming at the problems of low accuracy and poor real-time performance in human motion recognition using image method,a motion learning and control scheme of manipulator based on inertial measurement method is proposed.The motion parameters of human arm are measured by 9-axis inertial sensor,the control parameters of each actuator are obtained by attitude calculation and attitude decomposed by rigid body transformation theory of Lie group.Finally,the modeling and simulation test of the manipulator are carried out in Labview,the simulation results show that the scheme has the advantages of low cost,high precision,and the measurement error is less than±1.5°。