基于高斯模型和视觉测量的机械臂在线自标定方法研究

Research on On-line Self-calibration Method for Manipulators Based on Gauss Model and Vision Measurement

对于在空间站、核电站或其他无人环境中工作的特种机械臂,过载、碰撞、振动、温度变化和应力释放等都会对结构几何参数产生影响,长期使用操作精度会不断降低。因此,当无人操作机械臂在执行高精度操作或维护时,应进行自主标定以提高操作精度。提出了一种基于多标识点的在线自标定方法,用固定在机械臂末端的相机测量1~3个标识点,形成非稳态多传感器手眼系统。针对系统存在的运动误差和传感器测量误差,提出了高斯运动(GM)方法,建立了可行的标定模型,并将线性二次型调节器(LQR)控制与扩展卡尔曼滤波(EKF)相结合,实现了误差影响下的标定运动轨迹跟踪。同时,在视觉系统存在测量误差和标识点非稳态识别造成间歇信号反馈时,为了形成高精度、稳定的信号采集,提出了基于蒙特卡罗方法,根据测量点方差筛选最优测量点。为了更加契合机械臂几何误差形态,采用了DH增广模型,一次逼近30个模型参数。基于中国空间站的生命科学手套箱机械臂的数据比较分析和试验结果证明了所提方法的有效性和可行性。

For some special manipulators such as the ones work at the space station,nuclear or some other unmanned environments,the overload,collision,vibration,temperature change or release of the internal stress would affect the structural parameters.And thus the operation precision might constantly decrease in long-term use.So the unattended manipulators should calibrate itself when they execute high precision operations or proceed self-maintenances.An on-line self-calibration method based on multi-markers is proposed.A camera fixed on the end of the manipulator is used to measure one to three positional makers,which forms an unstable multi-sensor eye-in-hand system.For the system has motion error and measurement error,a Gaussian motion(GM)method is proposed to form a feasible calibration model.The linear quadratic regulator(LQR)control and extended Kalman filter(EKF)are combined together to make the manipulator track the planned trajectories.In the same time,the Monte-Carlo method is proposed to form a high precision and stable signal acquisition when the visual system has measurement error and intermittent signal feedback.An automatic sampling process is adopted to select the optimal measurement points based on their variances.An expended DH model is proposed to approach 30 parameters in a time.Experiments are carried out on a manipulator in a life sciences glove box that will work at the Chinese space station to prove the efficiency and feasibility of the method proposed.