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首页> 外文期刊>Mechatronics, IEEE/ASME Transactions on >Direct Joint Space State Estimation in Robots With Multiple Elastic Joints
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Direct Joint Space State Estimation in Robots With Multiple Elastic Joints

机译：具有多个弹性关节的机器人中的直接关节空间状态估计

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摘要

For robots with joint elasticity, discrepancies exist between the motor side and the load side (e.g., the link of the robotic joint). Thus, the load side (end-effector) performance can hardly be guaranteed with motor side measurements alone. In this paper, a computationally efficient load side state estimation scheme is proposed for the multi-joint robot with joint elasticity, which is equipped with motor encoders and a low-cost end-effector MEMS sensor such as a three-axial accelerometer. An optimization-based inverse differential kinematics algorithm is developed to obtain the load side joint state rough estimates. With these rough estimates, the estimation problem is decoupled into simple second-order kinematic Kalman filter for each joint to refine the joint position and velocity estimates. Maximum likelihood principle is utilized to estimate the fictitious noise covariances used in the Kalman filter. Both offline and online solutions are derived. The extensions to other sensor configurations are discussed as well. The effectiveness of the developed method is validated through the simulation and the experimental study on a 6-DOF industrial robot.
机译：对于具有关节弹性的机器人，电机侧和负载侧之间存在差异（例如，机器人关节的连杆）。因此，仅靠电机侧的测量很难保证负载侧（末端执行器）的性能。本文针对具有关节弹性的多关节机器人提出了一种计算效率高的负载侧状态估计方案，该方案配备了电机编码器和低成本的末端执行器MEMS传感器（如三轴加速度计）。提出了一种基于优化的逆微分运动学算法来获得负荷侧联合状态的粗略估计。通过这些粗略的估计，可以将估计问题分解为每个关节的简单二阶运动卡尔曼滤波器，以精炼关节位置和速度估计。利用最大似然原理来估计卡尔曼滤波器中使用的虚拟噪声协方差。脱机和在线解决方案均派生。还讨论了其他传感器配置的扩展。通过对六自由度工业机器人的仿真和实验研究验证了该方法的有效性。

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