Cable-Driven Parallel Robot Pose Estimation Using Extended Kalman Filtering With Inertial Payload Measurements
Cable-Driven Parallel Robot Pose Estimation Using Extended Kalman Filtering With Inertial Payload Measurements
复制标题
使用扩展卡尔曼滤波和惯性有效负载测量进行电缆驱动并联机器人姿态估计
DOI：
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发表时间：
2021
影响因子：
5.2
通讯作者：
R. Caverly
中科院分区：
文献类型：
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作者：
Vinh Le Nguyen;R. Caverly
本文介绍了两种新颖的扩展卡尔曼滤波（EKF）方法，将有效载荷加速度计和速率陀螺仪数据与正运动学融合，以估计缆索驱动并联机器人（CDPR）的有效载荷姿态。为此提出了基于欧拉角的EKF和基于旋转矢量的乘法扩展卡尔曼滤波器（MEKF）。利用无约束姿态参数化恒等式导出了迭代正运动学计算中雅可比矩阵的解析形式，方便了不同姿态参数化的使用。蒙特卡罗模拟进行了两个级别的现实传感器噪声和偏差，以及校准误差。数值结果表明，与单独的正运动学计算相比，使用EKF和MEKF可以更准确地估计姿态。
This letter introduces two novel extended Kalman filtering (EKF) approaches to fuse payload accelerometer and rate gyroscope data with forward kinematics to estimate the payload pose of a cable-driven parallel robot (CDPR). An Euler-angle-based EKF and a rotation-vector-based multiplicative extended Kalman filter (MEKF) are proposed for this purpose. An unconstrained attitude parameterization identity is used to derive an analytic form of the Jacobian involved in the iterative forward kinematics calculations, which facilitates the use of different attitude parameterizations. Monte-Carlo simulations are performed with two levels of realistic sensor noise and bias, as well as calibration errors. The numerical results demonstrate more accurate pose estimates using the EKF and MEKF compared to forward kinematics computations alone.