Joint Angle Estimation Using Accelerometer Arrays and Model-Based Filtering
Joint Angle Estimation Using Accelerometer Arrays and Model-Based Filtering
复制标题
使用加速度计阵列和基于模型的过滤进行关节角度估计
DOI：
10.1109/jsen.2022.3200251
复制
发表时间：
2022
影响因子：
4.3
通讯作者：
Vikas, Vishesh
中科院分区：
文献类型：
--
作者：
Woods, Cole;Vikas, Vishesh
关节角度的测量是控制机器人系统和监测人类步态的重要元素。传统上，这是通过使用接触传感器（例如光学或磁性编码器）以及惯性测量单元（IMU）来实现的。 IMU 融合来自加速度计、陀螺仪和磁力计的数据来估计身体的方向。然而，微机电系统（MEMS）陀螺仪容易漂移，磁力计容易受到电磁干扰。相比之下，MEMS 加速度计具有稳定的偏置，并且能够抵抗外部电磁干扰。因此，全加速度计非接触式传感器可以缓解这些问题。在两个连杆在一个关节处连接的情况下，无论任一连杆的坐标系如何，该公共点处的加速度都是相等的。该研究提出使用两个或多个加速度计（非接触式传感器）阵列以及关节处的加速度等效知识来构建动态模型，其中状态对应于关节的角速度和关节角度。使用三种方法估计关节角度：解析法、扩展卡尔曼滤波器 (EKF) 和无迹卡尔曼滤波器 (UKF)。分析方法估计关节角度，而基于模型的滤波方法（EKF 和 UKF）还估计链接角速度。在每个链路上使用两到十个加速度计进行模拟，以比较三种方法的性能并研究加速度计沿链路的放置。仿真结果表明基于模型的过滤方法比解析方法具有更优越的性能。分析还得出结论，加速度计的最佳物理位置是靠近链路的末端，以最大限度地减少估计误差。此外，估计误差的下限由两个链路之间的平均值与相对加速度计长度的最大比率决定。使用三种不同的加速度计对算法进行了实验验证：ADXL345、ADXL357 和 BNO055。研究了慢速和快速周期性、斜坡和脉冲的四种不同规范运动。实验结果证实了基于模型的滤波器比分析方法具有更好的性能。
Measurement of joint angles is an important element for the control of robotic systems and monitoring human gait. This has been traditionally approached through the use of contact sensors, e.g., optical or magnetic encoders, and inertial measurement units (IMUs). IMUs fuse data from accelerometers, gyroscopes, and magnetometers to estimate the orientation of the body. However, microelectromechanical system (MEMS) gyroscopes are prone to drift, and magnetometers are susceptible to electromagnetic interference. In contrast, MEMS accelerometers have stable bias and are resilient to external electromagnetic disturbances. Consequently, an all-accelerometer noncontact sensor can mitigate these problems. In the context of two links connected at a joint, the acceleration at this common point is equivalent irrespective of the coordinate system of either of the links. The research presents the use of an array of two or more accelerometers (noncontact sensors) and the knowledge of the acceleration equivalence at the joint to construct a dynamic model where the states correspond to angular velocities of the joints and the joint angle. The joint angle is estimated using three approaches—analytical, the extended Kalman filter (EKF), and the unscented Kalman filter (UKF). The analytical approach estimates the joint angle, while the model-based filtering approaches (EKF and UKF) also estimate the link angular velocities. Simulations are performed using two to ten accelerometers on each link to compare the performances of the three methods and investigate the placement of accelerometers along the links. The simulation results indicate superior performance of the model-based filtering approaches over the analytical. The analysis also concludes that the best physical placement of the accelerometers is toward the ends of the link for minimizing estimation error. In addition, the lower bound of the estimation error is dictated by the maximum ratio of mean to relative accelerometer length between the two links. The algorithms are experimentally validated using three different accelerometers: ADXL345, ADXL357, and BNO055. Four different canonical movements of slow and fast periodic, ramp, and impulse are examined. The experiment results corroborate the better performance of the model-based filters over the analytical approach.
影响因子：
4.6
作者：
El-Gohary, Mahmoud;McNames, James
通讯作者：
McNames, James