基于低成本imu的倒立摆角位置估计

D. Aoyagi, Sukgi Choi
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摘要

在寻找一种经济实惠的解决方案来测量自行车的滚动角度时,我们遇到了博世的惯性测量单元(IMU) BNO055,它包含3轴加速度计,陀螺仪和磁力计,并通过内置专有的“Fusion”算法来产生“绝对方向”。我们找到了另一个低成本的IMU, InvenSense的MPU-9250,它也可以通过嵌入式Fusion软件计算绝对方向。由于无法在这些imu的数据表中找到有关其动态特性的信息,我们试图在动态条件下对其进行评估,特别是在估计滚转角时。我们构造了一个倒立摆作为自行车的模型,在倒立摆上安装了两个imu,并附加了一个电位器来测量实际的角度位置以供参考。此外,作为专有融合算法的替代方案,我们设计并实现了一个扩展卡尔曼滤波器,我们假设该滤波器的性能优于专有融合算法,因为我们的算法包含了倒立摆的运动学,而imu的融合算法没有。在一系列实验中,我们观察到BNO055的原始加速度和陀螺仪信号存在明显的时间滞后,大约为0.05-0.1秒。BNO055的聚变反应也有类似的滞后和0.5-3°的偏移;我们还注意到输出信号的波动相当不可预测,可能是由于它的“自动校准”功能,无法关闭。MPU-9250在原始加速度信号方面表现出比BNO055更好的性能,特别是陀螺仪信号。MPU-9250的Fusion性能略好于BNO055,通常显示0.03-0.06秒的滞后和0.5-1°的静态偏移。我们基于MPU-9250原始信号实现的卡尔曼滤波比两种融合算法表现更好,延迟约0.02-0.03秒,偏移约0.5-1°,支持我们的假设。我们的下一步是在一辆运动中的自行车上进行实验。
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Angular Position Estimation of an Inverted Pendulum Using Low-Cost IMUs
Seeking an affordable solution to measure a bicycle’s roll angle, we came across an Inertial Measurement Unit (IMU) BNO055 by Bosch, which contains 3-axis accelerometer, gyroscope, and magnetometer, and is advertised to produce “absolute orientation” by a built-in proprietary “Fusion” algorithm. We found another low-cost IMU, an MPU-9250 from InvenSense, which could also calculate absolute orientation via embedded Fusion software. Being unable to find information about dynamic characteristics of these IMUs in their datasheets, we sought to evaluate them under dynamic conditions, specifically in the estimation of roll angle. We constructed an inverted pendulum as a model of a bicycle, mounted both IMUs on it, and attached a potentiometer to measure actual angular position for reference. Additionally, as an alternative to the proprietary Fusion algorithms, we devised and implemented an Extended Kalman Filter, which, we hypothesized, would perform better than the proprietary Fusion algorithms, because our algorithm incorporated the kinematics of the inverted pendulum while the Fusion algorithm of the IMUs did not. In a series of experiments, we observed a significant time lag, about 0.05-0.1 second, in BNO055’s raw acceleration and gyro signals. The BNO055’s Fusion responded with similar lag and an offset of 0.5-3°; we also noticed rather unpredictable fluctuation in the output signals, possibly due to its “automatic calibration” feature, which cannot be disabled. The MPU-9250 exhibited better performance than the BNO055 in terms of raw acceleration signals and, particularly, gyro signals. The MPU-9250’s Fusion performed somewhat better than BNO055’s, typically showing lag of 0.03-0.06 sec and static offset of 0.5-1°. Our implementation of Kalman Filter based on MPU-9250 raw signal performed better than either Fusion algorithm, with about 0.02-0.03 second lag and 0.5-1° offset, supporting our hypothesis. Our next step is to experiment on an actual bicycle in motion.
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