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首页> 外文期刊>American Journal of Sensor Technology >Angular Position Estimation of an Inverted Pendulum Using Low-Cost IMUs
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Angular Position Estimation of an Inverted Pendulum Using Low-Cost IMUs

机译:使用低成本IMU的倒立摆的角位置估计

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摘要

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.
机译:为了找到一种价格合理的解决方案来测量自行车的侧倾角,我们遇到了博世的惯性测量单元(IMU)BNO055,该单元包含3轴加速度计,陀螺仪和磁力计,并通过内置广告宣传“绝对定向”在专有的“融合”算法中。我们找到了另一种低成本IMU,来自InvenSense的MPU-9250,它也可以通过嵌入式Fusion软件计算绝对方向。由于无法在其数据表中找到有关这些IMU的动态特性的信息,我们试图在动态条件下评估它们,尤其是在侧倾角估计中。我们构造了倒立摆作为自行车的模型,在其上安装了两个IMU,并连接了一个电位计以测量实际角度位置,以供参考。此外,作为专有融合算法的替代方案,我们设计并实现了扩展卡尔曼滤波器,我们假设它的性能要优于专有融合算法,因为我们的算法结合了倒立摆的运动学特性,而融合算法则融合了倒立摆的运动学。 IMU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滞,约为0.05-0.1秒。 BNO055的Fusion响应时间类似,延迟为0.5-3°。我们还注意到输出信号中相当不可预测的波动,这可能是由于其“自动校准”功能而无法禁用的。就原始加速度信号,特别是陀螺仪信号而言,MPU-9250的性能优于BNO055。 MPU-9250的Fusion性能比BNO055更好,通常滞后时间为0.03-0.06秒,静态偏移为0.5-1°。我们基于MPU-9250原始信号实现的卡尔曼滤波器的性能要优于任一种Fusion算法,其时滞约为0.02-0.03,偏移为0.5-1°,这支持了我们的假设。我们的下一步是对一辆真正的运动自行车进行实验。

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