Leveraging A Robotic Arm Platform for Low-Cost Calibration of Inertial Sensors on LAPAN Sounding Rockets

The technology has enabled the widespread use of multisensory integration such as inertia sensors for the localization of inertia and navigation systems. The accuracy of the measurement is a key factor that must be maintained and is influenced by default sensor errors (scale factor, misalignment and bias). Thus, this study focuses on proper calibration (complementary filter) to minimize the invalidation of sensor data. The proposed calibration system applies correction of the accelerometer and gyroscope bias data to the x, y, and z axes. Application to the motion of each axis was carried out on the data surface of a robot arm by adjusting the expected axis in the positive or negative areas. For bias correction or compensated distortion of the accelerometer, low pass filter to eliminate deviation and noise was applied into Arduino software. While, for bias correction or compensated distortion of the gyroscope used high-pass filter via software to allow short-term signals and prevents long-term fluctuations. Consequently, this proposed Inertia Measurement Unit (IMU) calibration system demonstrates a high accuracy at estimating the offset of an object. As a result, the accelerometer data of uncalibration and calibration are [14,29 17,38 –22,57] and [1,56 -4,26 -1,91], respectively, while gyroscope resulted uncalibration data [-4,78 0,72 -3,39] and calibration data are [-0.15 0,64 -0,39]. The results highlight the importance of the calibration process for precise sensor data.