Rotational Angle Estimation Using an Acceleration Sensor Array and Real-Time Detection Algorithm

Abstract

The instantaneous center of rotation (ICOR) is essential engineering information, providing wheel slip data for fully autonomous skid-steering mobile robots driven by deep learning-based dead-reckoning algorithms. Conventionally, cost-effective and compact inertial measurement unit (IMU) sensors based on microelectromechanical systems (MEMSs) have been employed for ICOR measurement. However, these sensors inherently rely on the differential value of gyroscope data, leading to significant estimation errors due to the amplified noise at low sampling rates. To address this challenge, we developed an acceleration sensor array based on a fiber Bragg grating, inspired by existing MEMS acceleration sensor arrays, which allows ICOR estimation solely from the acceleration data provided by two sensors at the beginning of rotation. The estimation accuracies in the clockwise and counter-clockwise directions were measured to be 3.1 ± 5.1 mm and 3.7 ± 9.9 mm, respectively. Furthermore, a peak detection algorithm was proposed for extracting the peak acceleration under different ICOR conditions using the variance and Bollinger bands. The precision level was comparable to those of optical cameras and MEMS IMU sensors that use computationally intensive algorithms, such as Kalman filters, even without differentiation. This breakthrough in measurement capability opens up the possibility for real-time ICOR estimation, allowing the complete automation of skid-steering-based robots and benefiting fields constrained by the structural limitations of conventional MEMS IMU sensors.