A Cost Effective Motion Platform for Performance Testing of MEMS-Based Attitude and Heading Reference Systems

Abstract

An accurate attitude and heading reference system (AHRS) is a key component to ensuring safe and reliable flight of unmanned aerial vehicles (UAVs). Recently, much attention has been given to developing AHRS using inertial sensors based on microelectromechanical sytems (MEMS). These MEMS-based AHRS are low-cost, lightweight, and consume little power. However, the advantages of inexpensive MEMS sensors are coupled with the drawback of having greater potential error in reported roll, pitch, and yaw angles due to increased sensor noise and drift. To minimize this error, advanced sensor fusion techniques such as Kalman Filtering are commonly implemented. Testing these techniques, and the AHRS as a whole, is therefore a crucial part of the performance optimization process. This paper outlines the development of an inexpensive 3-axis motion platform for AHRS calibration and testing that replicates aircraft motions from actual UAV flights, or from a flight simulator. To accomplish this, custom LabVIEW control software was developed to process time-stamped aircraft orientation data. Commands were then sent through a microcontroller to the motion platform, which reconstructs the flights with high precision (R2 = .994). By using this method, AHRS testing can be performed under more realistic conditions, providing an alternative to costly field testing. This technique is especially useful for simulations of autonomous vehicle technologies such as collision avoidance, where an increased risk of damage to the UAV is present.