MAV Stabilization using Machine Learning and Onboard Sensors

Past automation work with miniature aerial vehicles (MAVs) at Cornell has produced interesting results [1] and presented additional challenges. During past projects, results have often been limited not by insufficiencies in planning algorithms, but by navigation errors stemming from inadequate control in the face of realistic, breezy operating environments. In many cases the MAVs will simply drift off the desired path (Figure 1). Thus, this project focuses on refining the basic motion of the same platform, and in particular, minimizing its drift. Our work focuses on reduction of low frequency drift in gps-denied environments. Similar work has been done, some using neural networks [4] or using adaptive-fuzzy control methods [5] to stabilize a quadrotor. Though this research has produced promising results, these methods were demonstrated only in simulation, not via live testing. 1 Figure 1: Desired path vs. actual path due to drift.