This research investigates 4-rotor Uninhabited Aerial Vehicle (UAV) rendezvous and docking precision navigation using 3D imaging sensor data and MATLAB software. The orientation of a UAV is determined relative to a docking station, and in future research the UAV’s controller implementation will incorporate the orientation to enable autonomous precision docking. The 3D imaging solution leverages fixed docking station geometry, which is a square based pyramid with a square top surface. This shape has 5 planar surfaces that can be identified by the sensor processing software.
This implementation builds on previous sensor processing research at Ohio University and expands the initial capability in a series of three subsystems. Subsystem one detects planar surfaces from a 3D point cloud docking station data set. Subsystem two consistently identifies the sides of the docking station. Subsystem three implements Horn’s algorithm to detect UAV orientation with respect to the dock.
The presented data identifies UAV rotation and translation with respect to the dock using actual data. The research verifies the solution using simulated data.