Autonomous Landing of a Rotary Unmanned Aerial Vehicle in a Non-cooperative Environment using Machine Vision

Abstract

Landing an Unmanned Aerial Vehicle (UAV) is a non-trivial problem. Removing the ability to cooperate with the landing site further increases the complexity. This thesis develops a multi-stage process that allows a UAV to locate the safest landing site, and then land without a georeference. Machine vision is the vehicle sensor used to locate potential landing hazards and generate an estimated UAV position. A description of the algorithms, along with validation results, are presented. The thesis shows that software-simulated landing performs adequately, and that future hardware integration looks promising.