Artificial Intelligence In Commodity Hardware Aerial Drones

Recent computer science research has been particularly interested in autonomous or semi-autonomous unmanned aerial vehicles (UAVs) because of their application in situations too dangerous for human involvement. Modern artificial intelligence advancements have allowed for the implementation of highly complex spatial awareness algorithms, which enable autonomous flight on a variety of di↵erent flying devices. However, these approaches usually involve aerial vehicles with expensive sensors or a large number of surrounding cameras that are capable of extracting an entire 3D construction from multiple 2D images. Unfortunately, there are many situations when financially, or locationally, expensive sensors are not a viable option. This paper defines a set of improvements that can be made to the AR Drone 2.0 quadrocopter, a simple flying vehicle with limited hardware capabilities. These improvements enable it to simulate some of the tasks which more expensive implementations have already achieved. Software alternatives for visual guidance and obstacle avoidance are achieved by modeling techniques used by insects, such as the bumblebee, including object recognition, optical flow, and odometry. These improvements could be widely applicable and provide greater access to UAVs for situations where recovery is unlikely, or for other financially constrained applications.