Artificial Intelligence In Commodity Hardware Aerial Drones Dept. of CIS- Senior Design 2012-2013

Recent computer science research has been particularly inter-ested in autonomous or semi-autonomous unmanned aerial vehicles (UAVs) because of their application in situations too dangerous for human involvement. Modern artificial intel-ligence advancements have allowed for the implementation of highly complex spatial awareness algorithms, which en-able autonomous flight on a variety of di↵erent flying de-vices. However, these approaches usually involve aerial ve-hicles with expensive sensors or a large number of surround-ing cameras that are capable of extracting an entire 3D con-struction 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 en-able it to simulate some of the tasks which more expensive implementations have already achieved. Software alterna-tives for visual guidance and obstacle avoidance are achieved by modeling techniques used by insects, such as the bumble-bee, including object recognition, optical flow, and odometry. These improvements could be widely applicable and provide greater access to UAVs for situations where recovery is un-likely, or for other financially constrained applications. 1