Tracking of Mobile Targets using Unmanned Aerial Vehicles

Abstract

This paper presents an integrated effort by Cal Poly Pomona and UC Irvine for the tracking of mobile targets using unmanned aerial vehicles. A twin-engine airplane is used as the aerial platform. The airplane is being equipped with a commercial-off-the-shelf imaging and video processing systems, which are capable of tracking mobile targets. Besides using the off-the-shelf systems, we are also developing neuromorphic algorithms for autonomous target tracking and action selection. The algorithm is inspired by the mammalian visual system function and is implemented through GPU parallel computing. The algorithm uses a neuromorphic image preprocessing pipeline implemented on a NVIDIA GPU using CUDA and a visual tracking pipeline implemented on a CPU. For autonomous flight, the airplane is being equipped with a Piccolo II autopilot from Cloud Cap Technologies. Our UAV path planning strategy includes either circular or sinusoidal patterns, depending on the target speed. The path planning algorithm will be implemented on top of the autopilot system.