Towards a Heterogeneous Swarm for Object Classification

Abstract

Object classification capabilities and associated reactive swarm behaviors are implemented in a decentralized swarm of autonomous, heterogeneous unmanned aerial vehicles (UAVs). Each UAV possesses a separate capability to recognize and classify objects using the You Only Look Once (YOLO) neural network model. The UAVs communicate and share data through a swarm software architecture using an adhoc wireless network. When one UAV recognizes a particular object of interest, the entire swarm reacts with a pre-programmed behavior. Classification results of people and backpacks using our modified UAV detection platforms are provided, as well as a simulated demonstration of the reactive swarm behaviors with actual hardware and swarm software in the loop.