Towards a Heterogeneous Swarm for Object Classification
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.
Object recognition, Software, Unmanned aerial vehicles, Computers, Cameras, Robots, Software architecture
R. Arnold, B. Abruzzo and C. Korpela, "Towards a Heterogeneous Swarm for Object Classification," 2019 IEEE National Aerospace and Electronics Conference (NAECON), Dayton, OH, USA, 2019, pp. 139-147, doi: 10.1109/NAECON46414.2019.9058257.