Towards a Heterogeneous Swarm for Object Classification
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Authors
Arnold, Ross
Abruzzo, Benjamin
Korpela, Christopher M.
Issue Date
2019-07
Type
proceedings-article
Language
Keywords
Object recognition , Software , Unmanned Aerial Vehicle , Computers , Cameras , Robots , Software architecture
Alternative Title
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.
Description
Citation
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.
Publisher
IEEE