Mr. Reece Ronquillo Profile

Reece Ronquillo

at New Jersey Institute of Technology

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Proceedings Article | 21 April 2020 Presentation + Paper

Network analyses for bioinspired configurations of human and multiple entity teams

Elizabeth Mezzacappa, Alexander Tran, Reece Ronquillo, Austin Hall

Proceedings Volume 11413, 114130T (2020) https://doi.org/10.1117/12.2557313

KEYWORDS: Brain, Sensors, Biological research, Social networks, Social network analysis, Analytical research, Situational awareness sensors

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Contributions of network analyses and neuroscience for the design of a system of heterogeneous deployable sensors for multi-domain operations are explored. The work addresses configuration of lines of the communication to more effectively transfer information from the deployed remote sensors systems back to human decision-makers. These are our initial attempts to craft a framework to guide the creation of robotic swarm networks deployed to gather sensor data for the intelligence preparation of the battlefield. The work proposes that if the sensing swarm’s main function is to gather sensor information to relay back to analysts and decision makers, the best analogy is that of a biological nervous system. The swarm acts as a perceptual system, with drones as the “eyes” of the system and the analysts as the “brain.” Network science also offers vocabulary and concepts to understand parameters that can be thought to reflect characteristics and performance of the swarms of sensors. Using the program ORA (Carnegie Mellon University), a series of models with 44, 60, 200, and 250 entity agents was randomly generated in common network configurations (e.g., small world, coreperiphery). In addition, deliberately designed networks were created to reflect system redundancies and data fusion. These possible swarm communication configurations were compared on operationally relevant characteristics and predicted performance (e.g., bandwidth required, resilience). Substantial differences were observed in characteristics and predicted performance among the candidate configurations. These types of parameters could then be used to guide development of requirements and testing and evaluation for entities making up sensing drone swarms.

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