Dr. Elizabeth Mezzacappa Profile

Dr. Elizabeth Mezzacappa

at DEVCOM Armament Ctr

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Publications (4)

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Proceedings Article | 7 June 2024 Presentation + Paper

Simulation experimentation of swarms: methodologies and analyses

Elizabeth Mezzacappa, Melissa Jablonski, Michael McBride, Shawn Mather, Aden Clymer, Kayla Jones, Tess Huchun-Walker, Curtis Meares, Ross Arnold

Proceedings Volume 13051, 1305108 (2024) https://doi.org/10.1117/12.3012773

KEYWORDS: Computer simulations, Data modeling, Design, Analytical research, Visualization, Control systems, Machine learning, Artificial neural networks

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Collections of autonomously behaving systems, or swarms, are predicted to be an important component of the US DoD strategy. Therefore, research into how to create swarms with suitable characteristics, behaviors, and function for these different purposes is in the interest of the US military. However, there are challenges in swarm research, including technical limitations of existing hardware, the need to address both individual drone level behavior as well as the complexities of the entire swarm behavior, and the parameter combinatorics that may be relevant to swarm performance in operations.

This presentation proposes methodologies for the computer simulation research and analyses for experimentation on swarm behavior. Swarm performance data from computer simulation experimentations using simulation software were analyzed through multiple steps to investigate how individual and entire swarm characteristics might affect how well the swarm performed a mission.

Proceedings Article | 12 April 2021 Presentation + Paper

Human data collection for machine learning and artificial intelligence aid development for multi-domain operations

Elizabeth Mezzacappa, Gladstone Reid, Robert DeMarco

Proceedings Volume 11746, 1174614 (2021) https://doi.org/10.1117/12.2585583

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This presentation will describe the Combat Capabilities Development Command (DEVCOM) Armament Center’s research on human behavior for machine learning and development of artificial intelligence aids. Human data collection for the development of artificial intelligence aid development can be roughly categorized into three categories. The first can be described as collection of human behavioral data in order to base artificial intelligence on natural intelligence. The second is the work in turning artificial intelligence into artificial intelligence aids, with a focus on the examination of the interaction between human and artificial intelligence aid. The third is research examining validation—confirming that the artificial intelligence aid actual aids the natural intelligence. For the past few years the DEVCOM Armament Center’s Tactical Behavior Research Laboratory has engaged in research efforts aimed at generating human data sets for machine learning analyses while conducting testing, evaluation and validation of notional artificial aids. Several efforts across the three categories will be described. These include human data gathering efforts for a targeting prioritization, human threat identification in an urban environment, and electrophysiological interfaces. The data from these efforts are inputted into appropriate machine learning methodologies from which algorithms underlying artificial intelligence aids may be derived. Specialized facilities (the Experimental Verification and Validation Assessment Lab) for gathering these data will be described. Finally, the presentation will include an overview of the lab’s processes and pipelines from its initial data gathering and algorithm development to its testing and evaluation and ultimately, to its verification and validation of artificial intelligence aids.

Proceedings Article | 12 April 2021 Presentation + Paper

Experimentation for optimization of heterogeneous drone swarm configurations: terrain and distribution

Ross Arnold, Elizabeth Mezzacappa, Melissa Jablonski, Benjamin Abruzzo, Jonathan Jablonski

Proceedings Volume 11746, 1174625 (2021) https://doi.org/10.1117/12.2585589

KEYWORDS: Statistical analysis, Relays, Natural disasters, Computer simulations

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Computer simulation experimentation examined the effectiveness of different Unmanned Aircraft System (UAS) swarm configurations for identification and localization of survivors after a natural disaster using the DroneLab application. Swarms differed in terms of total number of drones and ratio of entities programmed to perform one of three different “personalities”—Relay, Social, and Antisocial. Relay behavior puts a high priority on maintaining proximity to the centroid of the swarm while also maintaining a distance to closest neighbor drones equal to half of the maximum WiFi range. Antisocial drones prioritize an expanding behavior, increasing the spread of the swarm, while the Social behavior prioritizes a contractive behavior resulting in a tighter swarm formation. All drones performed a local waypoint-based search behavior while conducting a spiral-out search pattern upon detecting four or more survivors within a 10-meter radius. Swarm configurations with different ratios of these behaving entities were assessed for mission completion, defined as time to find 90% of the survivors. Mission completions were recorded for four simulation scenarios consisting of two terrains (urban/rural) with two different distributions of survivors (naturalistic/randomized). Ten replications of 98 different drone configurations were evaluated. Statistically significant differences between time to mission completion between the terrains, between the two distributions, and among the iterations were revealed. Qualitative comparisons revealed differences in configurations that performed the best in each terrain. A few configurations performed well in all four scenarios. Moreover, the minimum number of entities needed for well-performing swarms was indicated. The work demonstrates the utility of computer experimentation and statistical analyses for developing a framework for swarm design for operational effectiveness.

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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