Classifying environmental features from local observations of emergent swarm behavior

Emmons, Megan; Maciejewski, Anthony A.; Anderson, Charles W.; Chong, Edwin K. P.

doi:10.1109/jas.2020.1003129

Cited by 4 publications

(2 citation statements)

References 21 publications

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“…Swarms can potentially leverage these advancements and further reliability efforts by utilizing a large number of robots for increased robustness. Experiments have shown that emergent swarm patterns can serve as an information storage mechanism [19,20] which supports the robustness paradigm. Yet, to the best of our knowledge, no work has actually quanti ied the resilience of a swarm.…”

Section: Related Workmentioning

confidence: 79%

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Quantifying Swarm Resilience with Simulated Exploration of Maze-Like Environments

Emmons,

Maciejewski

2024

JAMRIS

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Artificial swarms have the potential to provide robust, efficient solutions for a broad range of applications from assisting search and rescue operations to exploring remote planets. However, many fundamental obstacles still need to be overcome to bridge the gap between theory and application. In this characterization work, we demonstrate how a human rescuer can leverage minimal local observations of emergent swarm behavior to locate a lone survivor in maze-like environments. The simulated robots and rescuer have limited sensing and no communication capabilities to model a worst-case scenario. We then explore the impact of fundamental properties at the individual robot level on the utility of the emergent behavior to direct swarm design choices. We further demonstrate the relative robustness of the simulated robotic swarm by quantifying how reasonable probabilistic failure affects the rescue time in a complex environment. These results are compared to the theoretical performance of a single wall-following robot to further demonstrate the potential benefits of utilizing robotic swarms for rescue operations.

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Section: Related Workmentioning

confidence: 79%

“…Swarm interactions encode important environmental information, like the presence of openings or obstacles [20]. The encoding process changes properties of the emergent behavior but takes an amount of time depending on the robot density and distance to obstacles.…”

Section: Rescuer Modelmentioning

confidence: 99%