Is this project an undergraduate, graduate, or faculty project?
Undergraduate
individual
What campus are you from?
Daytona Beach
Authors' Class Standing
Joanna Morris, Junior
Lead Presenter's Name
Joanna Morris
Faculty Mentor Name
Dr. Bryan Watson
Abstract
"Models must be capable of demonstrating adaptability across multiple case studies to confirm their reliability and versatility. The Snapping Shrimp Resource Allocation Algorithm, which has been successfully applied in a wildfire response scenario, models efficient, decentralized coordination inspired by the behavior of snapping shrimp. Building on this foundation, the current work explores the potential application of the Snapping Shrimp Resource Allocation algorithm to improvised explosive device response where rapid, autonomous coordination could significantly enhance safety and operational efficiency. Improvised explosive devices remain a persistent issue in modern conflict, requiring strategies for response that are cost-effective, scalable, and capable of adapting to dynamic environments. This case study establishes a foundational simulation environment for future implementation of the Snapping Shrimp Resource Allocation Algorithm, consisting of a four by four grid where two autonomous agents travel randomly between nodes, pausing briefly before continuing to new locations. In later phases of this work, the Snapping Shrimp Resource Allocation Algorithm will be implemented to enable agents to allocate resources based on proximity, explosive location, and resolution time. The algorithm’s effectiveness will be evaluated based on the agents’ ability to resolve simulated improvised explosive devices before detonation, thereby reducing risk to military personnel, civilians, and equipment. By applying the Snapping Shrimp Resource Allocation Algorithm to a defense-oriented scenario, this research aims to assess the algorithm’s applicability to scenarios beyond the original case study. Continued validation across distinct operational contexts will support its advancement towards real-world applications in both civilian and military domains. "
Did this research project receive funding support from the Office of Undergraduate Research.
No
Included in
Development of an IED Response Model for Testing of the Snapping Shrimp Resource Allocation Algorithm
"Models must be capable of demonstrating adaptability across multiple case studies to confirm their reliability and versatility. The Snapping Shrimp Resource Allocation Algorithm, which has been successfully applied in a wildfire response scenario, models efficient, decentralized coordination inspired by the behavior of snapping shrimp. Building on this foundation, the current work explores the potential application of the Snapping Shrimp Resource Allocation algorithm to improvised explosive device response where rapid, autonomous coordination could significantly enhance safety and operational efficiency. Improvised explosive devices remain a persistent issue in modern conflict, requiring strategies for response that are cost-effective, scalable, and capable of adapting to dynamic environments. This case study establishes a foundational simulation environment for future implementation of the Snapping Shrimp Resource Allocation Algorithm, consisting of a four by four grid where two autonomous agents travel randomly between nodes, pausing briefly before continuing to new locations. In later phases of this work, the Snapping Shrimp Resource Allocation Algorithm will be implemented to enable agents to allocate resources based on proximity, explosive location, and resolution time. The algorithm’s effectiveness will be evaluated based on the agents’ ability to resolve simulated improvised explosive devices before detonation, thereby reducing risk to military personnel, civilians, and equipment. By applying the Snapping Shrimp Resource Allocation Algorithm to a defense-oriented scenario, this research aims to assess the algorithm’s applicability to scenarios beyond the original case study. Continued validation across distinct operational contexts will support its advancement towards real-world applications in both civilian and military domains. "