Robotics-Driven Swarm Intelligence for Adaptive and Resilient Pandemic Alleviation in Urban Ecosystems: Advancing Distributed Automation and Intelligent Decision-Making Processes

Abstract

Background Information: Pandemics pose serious problems for urban environments because of overburdened healthcare systems and limited resources. By facilitating effective resource allocation and decision-making, technologies such as robots and swarm intelligence can improve these systems' resilience and adaptability. Objectives: This research aims to improve decision-making through distributed automation, optimize task efficiency in managing pandemic situations, develop swarm intelligence models
for effective pandemic response, and integrate AI for real-time anomaly detection. Methods: Robotics and AI-based anomaly detection are combined with swarm intelligence algorithms to produce real-time, adaptive systems. Urban healthcare systems use distributed
automation to process data and complete tasks efficiently. Empirical results: Swarm intelligence improves real-time decision-making and crisis management during pandemics, and the results show notable gains in task efficiency, accuracy, and resource utilization. Conclusion: Urban pandemic management and decision-making are greatly enhanced by robotics-driven swarm intelligence, which offers scalable solutions for real-time reaction