Real-Time Disaster Response and Recovery by using Neuromorphic Computing-Enabled Autonomous Agents

This paper presents a practical approach to disaster response and recovery through the integration of neuromorphic computing, autonomous agents, and energy-efficient processing systems. Neuromorphic computing, inspired by the human brain’s architecture, enables ultralow power consumption while maintaining high-speed, real-time processing capabilities essential for disaster scenarios. The proposed system deploys Spiking Neural Networks (SNNs) running on neuromorphic hardware platforms like Brain Chip Akida, combined with event-driven sensors including Dynamic Vision Sensors (DVS) and Silicon Cochlea for audio processing. These autonomous agents can operate independently for extended periods without external power sources or internet connectivity, making them ideal for disaster environments where traditional infrastructure fails. The system integrates multi-agent coordination protocols, explainable AI for trustworthy decision-making, and real-time adaptation through spike-timing-dependent plasticity (STDP). The neuromorphic approach addresses critical limitations of traditional disaster response systems including high power consumption, infrastructure dependency, and limited operational duration. This research contributes to advancing sustainable AI for humanitarian applications, potentially revolutionizing emergency response capabilities and saving lives through more effective, autonomous disaster management systems.