Multimodal Sensor Fusion and Adaptive Coordination Algorithms for Swarm Robotics in Disaster Response Environments.

Abstract

The increasing frequency of natural and man-made disasters highlights the urgent need for efficient response systems capable of navigating complex and hazardous environments. Swarm robotics, combined with advanced multimodal sensor fusion and adaptive coordination algorithms, offers a novel approach to addressing these challenges. This research explores the integration of diverse sensor modalities—such as thermal imaging, LiDAR, and acoustic data—into swarm robotic systems to improve real-time situational awareness and decision-making. Furthermore, we propose an adaptive coordination framework that optimizes robotic deployment, energy usage, and communication during disaster missions. Through a combination of simulations and physical experiments, the proposed system demonstrates notable advancements in victim detection accuracy, environmental mapping, and energy efficiency compared to existing methodologies. The findings of this study present a scalable and effective solution for deploying robotic swarms in disaster response scenarios, offering significant contributions to the fields of robotics and emergency management.