Adaptive Neural-Controlled Swarm Robotics for Dynamic Disaster Response

Abstract

Swarm robotics has emerged as disruptive paradigm of large and complex tasks in very unpredictable settings such as disaster zones. The dynamics of the circumstances that may happen during earthquakes, floods, or explosions during the industry can be easily misinterpreted by conventional robotic mechanisms due to their unpredictability. But the adaptive neural-controlled swarm robotics applies the distributed artificial intelligence, self-organization and biologically inspired learning in enhancing autonomous decision-making and coordination. Such an approach can empower individual agents to handle the sensory data, dynamically control their actions, and coordinate their movements in the unfriendly or even inaccessible to humans environments. Swarm systems can offer real-time reactivity and robustness that is imminently beyond the ability of centralized robotic systems due to neural control structures offering quick pattern identification, obstacle avoidance and distributive adaptive tasks. The ability to learn new situations and demonstrate good collective performances is also a benefit to reinforcement learning and spiking neural networks.The present day interest in neural-controlled swarms is a pointer of the looming need of scalable, efficient, and self-reliant technologies that can benefit the groups of disaster responders. As a result of the emergence of climate-related disasters in the global environment, the autonomy of robots in searching people, scanning the area of devastation, and providing life-saving solutions are deemed relevant in case of the emergence of an emergency. This paper describes the design, functionality and performance of adaptive swarms controlled by neural networks in regard to emergent coordination, learning processes and environmental adaptability. The article presents the state-of-the-art architectures and examines their functionality in dynamic disaster scenarios as well as identifies technical challenges such as communication problems, energy problems and accuracy of real-time solutions. The results suggest the possibility of the impact of neural-controlled swarms to transform disaster response by performing autonomous interventions more quickly, safely, and intelligibly.