Paper Contents
Abstract
Natural and artificial disastersincluding earthquakes, floods, wildfires, and structural collapsespose severe obstacles for conventional human-led search and rescue (SAR) operations. These efforts are often constrained by time, safety risks, limited visibility, and difficult terrain. In such high-risk environments, swarm robotics has emerged as a compelling solution, enabling decentralized, scalable, and resilient coordination through autonomous multi-robot systems. Drawing inspiration from the collective behavior of organisms like ants, birds, and bees, swarm robotics utilizes simple, autonomous units that cooperate to accomplish complex objectives without centralized oversight.This paper examines the development and application of swarm robotic systems in SAR missions. It outlines how robot swarms adapt to unpredictable conditions, explore disaster zones autonomously, map disordered terrain, detect human presence via onboard sensors, and coordinate actions in real time through distributed communication and task-sharing strategies. The study also delves into the foundational concepts of swarm intelligencesuch as self-organization, local interaction, and emergent behaviorand explores how these ideas are implemented through algorithms like Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and behavior-based coordination models.A major advantage of swarm systems is their inherent fault tolerance. Unlike centralized frameworks, swarm-based networks maintain functionality even when individual robots fail, a critical trait in hazardous environments. Their modular nature also ensures scalability, as new units can be added or replaced without system-wide reconfiguration. This flexibility makes swarm robotics suitable for a range of mission scales, from confined building interiors to large urban disaster zones.Furthermore, the paper investigates communication mechanisms within swarms, particularly in GPS-denied environments. It evaluates local communication methods such as infrared, Bluetooth, and acoustic signaling, along with stigmergy-based approacheswhere robots leave environmental markers to influence peer behavior. These bio-inspired strategies promote a dynamic and robust response to rapidly evolving SAR scenarios.
Copyright
Copyright © 2025 Anand Jha. This is an open access article distributed under the Creative Commons Attribution License.
