Multi-Agent Coordination in Swarm Robotics for Search and Rescue Missions

Abstract

Search and rescue (SAR) operations in unstructured and disaster-prone environments pose immense challenges to human responders due to instability, limited accessibility, and the urgent need for rapid action. Swarm robotics, inspired by collective behavior in biological systems such as ants and bees, provides an innovative approach to enhancing SAR efforts. This paper explores decentralized control strategies and cooperative algorithms used in multi agent swarm systems, enabling autonomous robots to navigate, communicate, and coordinate in real-time. The primary focus lies in deploying these systems within complex terrains like collapsed buildings and dense forests, where centralized infrastructure is unreliable or absent. We analyze current methodologies in cooperative exploration, obstacle avoidance, task allocation, and real-time communication among agents. The study also presents simulation-based evaluations and illustrative diagrams to represent coordination strategies and obstacle negotiation in SAR environments.

Keywords: Decentralized control, cooperative behavior, swarm intelligence, real-time obstacle avoidance, multi-agent systems, disaster robotics.