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In this study, we present a novel hybrid algorithm, combining Levy Flight(LF) and Particle Swarm Optimization (PSO) (LF-PSO), tailored for efficientmulti-robot exploration in unknown environments with limited communication andno global positioning information. The research addresses the growing interestin employing multiple autonomous robots for exploration tasks, particularly inscenarios such as Urban Search and Rescue (USAR) operations. Multiple robotsoffer advantages like increased task coverage, robustness, flexibility, andscalability. However, existing approaches often make assumptions such as searcharea, robot positioning, communication restrictions, and target informationthat may not hold in real-world situations. The hybrid algorithm leverages LF,known for its effectiveness in large space exploration with sparse targets, andincorporates inter-robot repulsion as a social component through PSO. Thiscombination enhances area exploration efficiency. We redefine the local bestand global best positions to suit scenarios without continuous targetinformation. Experimental simulations in a controlled environment demonstratethe algorithm's effectiveness, showcasing improved area coverage compared totraditional methods. In the process of refining our approach and testing it incomplex, obstacle-rich environments, the presented work holds promise forenhancing multi-robot exploration in scenarios with limited information andcommunication capabilities.

From Simulations to Reality: Enhancing Multi-Robot Exploration for Urban Search and Rescue