In this study, we present a novel hybrid algorithm, combining Levy Flight (LF) and Particle Swarm Optimization (PSO) (LF-PSO), tailored for efficient multi-robot exploration in unknown environments with limited communication and no global positioning information. The research addresses the growing interest in employing multiple autonomous robots for exploration tasks, particularly in scenarios such as Urban Search and Rescue (USAR) operations. Multiple robots offer advantages like increased task coverage, robustness, flexibility, and scalability. However, existing approaches often make assumptions such as search area, robot positioning, communication restrictions, and target information that may not hold in real-world situations. The hybrid algorithm leverages LF, known for its effectiveness in large space exploration with sparse targets, and incorporates inter-robot repulsion as a social component through PSO. This combination enhances area exploration efficiency. We redefine the local best and global best positions to suit scenarios without continuous target information. Experimental simulations in a controlled environment demonstrate the algorithm's effectiveness, showcasing improved area coverage compared to traditional methods. In the process of refining our approach and testing it in complex, obstacle-rich environments, the presented work holds promise for enhancing multi-robot exploration in scenarios with limited information and communication capabilities.

翻译：本研究提出了一种融合莱维飞行（LF）与粒子群优化（PSO）的新型混合算法（LF-PSO），专为在缺乏全局定位信息和有限通信条件下的未知环境中实现高效多机器人探索而设计。该研究回应了在多自主机器人探索任务中日益增长的应用需求，特别是在城市搜救（USAR）等场景中。多机器人系统具有任务覆盖范围广、鲁棒性强、灵活度高和可扩展性好等优势。然而，现有方法通常对搜索区域、机器人定位、通信限制和目标信息做出假设，这些假设在现实场景中可能不成立。所提混合算法利用LF在稀疏目标的广阔空间探索中的有效性，并通过PSO将机器人间互斥性作为社会性组分融入其中。这种结合提升了区域探索效率。我们重新定义了局部最优和全局最优位置，以适应缺乏持续目标信息的场景。受控环境下的仿真实验表明，该算法相较于传统方法实现了更优的区域覆盖率。在通过复杂障碍物密集环境进行算法优化与测试的过程中，本工作为提升有限信息与通信能力场景下的多机器人探索性能提供了可行方案。