Alqefari Shahad, Menai Mohamed El Bachir
Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11451, Saudi Arabia.
Department of Computer Science, College of Computer and Information Science, Imam Mohammed Ibn Saud Islamic University, Riyadh 11564, Saudi Arabia.
Sensors (Basel). 2025 Apr 16;25(8):2502. doi: 10.3390/s25082502.
In the rapidly evolving field of aerial robotics, the coordinated management of multiple unmanned aerial vehicle (multi-UAV) systems to address complex and dynamic environments is increasingly critical. Multi-UAV systems promise enhanced efficiency and effectiveness in various applications, from disaster response to infrastructure inspection, by leveraging the collective capabilities of UAV fleets. However, the dynamic nature of such environments presents significant challenges in task allocation and real-time adaptability. This paper introduces a novel hybrid algorithm designed to optimize multi-UAV task assignments in dynamic environments. State-of-the-art solutions in this domain have exhibited limitations, particularly in rapidly responding to dynamic changes and effectively scaling to large-scale environments. The proposed solution bridges these gaps by combining clustering to group and assign tasks in an initial offline phase with a dynamic partial reassignment process that locally updates assignments in response to real-time changes, all within a centralized-distributed communication topology. The simulation results validate the superiority of the proposed solution and demonstrate its improvements in efficiency and responsiveness over existing solutions. Additionally, the results highlight the scalability of the solution in handling large-scale problems and demonstrate its ability to efficiently manage a growing number of UAVs and tasks. It also demonstrated robust adaptability and enhanced mission effectiveness across a wide range of dynamic events and different scale scenarios.
在快速发展的空中机器人领域，协调管理多无人机系统以应对复杂多变的环境变得越来越关键。多无人机系统通过利用无人机机群的集体能力，有望在从灾难响应到基础设施检查等各种应用中提高效率和效能。然而，此类环境的动态特性在任务分配和实时适应性方面带来了重大挑战。本文介绍了一种新颖的混合算法，旨在优化动态环境中的多无人机任务分配。该领域的现有先进解决方案存在局限性，尤其是在快速响应动态变化以及有效扩展至大规模环境方面。所提出的解决方案通过在初始离线阶段结合聚类来分组和分配任务，并通过动态部分重新分配过程来响应实时变化在本地更新任务分配，所有这些都在集中 - 分布式通信拓扑结构内，弥补了这些差距。仿真结果验证了所提解决方案的优越性，并展示了其在效率和响应性方面相对于现有解决方案的改进。此外，结果突出了该解决方案在处理大规模问题时的可扩展性，并展示了其有效管理越来越多的无人机和任务的能力。它还在广泛的动态事件和不同规模场景中展示了强大的适应性和增强的任务效能。
