The rapid advancement of unmanned aerial vehicles (UAVs) has transformed a wide range of applications, including military operations, disaster response, agricultural monitoring, and infrastructure inspection. Deploying multiple UAVs to work collaboratively offers significant advantages in terms of enhanced coverage, redundancy, and operational efficiency. However, as UAV missions become more complex and operate in dynamic environments, the task assignment problem becomes increasingly challenging. Multi-UAV dynamic task assignment is critical for optimizing mission success. It involves allocating tasks to UAVs in real-time while adapting to unpredictable changes, such as sudden task appearances, UAV failures, and varying mission requirements. A key contribution of this article is that it provides a comprehensive study of state-of-the-art solutions for dynamic task assignment in multi-UAV systems from 2013 to 2024. It also introduces a comparative framework to evaluate algorithms based on metrics such as responsiveness, robustness, and scalability in handling real-world dynamic conditions. Our analysis reveals distinct strengths and limitations across three major approaches: market-based, intelligent optimization, and clustering-based solutions. Market-based solutions excel in distributed coordination and real-time adaptability, but face challenges with communication overhead. Intelligent optimization solutions, including evolutionary and swarm intelligence, provide high flexibility and performance in complex scenarios but require significant computational resources. Clustering-based solutions efficiently group and allocate tasks geographically, reducing overlap and improving efficiency, although they struggle with adaptability in dynamic environments. By identifying these strengths, limitations, and emerging trends, this article not only offers a detailed comparative analysis but also highlights critical research gaps. Specifically, it underscores the need for scalable algorithms that can efficiently handle larger UAV fleets, robust methods to adapt to sudden task changes and UAV failures, and multi-objective optimization frameworks to balance competing goals such as energy efficiency and task completion. These insights serve as a guide for future research and a valuable resource for developing resilient and efficient strategies for multi-UAV dynamic task assignment in complex environments.

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.