Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions
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 e...
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MDPI AG
2025-01-01
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| Series: | Drones |
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| Online Access: | https://www.mdpi.com/2504-446X/9/1/75 |
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| author | Shahad Alqefari Mohamed El Bachir Menai |
| author_facet | Shahad Alqefari Mohamed El Bachir Menai |
| author_sort | Shahad Alqefari |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f49ba08717ce4766be431fdc031652dc |
| institution | Kabale University |
| issn | 2504-446X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Drones |
| spelling | doaj-art-f49ba08717ce4766be431fdc031652dc2025-01-24T13:29:53ZengMDPI AGDrones2504-446X2025-01-01917510.3390/drones9010075Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future DirectionsShahad Alqefari0Mohamed El Bachir Menai1Department of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11451, Saudi ArabiaDepartment of Computer Science, College of Computer and Information Science, King Saud University, Riyadh 11451, Saudi ArabiaThe 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.https://www.mdpi.com/2504-446X/9/1/75multi-UAV systemsdynamic task allocationhybrid algorithmUAV clustering techniquesreal-time UAV coordination |
| spellingShingle | Shahad Alqefari Mohamed El Bachir Menai Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions Drones multi-UAV systems dynamic task allocation hybrid algorithm UAV clustering techniques real-time UAV coordination |
| title | Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions |
| title_full | Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions |
| title_fullStr | Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions |
| title_full_unstemmed | Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions |
| title_short | Multi-UAV Task Assignment in Dynamic Environments: Current Trends and Future Directions |
| title_sort | multi uav task assignment in dynamic environments current trends and future directions |
| topic | multi-UAV systems dynamic task allocation hybrid algorithm UAV clustering techniques real-time UAV coordination |
| url | https://www.mdpi.com/2504-446X/9/1/75 |
| work_keys_str_mv | AT shahadalqefari multiuavtaskassignmentindynamicenvironmentscurrenttrendsandfuturedirections AT mohamedelbachirmenai multiuavtaskassignmentindynamicenvironmentscurrenttrendsandfuturedirections |