State-of-the-Art and Future Research Challenges in UAV Swarms

Javed, Sadaf; Hassan, Ali; Ahmad, Rizwan; Ahmed, Waqas; Ahmed, Rehan; Saadat, Ahsan; Guizani, Mohsen

doi:10.1109/jiot.2024.3364230

Cited by 5 publications

(1 citation statement)

References 247 publications

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“…With the advancement of industrial technology, unmanned aerial vehicles (UAVs) have become increasingly prevalent and have seen significant development across multiple fields [1][2][3][4][5]. Compared with traditional fixed cameras, UAVs offer unique advantages in the video field due to their distinctive high-altitude perspective, high mobility, and the ability to move vertically.…”

Section: Introductionmentioning

confidence: 99%

A Benchmark for UAV-View Natural Language-Guided Tracking

Li,

Liu,

2024

Electronics

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We propose a new benchmark, UAVNLT (Unmanned Aerial Vehicle Natural Language Tracking), for the UAV-view natural language-guided tracking task. UAVNLT consists of videos taken from UAV cameras from four cities for vehicles on city roads. For each video, vehicles’ bounding boxes, trajectories, and natural language are carefully annotated. Compared to the existing data sets, which are only annotated with bounding boxes, the natural language sentences in our data set can be more suitable for many application fields where humans take part in the system for that language, being not only more friendly for human–computer interaction but also capable of overcoming the appearance features’ low uniqueness for tracking. We tested several existing methods on our new benchmarks and found that the performance of the existing methods was not satisfactory. To pave the way for future work, we propose a baseline method suitable for this task, achieving state-of-the-art performance. We believe our new data set and proposed baseline method will be helpful in many fields, such as smart city, smart transportation, vehicle management, etc.

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Section: Introductionmentioning

confidence: 99%

A Benchmark for UAV-View Natural Language-Guided Tracking

Li,

Liu,

2024

Electronics

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Collaborative target assignment problem for large-scale UAV swarm based on two-stage greedy auction algorithm

Wang,

Wang

et al. 2024

Aerospace Science and Technology

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UAV swarm path planning approach based on integration of multi-population strategy and adaptive evolutionary optimizer

Wang,

Hu,

Gao

et al. 2024

Meas. Sci. Technol.

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Addressing the optimal path planning problem encountered by swarm of unmanned aerial vehicle (UAV) in three-dimensional space under multiple constraints, the Multi-population Adaptive Cuckoo Search and Grey Wolf Optimizer (MACSGWO) integrates Multi-Population (MP) strategies and adaptive evolutionary optimizer including the enhanced Adaptive Grey Wolf Optimizer (AGWO) and adaptive Cuckoo search (ACS). The optimizer strategically divides the initial population into multiple sub-groups, enabling each sub-group to independently iterate. During the iteration process, the algorithm adaptively adjusts parameters based on the optimal fitness values obtained by each sub-group after each iteration. The iteration cycle is divided into two stages: during the global exploration phase, each sub-group autonomously executes AGWO and periodically shares the fitness information of the Alpha wolf with other sub-groups, accelerating convergence. In the subsequent local optimization phase, MACSGWO dynamically decides whether to initiate ACS based on the disparity in the best fitness of each sub-group after each iteration, assisting the algorithm in escaping local optima. In experiments involving various complex benchmark functions and swarm path planning scenarios, MACSGWO demonstrated significant superiority in solution stability, convergence speed, and optimal convergence value compared to multiple existing variant algorithms. The integration of MACSGWO with the best relay UAV selection strategy further optimized the communication efficiency within the swarm. MACSGWO ensures the efficient resolution of UAV swarm path planning problems, providing robust support for optimization challenges in complex, multi-constraint scenarios.

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State-of-the-Art and Future Research Challenges in UAV Swarms

Cited by 5 publications

References 247 publications

A Benchmark for UAV-View Natural Language-Guided Tracking

A Benchmark for UAV-View Natural Language-Guided Tracking

Collaborative target assignment problem for large-scale UAV swarm based on two-stage greedy auction algorithm

UAV swarm path planning approach based on integration of multi-population strategy and adaptive evolutionary optimizer

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