Research on Multi-UAV Task Allocation Algorithm Considering Dynamic Priority Changes

He Yulai; Wu Hua; Zhang Weizheng

Research on Multi-UAV Task Allocation Algorithm Considering Dynamic Priority Changes

He Yulai, Wu Hua, Zhang Weizheng

Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, PMLR 245:373-382, 2024.

Abstract

In recent years, unmanned aerial vehicles (UAVs) have found increased application across various domains. Clusters of Multi-UAV can accomplish more complex tasks, optimizing overall efﬁciency through rational task allocation. In practical scenarios, they exhibit distinct advantages and characteristics. However, the allocation of tasks to Multi-UAV in special environments or emergency conditions poses a widely studied problem. Existing research or methods often impose ﬁxed task priorities (task sequences) during Multi-UAV task allocation. Yet, real-world UAV operations may witness ﬂuctuations in task priorities due to environmental changes or human factors. For instance, areas experiencing a drop in temperature may heighten the urgency of certain supplies, or sudden outbreaks of disease may increase the demand for medical supplies. In such cases, conventional planning methods become inadequate. Hence, this paper addresses these scenarios by proposing a model for dynamic task priority changes in Multi-UAV task allocation within special environments. This thesis introduces an improved genetic algorithm, termed the improved partheno geneticgreedy combination algorithm. Through comparative experiments, the efﬁcacy of the proposed algorithm in addressing dynamic priority changes in Multi-UAV collaborative task allocation problems is validated, enhancing problem-solving efﬁciency.

Cite this Paper

BibTeX


@InProceedings{pmlr-v245-yulai24a,
  title = 	 {Research on Multi-UAV Task Allocation Algorithm Considering Dynamic Priority Changes},
  author =       {Yulai, He and Hua, Wu and Weizheng, Zhang},
  booktitle = 	 {Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing},
  pages = 	 {373--382},
  year = 	 {2024},
  editor = 	 {Nianyin, Zeng and Pachori, Ram Bilas},
  volume = 	 {245},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {26--28 Apr},
  publisher =    {PMLR},
  pdf = 	 {https://raw.githubusercontent.com/mlresearch/v245/main/assets/yulai24a/yulai24a.pdf},
  url = 	 {https://proceedings.mlr.press/v245/yulai24a.html},
  abstract = 	 {In recent years, unmanned aerial vehicles (UAVs) have found increased application across various domains. Clusters of Multi-UAV can accomplish more complex tasks, optimizing overall efﬁciency through rational task allocation. In practical scenarios, they exhibit distinct advantages and characteristics. However, the allocation of tasks to Multi-UAV in special environments or emergency conditions poses a widely studied problem. Existing research or methods often impose ﬁxed task priorities (task sequences) during Multi-UAV task allocation. Yet, real-world UAV operations may witness ﬂuctuations in task priorities due to environmental changes or human factors. For instance, areas experiencing a drop in temperature may heighten the urgency of certain supplies, or sudden outbreaks of disease may increase the demand for medical supplies. In such cases, conventional planning methods become inadequate. Hence, this paper addresses these scenarios by proposing a model for dynamic task priority changes in Multi-UAV task allocation within special environments. This thesis introduces an improved genetic algorithm, termed the improved partheno geneticgreedy combination algorithm. Through comparative experiments, the efﬁcacy of the proposed algorithm in addressing dynamic priority changes in Multi-UAV collaborative task allocation problems is validated, enhancing problem-solving efﬁciency.}
}

Endnote

%0 Conference Paper
%T Research on Multi-UAV Task Allocation Algorithm Considering Dynamic Priority Changes
%A He Yulai
%A Wu Hua
%A Zhang Weizheng
%B Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing
%C Proceedings of Machine Learning Research
%D 2024
%E Zeng Nianyin
%E Ram Bilas Pachori	
%F pmlr-v245-yulai24a
%I PMLR
%P 373--382
%U https://proceedings.mlr.press/v245/yulai24a.html
%V 245
%X In recent years, unmanned aerial vehicles (UAVs) have found increased application across various domains. Clusters of Multi-UAV can accomplish more complex tasks, optimizing overall efﬁciency through rational task allocation. In practical scenarios, they exhibit distinct advantages and characteristics. However, the allocation of tasks to Multi-UAV in special environments or emergency conditions poses a widely studied problem. Existing research or methods often impose ﬁxed task priorities (task sequences) during Multi-UAV task allocation. Yet, real-world UAV operations may witness ﬂuctuations in task priorities due to environmental changes or human factors. For instance, areas experiencing a drop in temperature may heighten the urgency of certain supplies, or sudden outbreaks of disease may increase the demand for medical supplies. In such cases, conventional planning methods become inadequate. Hence, this paper addresses these scenarios by proposing a model for dynamic task priority changes in Multi-UAV task allocation within special environments. This thesis introduces an improved genetic algorithm, termed the improved partheno geneticgreedy combination algorithm. Through comparative experiments, the efﬁcacy of the proposed algorithm in addressing dynamic priority changes in Multi-UAV collaborative task allocation problems is validated, enhancing problem-solving efﬁciency.

APA


Yulai, H., Hua, W. & Weizheng, Z.. (2024). Research on Multi-UAV Task Allocation Algorithm Considering Dynamic Priority Changes. Proceedings of 2024 International Conference on Machine Learning and Intelligent Computing, in Proceedings of Machine Learning Research 245:373-382 Available from https://proceedings.mlr.press/v245/yulai24a.html.

Related Material

Download PDF