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Article | Open Access

Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods

Mengzhen Li1, Na Li2, Xiaoyu Shao1, Jiahe Wang1, Dachuan Xu1( )
Department of Operations Research and Information Engineering, Beijing University of Technology, Beijing 100124, China
Beijing Jinghang Research Institute of Computing and Communication, Beijing 100074, China
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Abstract

Heterogeneous unmanned aerial vehicle (UAV) swarms have garnered significant attention from researchers worldwide due to their remarkable flexibility, diverse mission capabilities, and wide-ranging potential applications. Mission planning stands at the core of UAV swarm operations, requiring consideration of various factors including mission environment, requirements, and inherent characteristics. In this paper, we investigate the model of the cooperative tasking problem in heterogeneous UAV swarms. We provide a comprehensive review of artificial intelligence algorithms applied in UAV swarm mission planning, analyzing their strengths and weaknesses in multi-UAV cooperative environments. By discussing these key techniques and their practical applications, the article highlights future research trends and challenges. This review serves as a valuable reference for understanding the current state of AI algorithm applications in heterogeneous UAV swarm task assignments.

Keywords

heterogeneous unmanned aerial vehicles (UAVs)collaborative task assignmentartificial intelligence methods

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CAAI Artificial Intelligence Research
Volume 3,
2024
Article number: 9150033
DOI: 10.26599/AIR.2024.9150033
Cite this article:
Li M, Li N, Shao X, et al. Survey on Collaborative Task Assignment for Heterogeneous UAVs Based on Artificial Intelligence Methods. CAAI Artificial Intelligence Research, 2024, 3: 9150033. https://doi.org/10.26599/AIR.2024.9150033
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Machine Learning and Optimization

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Received: 31 October 2023
Revised: 22 January 2024
Accepted: 22 February 2024
Published: 08 May 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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