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<p>Effective task assignment technology plays a pivotal role in optimizing Unmanned Aerial Vehicles operations during the collaboration of multiple Unmanned Aerial Vehicles (multi-UAV) in combat scenarios. Therefore, aiming at the cooperative task assignment of multi-UAV, this paper takes the value and time window of the ground target into consideration, takes the total fuel consumption, execution time, and execution cost of all tasks completed by multi-UAV as the objective functions, constructs a multi-objective multi-task assignment mathematical model, and proposes a multi-strategy improved Dung Beetle Optimizer (MIDBO) to solve the model. The MIDBO employs Sinusoidal chaotic mapping to generate the initial population, enhancing population diversity. Additionally, it integrates the nonlinear convergence factor and spiral search factor to augment the exploration capabilities of the rolling dung beetles. Moreover, by incorporating the subtraction average strategy, the algorithm bolsters the prowess of the foraging dung beetles, leading to improved algorithmic performance and attaining high-quality solutions. The experimental results show that the multi-UAV collaborative task assignment based on the MIDBO can enhance the global optimization ability and assign the optimal task sequence to multi-UAV.</p>

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Multi-UAV cooperative task assignment based on multi-strategy improved DBO

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年1月21日

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  • Ran Zhang,
  • Xiao Chen,
  • Maoyuan Li

摘要

Effective task assignment technology plays a pivotal role in optimizing Unmanned Aerial Vehicles operations during the collaboration of multiple Unmanned Aerial Vehicles (multi-UAV) in combat scenarios. Therefore, aiming at the cooperative task assignment of multi-UAV, this paper takes the value and time window of the ground target into consideration, takes the total fuel consumption, execution time, and execution cost of all tasks completed by multi-UAV as the objective functions, constructs a multi-objective multi-task assignment mathematical model, and proposes a multi-strategy improved Dung Beetle Optimizer (MIDBO) to solve the model. The MIDBO employs Sinusoidal chaotic mapping to generate the initial population, enhancing population diversity. Additionally, it integrates the nonlinear convergence factor and spiral search factor to augment the exploration capabilities of the rolling dung beetles. Moreover, by incorporating the subtraction average strategy, the algorithm bolsters the prowess of the foraging dung beetles, leading to improved algorithmic performance and attaining high-quality solutions. The experimental results show that the multi-UAV collaborative task assignment based on the MIDBO can enhance the global optimization ability and assign the optimal task sequence to multi-UAV.