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The challenge of balancing search performance and energy efficiency in multiple unmanned aerial vehicles (multi-UAVs) cooperative search within dynamic, complex environments is addressed in this paper. A cooperative search algorithm combining distributed model predictive control with ant colony optimization is developed. It features a task-aware adaptive adjustment mechanism and a comprehensive reward function for improved coordination and efficiency. Simulation results indicate that unit energy consumption and turning rate are significantly reduced by the proposed method compared to baseline algorithms, with effectiveness demonstrated across scenarios with varying target density and dynamic obstacles.

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Distributed Task-Aware Cooperative Search for Multiple Unmanned Aerial Vehicles Based on Model Predictive Control

  • 会议论文

  • 发表日期：

    2026年

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  • Yizhe Li,
  • Lei Lian,
  • Yadong Zhao,
  • Feiyue Wu,
  • Bingyang Zhu,
  • Dong Wang

摘要

The challenge of balancing search performance and energy efficiency in multiple unmanned aerial vehicles (multi-UAVs) cooperative search within dynamic, complex environments is addressed in this paper. A cooperative search algorithm combining distributed model predictive control with ant colony optimization is developed. It features a task-aware adaptive adjustment mechanism and a comprehensive reward function for improved coordination and efficiency. Simulation results indicate that unit energy consumption and turning rate are significantly reduced by the proposed method compared to baseline algorithms, with effectiveness demonstrated across scenarios with varying target density and dynamic obstacles.