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The low-altitude economy is empowering various industries, with UAV (Unmanned Aerial Vehicle) serving as the primary technological support. Relevant technical theories present new challenges for engineering talent cultivation in universities. This paper proposes an innovative teaching framework that integrates the PDCA (Plan-Do-Check-Act) cycle with DRL (Deep Reinforcement Learning). This framework continuously optimizes teaching objectives, content, and methods through the PDCA+DRL method, and uses DRL in the evaluation phase to enhance multi-dimensional assessment capabilities. Statistical analysis of UAV teaching practice cases indicates that this model can improve teaching effectiveness and provides a reference for teaching reform for UAV-related courses in low-altitude economy.

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Teaching Framework for UAV-Related Courses in Low-Altitude Economy Based on PDCA Cycle and DRL

  • 会议论文

  • 发表日期：

    2026年

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  • Yunchong Guan,
  • Liang Zhao,
  • Xiguang Li,
  • Junling Shi,
  • Qian Wang,
  • Haibo Yang,
  • Ammar Hawbani,
  • Hongkun Qiu,
  • Yipeng Cao,
  • Yanju Dong,
  • Guiying Meng,
  • Na Lin,
  • Yunhe Sun,
  • Cunqian Yu

摘要

The low-altitude economy is empowering various industries, with UAV (Unmanned Aerial Vehicle) serving as the primary technological support. Relevant technical theories present new challenges for engineering talent cultivation in universities. This paper proposes an innovative teaching framework that integrates the PDCA (Plan-Do-Check-Act) cycle with DRL (Deep Reinforcement Learning). This framework continuously optimizes teaching objectives, content, and methods through the PDCA+DRL method, and uses DRL in the evaluation phase to enhance multi-dimensional assessment capabilities. Statistical analysis of UAV teaching practice cases indicates that this model can improve teaching effectiveness and provides a reference for teaching reform for UAV-related courses in low-altitude economy.