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<p>This paper presents an integrated framework for lift prediction in bio-inspired flapping-wing micro air vehicles (FWMAVs) by synergistically combining image recognition, finite element analysis, and machine learning. FWMAVs offer advantages in agility and maneuverability, yet their lift generation remains limited due to complex structural deformation under aerodynamic loads. Unlike rigid-wing models that neglect the intrinsic flexibility and rib-membrane coupling of insect-inspired wings, our method explicitly incorporates these structural characteristics. A U-Net-based segmentation model is developed to automatically extract wing contours and rib distributions from Hymenoptera wing images, providing accurate morphological features for stiffness analysis. Furthermore, a modified MobileNetV2 architecture is employed to directly estimate bending and torsional stiffness from structural images, enabling efficient stiffness prediction. A stiffness-aware lift prediction model is subsequently proposed, accounting for deformation-induced variations in flapping and angle of attack. Experimental validation demonstrates that the proposed framework reduces prediction error by 69.72% compared to conventional rigid-wing models, achieving an average accuracy of 89.65%. This study provides a scalable and generalizable approach for lift prediction in deformable wings and offers practical insights for the design and optimization of lightweight, high-performance FWMAVs.</p>

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Lift prediction for micro flapping wings based on image-driven machine learning

  • 期刊论文

  • 发表日期：

    2026年5月4日

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  • Taishan Liu,
  • Rongbao Zeng,
  • Qingkun Zhao,
  • Hua Li,
  • Haofei Zhou

摘要

This paper presents an integrated framework for lift prediction in bio-inspired flapping-wing micro air vehicles (FWMAVs) by synergistically combining image recognition, finite element analysis, and machine learning. FWMAVs offer advantages in agility and maneuverability, yet their lift generation remains limited due to complex structural deformation under aerodynamic loads. Unlike rigid-wing models that neglect the intrinsic flexibility and rib-membrane coupling of insect-inspired wings, our method explicitly incorporates these structural characteristics. A U-Net-based segmentation model is developed to automatically extract wing contours and rib distributions from Hymenoptera wing images, providing accurate morphological features for stiffness analysis. Furthermore, a modified MobileNetV2 architecture is employed to directly estimate bending and torsional stiffness from structural images, enabling efficient stiffness prediction. A stiffness-aware lift prediction model is subsequently proposed, accounting for deformation-induced variations in flapping and angle of attack. Experimental validation demonstrates that the proposed framework reduces prediction error by 69.72% compared to conventional rigid-wing models, achieving an average accuracy of 89.65%. This study provides a scalable and generalizable approach for lift prediction in deformable wings and offers practical insights for the design and optimization of lightweight, high-performance FWMAVs.