A YOLO-Based Machine Learning Framework for Detection of Soft Pneumatic Actuator Bending Angles

Syahirul Alim Ritonga; Raditya Fadhil Arva; Sarah Iftin Atsani; Mohammad Ardyansah; Herianto

doi:10.31572/inotera.Vol10.Iss1.2025.ID448

Syahirul Alim Ritonga Departement of Mechanical and Industrial Engineering, Universitas Gadjah Mada
Raditya Fadhil Arva Departement of Mechanical and Industrial Engineering, Universitas Gadjah Mada
Sarah Iftin Atsani Center of Additive Manufacturing and Systems, Universitas Gadjah Mada
Mohammad Ardyansah Departement of Mechanical and Industrial Engineering, Universitas Gadjah Mada
Herianto Departement of Mechanical and Industrial Engineering, Universitas Gadjah Mada

DOI: https://doi.org/10.31572/inotera.Vol10.Iss1.2025.ID448

Keywords: soft pneumatic actuator, soft robot, Yolo, machine learning, artificial intelligence

Abstract

The bending angle of soft pneumatic actuator (SPA) is a critical parameter influencing their reliability and effectiveness across various applications. Conventional measurement methods are often labour-intensive and impractical for experiments requiring multiple trials, creating a need for efficient, non-invasive techniques. This study proposes a machine learning framework leveraging YOLO (You Only Look Once) models to detect SPA bending angles from image data, eliminating the need for additional hardware. A comprehensive dataset of SPAs under varying actuation pressures, with meticulously labelled bending angles, was curated to train a YOLO-based regression model. The results highlight the model's strong performance, achieving a recall of 99.1%, precision of 70%, and mean average precision (mAP) scores of 86.42% (IoU 0.5) and 84.35% (IoU 0.5–0.95). Low training and validation losses indicate high accuracy in bounding box predictions, object-background differentiation, and object classification. Optimized learning rates ensured efficient parameter updates, achieving convergence without overfitting. The proposed framework demonstrates a robust balance between accuracy, robustness, and efficiency, making it a practical solution for reliable SPA bending angle detection in real-world applications. This study underscores the potential of machine learning-driven techniques to streamline SPA characterization, offering a scalable and non-invasive alternative to traditional methods.

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