提出曲率感知校准方法，让柔性触觉传感器在曲面上更准地测力。
Curvature-Aware Calibration of Tactile Sensors for Accurate Force Estimation on Non-Planar Surfaces
- 用神经网络从无负载输出预测局部曲率，R2达0.91
- 在2~8N力下，曲面校准比平面校准误差小30%以上
- 适合机器人抓手、假肢等需贴合曲面的设备使用
柔性触觉传感器在机器人夹爪、假肢手、可穿戴手套和辅助设备中应用日益广泛，需贴合曲面与不规则表面。然而，现有传感器大多仅在平面基底上校准，一旦安装到曲面，其精度和一致性显著下降，限制了实际可靠性。为此，我们针对一种广泛应用的电阻式触觉传感器设计，开发了适用于一维曲面的校准模型。通过训练多层感知机神经网络，从无负载时的基准输出预测局部曲率，达到0.91的R²得分。该方法在五种日常物体上验证，施加2~8N的力，结果表明：曲率感知校准在所有曲面上保持稳定的力估计精度，而平面校准随曲率增加显著低估力值。结果证明，曲率感知建模提升了柔性触觉传感器的准确性、一致性和可靠性，支持其在真实场景中的可靠应用。
原文摘要 · Abstract (English)
Flexible tactile sensors are increasingly used in real-world applications such as robotic grippers, prosthetic hands, wearable gloves, and assistive devices, where they need to conform to curved and irregular surfaces. However, most existing tactile sensors are calibrated only on flat substrates, and their accuracy and consistency degrade once mounted on curved geometries. This limitation restricts their reliability in practical use. To address this challenge, we develop a calibration model for a widely used resistive tactile sensor design that enables accurate force estimation on one-dimensional curved surfaces. We then train a neural network (a multilayer perceptron) to predict local curvature from baseline sensor outputs recorded under no applied load, achieving an R2 score of 0.91. The proposed approach is validated on five daily objects with varying curvatures under forces from 2 N to 8 N. Results show that the curvature-aware calibration maintains consistent force accuracy across all surfaces, while flat-surface calibration underestimates force as curvature increases. Our results demonstrate that curvature-aware modeling improves the accuracy, consistency, and reliability of flexible tactile sensors, enabling dependable performance across real-world applications.
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