This paper introduces a novel Soft Acoustic Curvature (SAC) sensor. SAC incorporates integrated audio components and features an acoustic channel within a flexible structure. A reference acoustic wave, generated by a speaker at one end of the channel, propagates and is received by a microphone at the other channel's end. Our previous study revealed that acoustic wave energy dissipation varies with acoustic channel deformation, leading us to design a novel channel capable of large deformation due to bending. We then use Machine Learning (ML) models to establish a complex mapping between channel deformations and sound modulation. Various sound frequencies and ML models were evaluated to enhance curvature detection accuracy. The sensor, constructed using soft material and 3D printing, was validated experimentally, with curvature measurement errors remaining within 3.5 m-1 for a range of 0 to 60 m-1 curvatures. These results demonstrate the effectiveness of the proposed method for estimating curvatures. With its flexible structure, the SAC sensor holds potential for applications in soft robotics, including shape measurement for continuum manipulators, soft grippers, and wearable devices.

翻译：本文介绍了一种新型柔性声学曲率传感器。该传感器集成了音频组件，并在其柔性结构内部设计了一个声学通道。一个由通道一端的扬声器产生的参考声波在通道中传播，并由通道另一端的麦克风接收。我们前期的研究表明，声波能量耗散随声学通道的形变而变化，这促使我们设计了一种能够因弯曲而产生大形变的新型通道。随后，我们利用机器学习模型，在通道形变与声音调制之间建立起复杂的映射关系。为提升曲率检测精度，我们评估了多种声波频率和机器学习模型。该传感器采用柔性材料和3D打印技术制造，并通过实验验证了其性能：在0至60 m⁻¹的曲率范围内，其曲率测量误差保持在3.5 m⁻¹以内。这些结果证明了所提方法在曲率估计方面的有效性。凭借其柔性结构，该传感器在软体机器人领域具有应用潜力，例如连续体机械臂的形状测量、软体抓取器以及可穿戴设备。