Soft sensors have been playing a crucial role in detecting different types of physical stimuli to part or the entire body of a robot, analogous to mechanoreceptors or proprioceptors in biology. Most of the currently available soft sensors with compact form factors can detect only a single deformation mode at a time due to the limitation in combining multiple sensing mechanisms in a limited space. However, realizing multiple modalities in a soft sensor without increasing its original form factor is beneficial, because even a single input stimulus to a robot may induce a combination of multiple modes of deformation. Here, we report a multifunctional soft sensor capable of decoupling combined deformation modes of stretching, bending, and compression, as well as detecting individual deformation modes, in a compact form factor. The key enabling design feature of the proposed sensor is a combination of heterogeneous sensing mechanisms: optical, microfluidic, and piezoresistive sensing. We characterize the performance on both detection and decoupling of deformation modes, by implementing both a simple algorithm of threshold evaluation and a machine learning technique based on an artificial neural network. The proposed soft sensor is able to estimate eight different deformation modes with accuracies higher than 95%. We lastly demonstrate the potential of the proposed sensor as a method of human-robot interfaces with several application examples highlighting its multifunctionality.

类似于生物学中的机械感受器或本体感受器，软传感器在检测机器人的部分或整个身体的不同类型的物理刺激中起着至关重要的作用。由于在有限的空间中组合多个传感机制的限制，当前大多数具有紧凑形状因数的软传感器一次只能检测到一个变形模式。但是，在不增加其原始形状系数的情况下在软传感器中实现多种模式是有益的，因为即使对机器人的单个输入刺激也可能导致多种变形模式的组合。在这里，我们报告了一种多功能的软传感器，它能够以紧凑的形式将拉伸，弯曲和压缩的组合变形模式解耦，以及检测单个变形模式。提出的传感器的关键使能设计功能是异类传感机制的组合：光学，微流体和压阻传感。我们通过实现简单的阈值评估算法和基于人工神经网络的机器学习技术，来表征变形模式的检测和去耦性能。所提出的软传感器能够估计八种不同的变形模式，其准确度高于95％。我们最后展示了所提出的传感器作为人机界面方法的潜力，并通过几个应用实例来突出其多功能性。我们通过实现简单的阈值评估算法和基于人工神经网络的机器学习技术，来表征变形模式的检测和去耦性能。所提出的软传感器能够估计八种不同的变形模式，其准确度高于95％。我们最后展示了所提出的传感器作为人机界面方法的潜力，并通过几个应用实例来突出其多功能性。我们通过实现简单的阈值评估算法和基于人工神经网络的机器学习技术，来表征变形模式的检测和去耦性能。所提出的软传感器能够估计八种不同的变形模式，其准确度高于95％。我们最后展示了所提出的传感器作为人机界面方法的潜力，并通过几个应用实例来突出其多功能性。