We address the challenge of reliable and accurate proprioception in soft robots, specifically those with tight packaging constraints and relying only on internally embedded sensors. While various sensing approaches with single sensors have been tried, often with a constant curvature assumption, we look into sensing local deformations at multiple locations of the sensor. In our approach, we multi-tap an off-the-shelf resistive sensor by creating multiple electrical connections onto the resistive layer of the sensor and we insert the sensor into a soft body. This modification allows us to measure changes in resistance at multiple segments throughout the length of the sensor, providing improved resolution of local deformations in the soft body. These measurements inform a model based on a finite element method (FEM) that estimates the shape of the soft body and the magnitude of an external force acting at a known arbitrary location. Our model-based approach estimates soft body deformation with approximately 3% average relative error while taking into account internal fluidic actuation. Our estimate of external force disturbance has an 11% relative error within a range of 0 to 5 N. The combined sensing and modeling approach can be integrated, for instance, into soft manipulation platforms to enable features such as identifying the shape and material properties of an object being grasped. Such manipulators can benefit from the inherent softness and compliance while being fully proprioceptive, relying only on embedded sensing and not on external systems such as motion capture. Such proprioception is essential for the deployment of soft robots in real-world scenarios.

翻译：我们解决了软体机器人中可靠且精确的本体感知难题，特别是那些具有紧凑封装约束且仅依赖内部嵌入传感器的场景。尽管已有研究尝试基于单一传感器的多种感知方法（通常假设恒定曲率），但本研究探索了在传感器多个位置感知局部变形的方法。我们通过在市售电阻传感器的电阻层上创建多个电气连接点实现多触点化，并将该传感器嵌入软体结构中。这一改进使我们能够测量传感器全长各段电阻的变化，从而提升软体局部变形的解析精度。这些测量数据用于指导基于有限元方法的模型，该模型可估计软体形态及作用于已知任意位置的外力大小。我们的模型驱动方法在考虑内部流体驱动的前提下，估计软体变形的平均相对误差约为3%。在0至5N范围内，外力扰动估计的相对误差为11%。这种传感与建模的联合方法可集成至软体操作平台中，例如实现抓取物体形状与材料属性的识别。此类操作器可兼顾内在柔顺性与完全本体感知能力，仅依赖嵌入式传感器而非运动捕捉等外部系统。这种本体感知能力对于软体机器人在现实场景中的部署至关重要。