Accurate shape sensing, only achievable through distributed proprioception, is a key requirement for closed-loop control of soft robots. Low-cost power efficient optoelectronic sensors manufactured from flexible materials represent a natural choice as they can cope with the large deformations of soft robots without loss of performance. However, existing integration approaches are cumbersome and require manual steps and complex assembly. We propose a semi-automated printing process where plastic optical fibers are embedded with readout electronics in 3D printed flexures. The fibers become locked in place and the readout electronics remain optically coupled to them while the flexures undergo large bending deformations, creating a repeatable, monolithically manufactured bending transducer with only 10 minutes required in total for the manual embedding steps. We demonstrate the process by manufacturing multi-material 3D printed fingers and extensively evaluating the performance of each proprioceptive joint. The sensors achieve 70% linearity and 4.81{\deg} RMS error on average. Furthermore, the distributed architecture allows for maintaining an average fingertip position estimation accuracy of 12 mm in the presence of external static forces. To demonstrate the potential of the distributed sensor architecture in robotics applications, we build a data-driven model independent of actuation feedback to detect contact with objects in the environment.

翻译：精确的形状感知是实现软体机器人闭环控制的关键要求，这只能通过分布式本体感知实现。由柔性材料制成的低成本、高能效光电传感器因其能够适应软体机器人的大变形而不损失性能，成为自然的选择。然而，现有的集成方法繁琐，需要手动步骤和复杂组装。我们提出一种半自动化打印工艺，将塑料光纤与读出电子器件共同嵌入3D打印的柔性结构中。光纤被锁定在位，读出电子器件在柔性结构经历大弯曲变形时仍保持与光纤的光学耦合，从而制造出可重复、整体成型的弯曲传感器，手动嵌入步骤仅需总计10分钟。我们通过制造多材料3D打印手指并广泛评估每个本体感知关节的性能来验证该工艺。传感器平均实现了70%的线性度和4.81°的均方根误差。此外，分布式架构使得在存在外部静态力的情况下，仍能保持平均12毫米的指尖位置估计精度。为展示分布式传感器架构在机器人应用中的潜力，我们构建了一个独立于驱动反馈的数据驱动模型，用于检测与环境中物体的接触。