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In this work, we present two embedded soft optical waveguide sensors designed for real-time onboard configuration sensing in soft actuators for robotic locomotion. Extending the contributions of our collaborators who employed external camera systems to monitor the gaits of twisted-beam structures, we strategically integrate our OptiGap sensor system into these structures to monitor their dynamic behavior. The system is validated through machine learning models that correlate sensor data with camera-based motion tracking, achieving high accuracy in predicting forward or reverse gaits and validating its capability for real-time sensing. Our second sensor, consisting of a square cross-section fiber pre-twisted to 360 degrees, is designed to detect the chirality of reconfigurable twisted beams. Experimental results confirm the sensor’s effectiveness in capturing variations in light transmittance corresponding to twist angle, serving as a reliable chirality sensor. The successful integration of these sensors not only improves the adaptability of soft robotic systems but also opens avenues for advanced control algorithms. 

This paper presents the novel use of air gaps in flexible optical light pipes to create coded patterns for use in bend localization. The OptiGap sensor system allows for the creation of extrinsic intensity modulated bend sensors that function as flexible absolute linear encoders. Coded air gap patterns are identified by a Gaussian naive Bayes (GNB) classifier running on an STM32 microcontroller. The fitting of the classifier is aided by a custom software suite that simplifies data collection and processing from the sensor. The sensor model is analyzed and verified through simulation and experiments, highlighting key properties and parameters that aid in the design of OptiGap sensors using different light pipe materials and for various applications. The OptiGap system allows for real-time and accurate bend localization in many robotics and automation applications, in both wet and dry conditions. 

This paper presents the concept of creating virtual joints in soft robotic structures by modifying the local curvature of non-stretchable thin-walled structures through shape memory alloy (SMA)-based surface actuation. A thin planar flexible material can be stiffened by curving it along one axis, which increases stiffness by increasing the effective thickness. Locally deforming the curved sheet by making a flat region reduces this thickness, creating a defect. The material buckles and bends in a controlled manner at that location under an external force, producing a virtual compliant joint. We use tailored wire placement techniques to embed a continuous SMA wire in a serpentine pattern into denim cloth stiffened by a thin plastic film. When curved, joints can be created in this structure by activating small segments of the SMA wire using Joule heating which induces local curvature, with each of these segments able to exert up to 1.6 N of force. Finally, we present a circuit and algorithm for routing current through any desired SMA wire segment(s). Experimental results show that compliant joints can be created anywhere along the structure, resulting in a reconfigurable system.