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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 proposed device uses a single actuator to transition a bistable constrained compliant beam to generate undulatory motion. Undulatory locomotion is a unique form of swimming that generates thrust through the propagation of a wave through a fish’s body. This paper draws inspiration from Anguilliformes and discusses the kinematics and dynamics of wave propagation of a bistable underwater robot. Thrust generation is explored through modeling and experimentation of the length constraint to better understand the device. This paper validates the theoretical spine behavior through experimentation and provides a path forward for future development in device optimization for various applications. Previous work developed devices that utilized either paired soft actuators or multiple redundant classical actuators that resulted in a complex prototype with intricate controls. Our work contrasts with prior work in that it aims to achieve undulatory motion through passive actuation from a single actively driven point which simplifies the control. Through this work, the goal is to further explore low-cost soft robotics via bistable mechanisms, continuum material properties, and simplified modeling practices. 

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.