Bidirectional Optical sensors for Actuation Tracking (BOAT) in soft lattice systems

The growing adoption of lattice-based structures in soft robotics creates a need for advanced sensing solutions capable of monitoring their global deformation, particularly compression and extension. In this work, we address this challenge by introducing a novel optical sensor based on two patterned waveguides arranged in an ellipsoidal geometry. This Bidirectional Optical sensor for Actuation Tracking (BOAT) is seamlessly co-printed with a lattice structure actuated by an embedded pneumatic artificial muscle (PAM), and its performance is assessed. During PAM elongation or contraction, the bending of the embedded BOAT waveguides induces output signal variations that enable a clear discrimination between compression and extension states. The designs of both each specific waveguide structure (by surface patterning) and of the sensorized lattice-based unit embedding two BOATs are supported by numerical simulations. Experimental calibration over 100 consecutive pressure cycles ranging from +50 kPa to $-$40 kPa demonstrates a highly repeatable response, allowing a reliable distinction between extension and compression. Finally, sensor feedback is used to implement a digital shadow, enabling continuous synchronization between the whole sensorized unit and its virtual counterpart. These results establish BOAT as a powerful and reliable approach for deformation monitoring in soft lattice-based robotic systems.

Monolithic Units: Actuation, Sensing, and Simulation for Integrated Soft Robot Design

This work introduces the Monolithic Unit (MU), an actuator-lattice-sensor building block for soft robotics. The MU integrates pneumatic actuation, a compliant lattice envelope, and candidate sites for optical waveguide sensing into a single printed body. In order to study reproducibility and scalability, a parametric design framework establishes deterministic rules linking actuator chamber dimensions to lattice unit cell size. Experimental homogenization of lattice specimens provides effective material properties for finite element simulation. Within this simulation environment, sensor placement is treated as a discrete optimization problem, where a finite set of candidate waveguide paths derived from lattice nodes is evaluated by introducing local stiffening, and the configuration minimizing deviation from baseline mechanical response is selected. Optimized models are fabricated and experimentally characterized, validating the preservation of mechanical performance while enabling embedded sensing. The workflow is further extended to scaled units and a two-finger gripper, demonstrating generality of the MU concept. This approach advances monolithic soft robotic design by combining reproducible co-design rules with simulation-informed sensor integration.