Additive Manufacturing (AM) is a promising solution for handling the complexity of fabricating soft robots. However, the AM of hyperelastic materials is still challenging with limited material types. Within this work, pellet-based 3D printing of very soft thermoplastic elastomers (TPEs) was explored. Our results show that TPEs can have similar engineering stress and maximum strain as Ecoflex OO-10. These TPEs were used to 3D-print airtight thin membranes (0.2-1.2 mm), which could inflate up to a stretch of 1320\%. Combining the membrane's large expansion and softness with the 3D printing of hollow structures simplified the design of a bending actuator that can bend 180 degrees and reach a blocked force of 238 times its weight. In addition, by 3D printing TPE pellets and rigid filaments, the soft membrane could grasp objects by enveloping an object or as a sensorized sucker, which relied on the TPE's softness to conform to the object or act as a seal. In addition, the membrane of the sucker was utilized as a tactile sensor to detect an object before adhesion. These results suggest the feasibility of 3D printing soft robots by using soft TPEs and membranes as an interesting class of materials and sensorized actuators, respectively.

Integration of both actuation and proprioception into the robot body would provide actuation and sensing in a single integrated system. Within this work, a manufacturing approach for such actuators is investigated that relies on 3D printing for fabricating soft-graded porous actuators with piezoresistive sensing and identified models for strain estimation. By 3D printing, a graded porous structure consisting of a conductive thermoplastic elastomer both mechanical programming for actuation and piezoresistive sensing were realized. Whereas identified Wiener-Hammerstein (WH) models estimate the strain by compensating the nonlinear hysteresis of the sensorized actuator. Three actuator types were investigated, namely: a bending actuator, a contractor, and a three DoF bending segment (3DoF). The porosity of the contractors was shown to enable the tailoring of both the stroke and resistance change. Furthermore, the WH models could provide strain estimation with on average high fits (83%) and low RMS errors (6%) for all three actuators, which outperformed linear models significantly (76.2/9.4% fit/RMS error). These results indicate that an integrated manufacturing approach with both 3D printed graded porous structures and system identification can realize sensorized actuators that can be tailored through porosity for both actuation and sensing behavior but also compensate for the nonlinear hysteresis.

Sensorized insoles provide a tool to perform gait studies and health monitoring during daily life. These sensorized insoles need to be comfortable and lightweight to be accepted. Previous work has already demonstrated that sensorized insoles are possible and can estimate both ground reaction force and gait cycle. However, these are often assemblies of commercial components restricting design freedom and flexibility. Within this work, we investigate the feasibility of using four 3D-printed porous (foam-like) piezoresistive sensors embedded in a commercial insole. These sensors were evaluated using an instrumented treadmill as the golden standard. It was observed that the four sensors behaved in line with the expected change in pressure distribution during the gait cycle. In addition, Hammerstein-Wiener models were identified that were capable of estimating the vertical and mediolateral ground reaction forces (GRFs). Their NRMSE fits were on average 82% and 73%, respectively. Similarly, for the averaged gait cycle the R^2 values were 0.98 and 0.99 with normalized RMS errors overall below 6%. These values were comparable with other insoles based on commercial force sensing resistors but at a significantly lower cost (over four times cheaper). Thereby indicating that our 3D-printed sensors can be an interesting option for sensorized insoles. The advantage of 3D printing these sensors is that it allows for significantly more design freedom, reduces assembly, and is cheaper. However, further research is needed to exploit this design freedom for complex sensors, estimate the anteroposterior GRF, and fully 3D print the entire insole.

New additive manufacturing methods are needed to realize more complex soft robots. One example is soft fluidic robotics, which exploits fluidic power and stiffness gradients. Porous structures are an interesting type for this approach, as they are flexible and allow for fluid transport. Within this work, the Infill-Foam (InFoam) is proposed to print structures with graded porosity by liquid rope coiling (LRC). By exploiting LRC, the InFoam method could exploit the repeatable coiling patterns to print structures. To this end, only the characterization of the relation between nozzle height and coil radius and the extruded length were necessary (at a fixed temperature). Then by adjusting the nozzle height and/or extrusion speed the porosity of the printed structure could be set. The InFoam method was demonstrated by printing porous structures using styrene-ethylene-butylene-styrene (SEBS) with porosities ranging from 46\% to 89\%. In compression tests, the cubes showed large changes in modulus (more than 200 times), density (-89\% compared to bulk), and energy dissipation. The InFoam method combined coiling and normal plotting to realize a large range of porosity gradients. This grading was exploited to realize rectangular structures with varying deformation patterns, which included twisting, contraction, and bending. Furthermore, the InFoam method was shown to be capable of programming the behavior of bending actuators by varying the porosity. Both the output force and stroke showed correlations similar to those of the cubes. Thus, the InFoam method can fabricate and program the mechanical behavior of a soft fluidic (porous) actuator by grading porosity.