Efficient Reduced-Order Models for Soft Actuators
This addresses the problem of enabling soft robots to move beyond laboratory settings by improving sensing and modeling for applications like healthcare and search and rescue, though it appears incremental as it builds on existing sensing methods.
The study tackled the lack of real-time dynamic models for soft bending actuators by developing a reduced-order kinematic model using fiber optic strain sensing, which reconstructed continuous deformation and derived an efficient real-time equation of motion validated against experimental data.
Soft robotics have gained increased attention from the robotic community due to their unique features such as compliance and human safety. Impressive amount of soft robotic prototypes have shown their superior performance over their rigid counter parts in healthcare, rehabilitation, and search and rescue applications. However, soft robots are yet to capitalize on their potential outside laboratories and this could be attributed to lack of advanced sensing capabilities and real-time dynamic models. In this pilot study, we explore the use of high-accuracy, high-bandwidth deformation sensing via fiber optic strain sensing (FOSS) in soft bending actuators (SBA). Based on the high density sensor feedback, we introduce a reduced order kinematic model. Together with cubic spline interpolation, this model is able to reconstruct the continuous deformation of SBAs. The kinematic model is extended to derive an efficient real-time equation of motion and validated against the experimental data.