In this study, we utilize simple light-emitting diodes (LEDs) and photodetectors (PDs) combined with an intelligent shape decoding framework to enable 3D shape sensing of a self-contained flexible substrate. Finite element analysis (FEA) is leveraged to optimize the LED-PD layout and enrich ground-truth data from sparse to dense points for model training. The mapping from light intensities to overall sensor shape was achieved with an autoregression-based model that considers temporal continuity and spatial locality. The sensing framework was evaluated on an A5-sized flexible sensor prototype and a fish-shaped prototype, where sensing accuracy (RMSE = 0.27 mm) and repeatability (Δ light intensity < 0.31% over 1000 cycles) were tested underwater. We validate an affordable alternative to FBG sensors with high-order sensing outputs, where demonstrations are supplemented in the below videos.
This Supporting Information includes: _Supplementary text describing Preliminary Status Classifer, segmentation methods, model training and validation details; two supplementary tables, two supplementary figures and one supplementary video._ Corresponding author(s) Email: _ [email protected]; [email protected]; [email protected] _
This Supporting Information includes information regarding the magnetic field of the actuator magnet, MR-LF-S (which has the same geometry as MR-LF and a soft compartment), and a table comparing MR-LF to other small-scale, flexible magnetic crawler robots. Corresponding author email: [email protected]
This Supporting Information includes: a comparison of the REAL (Robot Ear Accomplished by Laser) with a typical vibration measuring system (Laser Doppler Vibrometers, LDV), frequency response of various materials on REAL and real-time analysis of REAL audio neural network model. Xiaoping Hong Email: [email protected]
Complex environments, such as those found in surgical and search-and-rescue applications, require soft devices to adapt to minimal space conditions without sacrificing the ability to complete dexterous tasks. Stacked Balloon Actuators (SBAs) are capable of large deformations despite folding nearly flat when deflated, making them ideal candidates for such applications. This paper presents the design, fabrication, modeling, and characterization of monolithic, inflatable, soft SBAs. Modeling is presented using analytical principles based on geometry, and then using conventional and real-time finite element methods. Both one and three degree-of-freedom (DoF) SBAs are fully characterized with regards to stroke, force, and workspace. Finally, three representative demonstrations show the SBA's small-aperture navigation, bracing, and workspace-enhancing capabilities.
This Supplementary Information includes: Section S1- Fabrication method Section S2- Actuation method Section S3- Analysis of sixth-DOF torque Section S4- Experiments Figures S1-S31 Supporting Table References Other supplementary materials for this manuscript include the following:Supporting SI Videos S1-S10 Corresponding author(s) Email: [email protected]
Figure S1. a) The memristive synaptic behavior with an ideally symmetric and linear weight update ability (constant ΔG for identical pulses) but limited conductance levels (N =20). b) Test accuracy for 10,000 images in the MNIST dataset obtained during the training of memristive DBN as a function of the training epoch (CDth=64).