Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
68
datasets available to search
ShareScore release 0.9.0
Dataset results
68 results for “Soft robotics”
Data and code related to the paper: "Integrated stretchable pneumatic strain gauges for electronics-free soft robots"
<p>This folder contains the raw data and Matlab scripts to reproduce the plots and supplementary movies for the paper:</p> <p>Anastasia Koivikko, Vilma Lampinen, Mika Pihlajamäki, Kyriacos Yiannacou, Vipul Sharma & Veikko Sariola, "Integrated Stretchable Pneumatic Strain Gauges for Electronics-Free Soft Robots", Communications Engineering, 1, 14 (2022).</p> <p><a href="https://doi.org/10.1038/s44172-022-00015-6">Link to the paper</a>.</p> <p>The scripts were tested on Matlab R2021a on Windows.</p> <p>Generally speaking, there is a folder containing the plotting scripts for each figure. In most cases, the folder contains scripts named <strong>plot<...>.m</strong> that recreate the actual plots. Some folders also have a scripts <strong>analyze<...>.m</strong> to analyze the data; these need to be run before the actual plotting.</p> <p>For more details, please see the paper.</p>
Supramolecular Self-Healing Sensor Fiber Composites for Damage Detection in Piezoresistive Electronic Skin for Soft Robots
<p>Self-healing materials can prolong the lifetime of structures and products by enabling the repairing of damage. However, detecting the damage and the progress of the healing process remains an important issue. In this study, self-healing, piezoresistive strain sensor fibers (ShSFs) are used for detecting strain deformation and damage in a self-healing elastomeric matrix. The ShSFs were embedded in the self-healing matrix for the development of self-healing sensor fiber composites (ShSFC) with elongation at break values of up to 100%. A quadruple hydrogen-bonded supramolecular elastomer was used as a matrix material. The ShSFCs exhibited a reproducible and monotonic response. The ShSFCs were investigated for use as sensorized electronic skin on 3D-printed soft robotic modules, such as bending actuators. Depending on the bending actuator module, the electronic skin was loaded under either compression (pneumatic-based module) or tension (tendon-based module). In both configurations, the ShSFs could be successfully used as deformation sensors, and in addition, detect the presence of damage based on the sensor signal drift. The sensor under tension showed better recovery of the signal after healing, and smaller signal relaxation. Even with the complete severing of the fiber, the piezoresistive properties returned after the healing, but in that case, thermal heat treatment was required. With their resilient response and self-healing properties, the supramolecular fiber composites can be used for the next generation of soft robotic modules</p>
Fabrication of a Soft Robotic Gripper With Integrated Strain Sensing Elements Using Multi-Material Additive Manufacturing
<p>With the purpose of making soft robotic structures with embedded sensors, additive manufacturing techniques like fused deposition modeling (FDM) are popular. Thermoplastic polyurethane (TPU) filaments, with and without conductive fillers, are now commercially available. However, conventional FDM still has some limitations because of the marginal compatibility with soft materials. Material selection criteria for the available material options for FDM have not been established. In this study, an open-source soft robotic gripper design has been used to evaluate the FDM printing of TPU structures with integrated strain sensing elements in order to provide some guidelines for the material selection when an elastomer and a soft piezoresistive sensor are combined. Such soft grippers, with integrated strain sensing elements, were successfully printed using a multi-material FDM 3D printer. Characterization of the integrated piezoresistive sensor function, using dynamic tensile testing, revealed that the sensors exhibited good linearity up to 30% strain, which was sufficient for the deformation range of the selected gripper structure. Grippers produced using four different TPU materials were used to investigate the effect of the Shore hardness of the TPU on the piezoresistive sensor properties. The results indicated that the <em>in situ</em> printed strain sensing elements on the soft gripper were able to detect the deformation of the structure when the tentacles of the gripper were open or closed. The sensor signal could differentiate between the picking of small or big objects and when an obstacle prevented the tentacles from opening. Interestingly, the sensors embedded in the tentacles exhibited good reproducibility and linearity, and the sensitivity of the sensor response changed with the Shore hardness of the gripper. Correlation between TPU Shore hardness, used for the gripper body and sensitivity of the integrated <em>in situ</em> strain sensing elements, showed that material selection affects the sensor signal significantly.</p>
Multi-material 3D Printing of Thermoplastic Elastomers for Development of Soft Robotic Structures with Integrated Sensor Elements
<p>Embedded sensing can benefit soft robots with the ability to interact with their environment but producing embedded soft sensors can be challenging. Multi-material Fused Deposition Modeling (FDM) additive manufacturing allows producing complex structures, by combining more than one kind of polymeric material. For multi-material FDM, conductive thermoplastic elastomer filaments have been developed. This allows the printing of flexible functional structures, based on thermoplastic elastomer structures with conductive paths that are of great interest for stretchable electronics and soft robotic applications. In this study, stretchable piezoresistive elastomer strain sensor composites were successfully produced by using multi-material FDM. A piezoresistive thermoplastic elastomer was printed on the top of a nonconductive, flexible thermoplastic elastomer strip using FDM multi-material 3D printer. FDM elastomer filaments with different shore hardness as substrate materials for the gripper structure were used. The hardness of the elastomer affected the printability and the adhesion to the conductive elastomer material, which was used as a strain sensor material. The hardness affected the strain sensor properties too. The piezoresistive response, dynamic behavior, drift, relaxation and sensitivity of the printed multi-material strips were investigated by tensile tests. Soft robotic grippers with integrated sensing elements to detect deformation while touching the objective were selected as a case study. The soft grippers with the integrated sensors exhibited intelligent response by recognizing when they were griping a small or big object and when an obstacle was inhibiting their function.</p>
Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" (Data set and materials used for human-robot interaction experiment)
<p>Supplementary materials (set 2 of 2) in support of "Signalling Emotions with a Breathing Soft Robot" authored by Troels Aske Klausen, Ulrich Farhadi, Evgenios Vlachos, and Jonas Jørgensen.</p> <p>Contents of set 2:<br> - Data set and materials used for the human-robot interaction experiment and for data analysis</p> <p>Files:<br> - "Questionnaire.pdf": Questionnaire used for data collection.<br> - "Video links.txt": Weblinks to stimuli videos used.<br> - "Data set.xls": Collected raw data.<br> - "Matlab_DataAnalysis.mlx": Matlab script used to analyze raw data.<br> - "Linear_Arousal.png": Linear fit between the scoring of arousal and BPM.<br> - "Linear_Dominance.png": Linear fit between the scoring of dominance and BPM.<br> - "Linear_Pleasure.png": Linear fit between the scoring of pleasure and BPM.</p> <p>The experiment procedure is described in the paper.<br> The soft robot used for the experiment is open source and can be manufactured using design files available on Zenodo: 10.5281/zenodo.5565201</p>
Data for "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots"
<p>This dataset contains all the data and CAD models needed to replicate the study presented in "Multimodal Soft Valve Enables Physical Responsiveness for Pre-emptive Resilience of Soft Robots".</p>
Data for "Adaptive and Resilient Soft Tensegrity Robots" (Rieffel & Mouret, 2018)
<p>Data (experimental results) for the paper "Adaptive and Resilient Soft Tensegrity Robots", to appear in Soft Robotics (2018).</p> <ul> <li>Source code: <a href="https://github.com/resibots/rieffel_mouret_2018_soft_tensegrity">https://github.com/resibots/rieffel_mouret_2018_soft_tensegrity </a></li> <li>Pre-print: <a href="https://arxiv.org/abs/1702.03258">https://arxiv.org/abs/1702.03258</a></li> </ul>
Pellet-based fused deposition modeling for the development of soft compliant robotic grippers with integrated
<p>Fused deposition modeling (FDM) has some advantages compared to other additive manufacturing techniques, such as the in situ integration of functional components, like sensors, and recyclability of parts. However, conventional filament-based FDM techniques are limited to thermoplastic elastomers with a Shore hardness above 70 A, thus it has marginal compatibility with soft robotic structures. Due to recently emerging pellet-based FDM printer technology, the fabrication of soft grippers with low Shore hardness has become possible. In this study, styrene based thermoplastic elastomers (TPS) were used to print elastic strips and soft gripper structures down to a Shore hardness of 25 A with an integrated strain sensing element (piezoresistive sensor). Printing on a soft rather than rigid substrate affects the integration of the printed thread on the substrate, because of the softness and relaxation, during the printing softness. It was seen that integrating the sensing element on a substrate with higher Shore hardness decreased the elongation at the point of fracture and the sensitivity of the sensing element. A soft compliant gripper structure with an integrated sensing layer was printed with the TPS-based elastomers successfully, and even due to the complex deformation of the compliant gripper structure, several positions could be detected successfully. Opened and closed position of the gripper, as well as, size recognition of spools of different sizes could be monitored by the piezoresistive printed sensor layer. The most sensitive sensing performance was obtained with the TPS of the lower Shore hardness (25 A), as the value of relative change in resistance was 1, followed by the gripper of Shore hardness 65 A and a relative change in resistance of 0.51. With this study, we demonstrated that pellet-based FDM printers can be used, to print potential soft robotic structures with in-situ integrated sensor structures.</p>
Datasets and images of publication: Additive manufacturing for self-healing soft robots
<p>This entry contains the images and data used for the publication: Additive manufacturing for self-healing soft robots (DOI: 10.1089/soro.2019.0081). The datasets are named after the image they refer to and are available under the CC-BYSA 4.0 International license.</p>
Room Temperature Self-Healing in Soft Pneumatic Robotics: Autonomous Self-Healing in a Diels-Alder Polymer Network
<p>Healable soft robotic systems have been developed by constructing flexible membranes out of Diels?Alder (DA) polymer networks. In these components, relatively large amounts of damage, on the centimeter scale, can be healed, provided that the temperature is increased to 80?90 ?C. This article presents a new DA polymer network that can heal at room temperature through a smart design of the network that increases the molecular mobility in the material. This new material is used to develop the first healable soft robotic prototype that can autonomously recover from severe, realistic damage. The soft pneumatic hand can recover from various types of injuries, including being cut completely in half, without the need for a temperature increase. After healing, the performance of the soft robotic prototype is recovered.</p>
Data from: 3D printed digital pneumatic logic for the control of soft robotic actuators
<p>Soft robots are paving their way to catch up with the application range of metal-based machines and to occupy fields which are challenging for traditional machines. Pneumatic actuators play an important role in this development, allowing the construction of bioinspired motion systems. Pneumatic logic gates provide a powerful alternative for controlling pressure-activated soft robots, which are often controlled by metallic valves and electric circuits. Many existing approaches for fully compliant pneumatic control logic suffer from high manual effort and low pressure tolerance. In our work, we invented 3D printable, pneumatic logic gates that perform Boolean operations and imitate electric circuits. Within 7 hours, an FDM printer is able to produce a module that serves as either an OR, AND or NOT gate; the logic function is defined by the assigned input signals. The gate contains two alternately acting pneumatic valves, whose work principle is based on the interaction of pressurized chambers and a 3D printed 1 mm tube inside. The gate design does not require any kind of support material for its hollow parts, which makes the modules ready to use directly after printing. Depending on the chosen material, the modules can operate on a pressure supply between 80 and over 750 kPa. The capabilities of the invented gates were verified by implementing an electronics-free drink dispenser based on a pneumatic ring oscillator and a 1-bit memory. Their high compliance is demonstrated by driving a car over a fully flexible, 3D printed robotic walker controlled by an integrated circuit.</p>
I-Seed_DS2 – SEED LIKE SOFT ROBOTS DESIGN AND DEVELOPMENT
<p>The dataset I-Seed_DS2 is dedicated to the design, development and functional validation of the I-Seed robots and flyers.</p> <p>Task 6.1: Development of biodegradable and hygromorphic structures</p> <p>Task 6.2: I-Seed robots design and development</p> <p>Task 6.3: I-Seed robots humidity detection</p> <p>Task 6.4: I-Seed robots functional tests</p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Dataset]
<p>Dataset used for the paper submitted to RO-MAN 2022</p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p>
Magnetic Soft Robotic Bladder for Assisted Urination
<p>The poor contractility of the detrusor muscle in underactive bladders (UABs) fails to increase the pressure inside the UAB, leading to strenuous and incomplete urination. However, existing therapeutic strategies by modulating/repairing detrusor muscles, e.g., neurostimulation and regenerative medicine, still have low efficacy and/or adverse effects. Here, we present an implantable magnetic soft robotic bladder (MRB) that can directly apply mechanical compression to the UAB to assist urination. Composed of a biocompatible elastomer composite with optimized magnetic domains, the MRB enables on-demand contraction of the UAB when actuated by magnetic fields. A representative MRB for an UAB in a porcine model is demonstrated and MRB-assisted urination is validated by in situ computed tomography imaging after 14-day implantation. The urodynamic tests show a series of successful urination with a high pressure increase and fast urine flow. Our work paves the way for developing MRB to assist urination for humans with UABs.</p>
A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots
<p>This dataset contains data and code to replicate main and supplemental figures for the related article published in Matter:</p> <p>Title: A fluidic relaxation oscillator for reprogrammable sequential actuation in soft robots</p> <p>DOI: 10.1016/j.matt.2022.06.002</p> <p>In the article we introduce a simple and compact soft valve with intentional hysteresis, analogous to an electronic relaxation oscillator. By integrating the valve with a soft actuator, we transform a continuous inflow to cyclic activation. Importantly, we show that our circuits can activate up to five actuators in various sequences, and that we can physically reprogram the activation order by varying the (initial) conditions in the fluidic circuit. Moreover, we show the feasibility of our approach under more realistic conditions by building a four-legged robot.</p> <p>This dataset contains measurement data and simulation files.</p> <p>The data are recorded (in human-readable format) from experiments on our fluidic circuits (e.g., pressure, flow data), and are accompanied by MATLAB scripts for data processing as well as generating figures.</p> <p>The simulation files are MATLAB and LTspice files for simulating our fluidic circuits making use of the analogy with electronic circuits. For more involved parameter sweeps we generate, run, and post-process LTspice input and result files using MATLAB. More details and instruction for use are provided in the included readme.txt files.</p>
Dataset for the article: Robotic Feet Modeled After Ungulates Improve Locomotion on Soft Wet Grounds
<div> <div>This repository contains data for three different experiments presented in the paper:</div> <br> <div>(1) moose_feet (40 files): The moose leg experiments are labeled as ax_y.nc,</div> <div>where 'a' indicates attached digits and 'f' indicates free digits. The</div> <div>number 'x' is either 1 (front leg) or 2 (hind leg), and the number 'y'</div> <div>is an increment from 0 to 9 representing the 10 samples of each set.</div> <br> <div>(2) synthetic_feet (120 files): The synthetic feet experiments are labeled</div> <div>as lw_a_y.nc, where 'lw' (Low Water content) can be replaced by 'mw'</div> <div>(Medium Water content) or 'vw' (Vast Water content). The 'a' can be 'o'</div> <div>(Original Go1 foot), 'r' (Rigid extended foot), 'f' (Free digits anisotropic</div> <div>foot), or 'a' (Attached digits). Similar to (1), the last number is an increment from 0 to 9.</div> <br> <div>(3) Go1 (15 files): The locomotion experiments of the quadruped robot on the</div> <div>track are labeled as condition_y.nc, where 'condition' is either 'hard_ground'</div> <div>for experiments on hard ground, 'bioinspired_feet' for the locomotion of the</div> <div>quadruped on mud using bio-inspired anisotropic feet, or 'original_feet' for</div> <div>experiments where the robot used the original Go1 feet. The 'y' is an increment from 0 to 4.</div> <br> <div>The files for moose_feet and synthetic_feet contain timestamp (s), position (m), and force (N) data.</div> <div>The files for Go1 contain timestamp (s), position (rad), velocity (rad/s), torque (Nm) data for all 12 motors, and the distance traveled by the robot (m).</div> <br> <div>All files can be read using xarray datasets (https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html).</div> </div>
Vector image of processing techniques for self-healing soft robots
<p>Vector images of processing techniques that can be used to manufacture self-healing soft robots.</p> <p>The file includes different types of additive manufacturing processes (fused filament fabrication, direct ink writing, selective laser sintering, stereolithography, inkjet printing, fused granulate fabrication), formative processes (compression moulding, solvent casting, injection moulding, casting, vacuum assisted resin transfer moulding, blow moulding), and assembly processes (folding & binding, joining & binding, stacking and binding, local thermal ablation & welding).</p>
Dataset for manuscript "Plants as inspiration for material‑based sensing and actuation in soft robots and machines"
<p>The dataset includes data for Figure 2 in the article "Plants as inspiration for material-based sensing and actuation in soft robots and machines<em>" MRS Bulletin</em> (2023). https://doi.org/10.1557/s43577-022-00470-8</p>
Data from: Stretchable Arduinos embedded in soft robots
Open the record for dataset details and reuse information.
Data from: 3D printed digital pneumatic logic for the control of soft robotic actuators
Open the record for dataset details and reuse information.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.