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”
Datasets and images of publication: Self-healing and high interfacial strength in multi-material soft pneumatic robots via reversible Diels-Alder bonds
<p>Data and Figures of the publication:</p> <p>Terryn, S.; Roels, E.; Brancart, J.; Assche, G.V.; Vanderborght, B. Self-Healing and High Interfacial Strength in Multi-Material Soft Pneumatic Robots via Reversible Diels–Alder Bonds. <em>Actuators</em> <strong>2020</strong>, <em>9</em>, 34.</p>
COgITOR Soft Robot
<p>This movie features two scientists at Empa in Switzerland, Physicits Dr. Loghman Jamilpanah, and Chemist Dr. Kwanele W. Kunene, who carry out materials research for the development of a soft robot. The robot project is called COgITOR, inspired by Prof. Dr. Alessandro Chiolerio, Italian Institute of Technology, Torino, and Genova.</p> <div> <div> <div> <p><strong>COgITOR</strong> is a project funded under the topic H2020-FETOPEN-2018-2020 / H2020-FETOPEN-2018-2019-2020-01 programme, aiming at developing a liquid state cybernetic system prototype. Holonomic memory and computing, pressure sensing, and energy harvesting from thermal gradients will be achieved using colloids. The prototype will be tested in extreme environments for potential space applications.</p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 964388</p> <p>The website reflects only the author's view and that the European Commission is not responsible for any use that may be made of the information it contains</p> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Video]
<p>Video of the paper submitted at 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> <p>Code for trainings and test available at:</p> <p>https://github.com/giorgionicola/SMAHRCO</p>
Magnetic Soft Robotic Bladder for Assisted Urination.
<p>Dataset</p>
Data for "Image-based Backbone Reconstruction for Non-Slender Soft Robots"
<p>This dataset provides the data for the forthcoming paper "Image-based Backbone Reconstruction for Non-Slender Soft Robots". The backbone reconstruction method used is based on the method described in Hoffmann et al. [1]. The modifications to this method to support the non-slender soft robot in this dataset are described in the forthcoming paper mentioned above. This dataset holds raw images of pressurized and elongated soft robots and the corresponding reconstructed backbones.</p> <h2>Dataset</h2> <p>The dataset is split into two subsets with similar structure. The first subset is contained in `dataset_01`. The second dataset is contained in `dataset_02`.</p> <p>Each subset consists of five folders and one schedule file. The schedule file `schedule.csv` contains the index of the schedule entry, the angle <span>α</span> in degree, the pressure of each chamber p_1 to p_3 in bar and if the pressurization is active. Furthermore, the five folders of the subset can be described as follows</p> <p>- `raw`: Contains the raw cropped images. The filenames are formatted as `CROPPED_C{CAMERA_INDEX}_E{SCHEDULE_ENTRY}.png` with the camera index `CAMERA_INDEX` and the schedule entry `SCHEDULE_ENTRY`.</p> <p>-`constant_curvature_slender`, `constant_curvature_volumetric`, `cubic_curvature_slender` and `cubic_curvature_volumetric`. These folders contain the actual reconstructed backbones based on the raw data from the `raw` folder. A different reconstruction approach was used in each of these folders<br> - `constant_curvature_slender` - A constant curvature backbone kinematic based on the slender model,<br> - `constant_curvature_volumetric` - A constant curvature backbone kinematic based on the volumetric model,<br> - `cubic_curvature_slender` - A cubic curvature backbone kinematic based on the slender model,<br> - `cubic_curvature_volumetric` - A cubic curvature backbone kinematic based on the volumetric model.<br>Each of these folders contain a `data` and `figures` folder. The data folder consists of `PARAMETER_E{SCHEDULE_ENTRY}.json` files listing the optimization parameters for each schedule entry `SCHEDULE_ENTRY` in the JSON format. The `figures` folder contains annotated images of the reconstructed backbone on the cropped raw images. The filenames are structured `ANNOTATED_E{SCHEDULE_ENTRY}_C{CAMERA_INDEX}_EPOCH{EPOCH}.png` with the schedule entry `SCHEDULE_ENTRY`, the camera index `CAMERA_INDEX` and the epoch `EPOCH` of the optimization algorithm.</p> <p>The optimization parameters include the base position `base_position` of the reconstructed backbone in world coordinates, the coefficients for the curvature polynomials `ux` and `uy`, and the constant coefficient for the elongation polynomial `la`.</p> <h2>Calibration Data</h2> <p>The calibration data is located in the `calibration` folder and consists of multiple `.npy` files in the numpy format. The corresponding camera index for the calibrated camera is abbreviated with `CAMERA_INDEX` in the following:</p> <ul> <li>`C{CAMERA_INDEX}.npy` - Stores the reprojection error, camera matrix, distortion coefficients, rotation, and translation vectors as returned by the `cv2.calibrateCamera` [2] method. </li> <li>`C{CAMERA_INDEX}_camera_matrix.npy` - Stores the camera_matrix as returned by the `cv2.calibrateCamera` [2] method. </li> <li>`C{CAMERA_INDEX}_distortion_coefficients.npy` - Stores the distortion coefficients as returned by the `cv2.calibrateCamera` [2] method. </li> <li> `C{CAMERA_INDEX}_projection_matrix.npy` - Stores the projection matrix from world space to pixel space based on the stereo camera calibration.</li> <li> `STEREO.npy` - Stores the reprojection error, R, T, E, F as returned by the `cv2.stereoCalibrate` [2] method as an object datatype.</li> </ul> <h2>Acknowledgement</h2> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 501861263 – SPP2353</p> <h2>References</h2> <p>[1] M. K. Hoffmann, J. Mühlenhoff, Z. Ding, T. Sattel and K. Flaßkamp. An iterative closest point algorithm for marker-free 3D shape registration of continuum robots. arXiv.<br>https://arxiv.org/abs/2405.15336</p> <p>[2] OpenCV. Camera Calibration and 3D Reconstruction. OpenCV Documentation. https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html, accessed May 27, 2024.</p>
Finite Element Analysis-Based Soft Robotic Modeling: Simulating a Soft Actuator in SOFA
<p>This document represent a step by step guide for a simulation in SOFA framework of a cable driven soft robot.</p>
[DATASET 7] - SOFT "SEARCHER-LIKE" ROBOT
<p>In the framework of GrowBot project, Task 5.4 aims at developing a robotic searcher with sensing and actuation abilities.<br> IIT has developed a modular continuum soft arm taking inspiration from the structural features of climbing plants investigated in WP3. The searcher module can explore the environment via circumnutation movements and tactile feedback.</p> <p>DS7 aims at collecting all the data related to the design and development of the soft searcher robot.</p>
Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits
<p>Most soft robots are pneumatically actuated and fabricated by molding and assembling processes that typically require many manual operations and limit complexity. Furthermore, complex control components (for example, electronic pumps and microcontrollers) must be added to achieve even simple functions. Desktop fused filament fabrication (FFF) three-dimensional printing provides an accessible alternative with less manual work and the capability of generating more complex structures. However, because of material and process limitations, FFF-printed soft robots often have a high effective stiffness and contain a large number of leaks, limiting their applications. We present an approach for the design and fabrication of soft, airtight pneumatic robotic devices using FFF to simultaneously print actuators with embedded fluidic control components. We demonstrated this approach by printing actuators an order of magnitude softer than those previously fabricated using FFF and capable of bending to form a complete circle. Similarly, we printed pneumatic valves that control a high-pressure airflow with low control pressure. Combining the actuators and valves, we demonstrated a monolithically printed electronics-free autonomous gripper. When connected to a constant supply of air pressure, the gripper autonomously detected and gripped an object and released the object when it detected a force due to the weight of the object acting perpendicular to the gripper. The entire fabrication process of the gripper required no posttreatment, postassembly, or repair of manufacturing defects, making this approach highly repeatable and accessible. Our proposed approach represents a step toward complex, customized robotic systems and components created at distributed fabricating facilities.</p>
Data from: In situ foliar augmentation of multiple species for optical phenotyping and bioengineering using soft robotics
Open the record for dataset details and reuse information.
Soft robots with autonomous adaptation for effective drug delivery amidst fibrous encapsulation
Open the record for dataset details and reuse information.
Experimental data of LCE-integrated soft everting robots
Open the record for dataset details and reuse information.
Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits
Open the record for dataset details and reuse information.
Development of a Pneumatically Actuated Quadruped Robot Using Soft-Rigid Hybrid Variable-Stiffness Rotary Joints
<p>This is a supplementary video for the paper "Development of a Pneumatically Actuated Quadruped Robot Using Soft-Rigid Hybrid Variable-Stiffness Rotary Joints" submitted to Robotics.</p>
Mechanoreceptive soft robotic molluscoids made of granular hydrogel-based organoelectronics
Open the record for dataset details and reuse information.
Upgrading and extending the lifecycle of soft robots by in-situ freeform liquid three-dimensional printing
<p>The data is associated with the research article titled '<em>Upgrading and extending the lifecycle of soft robots by in-situ freeform liquid three-dimensional printing</em>', published in <em>Science Robotics</em> journal.</p>
Supplementary footage for ctenophore-inspired soft robotic platform
<p>These three folders contain supplementary footage for “Encoding spatiotemporal asymmetry in artificial cilia with a ctenophore-inspired soft-robotic platform”.</p> <p>“Phase-averaged” contains gifs of phase averaged horizontal component of velocity (u), velocity magnitude, and vorticity. Note the folder naming convention denotes the shape of the propulsor, followed by the number of magnets on the timing belt (low or high phase lag), followed by the beat frequency.</p> <p>“PIV” contains videos of the flow fields computed with Particle Image Velocimetry. The horizontal component of velocity and vorticity are included for each propulsor shape, phase lag, and beat frequency listed above.</p> <p>“Raw_footage” contains videos of the raw footage used for kinematics and PIV.</p>
Supplementary data for "Crawling, Climbing, Perching, and Flying by FiBa Soft Robots"
<p>Supplementary Information for paper "Crawling, climbing, perching, and flying by FiBa soft robots". <a href="https://doi-org.ezp-prod1.hul.harvard.edu/10.1126/scirobotics.adk4533">DOI: 10.1126/scirobotics.adk4533</a> </p>
Files for: Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling
<p>This repository includes the files necessary to reproduce the digital anatomies, cardiac and aortic sleeves, and multi-material 3D-printed valves from the "Soft robotic patient-specific hydrodynamic model of aortic stenosis and ventricular remodeling" article.</p>
Sunlight-powered self-excited oscillators for sustainable autonomous soft robotics
<p>As soft robotics fast advances, full autonomy becomes highly sought, especially if their motion can be powered by environmental energy and self-regulated. This would present a self-sustained fashion in terms of both energy supply and motion control. Currently, autonomous movement can be realized by leveraging out-of-equilibrium oscillatory motion of stimuli-responsive polymers under a constant light source. It would be more advantageous if environmental energy can be scavenged to power robots. However, generating oscillation becomes challenging under the limited power density of available environmental energy sources. Herein, we develop fully autonomous soft robots with self-sustainability based on self-oscillation. Aided by multiphysics modeling, we have successfully reduced the required input power density to around one-Sun level through a liquid crystal elastomer (LCE)-based bilayer structure. The autonomous motion of the low-intensity LCE/elastomer bilayer oscillator “LiLBot” under low energy supply was achieved by high photothermal conversion, low modulus, and high material responsiveness simultaneously. The LiLBot features tunable peak-to-peak amplitudes from 4 degrees to 72 degrees and frequencies from 0.3 Hertz to 11 Hertz. The oscillation approach offers a general strategy for desgining autonomous, untethered and sustainable small-scale soft robots, such as sailboat, walker, roller, and synchronized flapping wings.</p>
Increasing the Payload Capacity of Soft Robot Arms by Localized Stiffening
<p>This is the data corresponding to the experiments in the Science Robotics paper entitled <em>Increasing the Payload Capacity of Soft Robot Arms by Localized Stiffening </em>by Daniel Bruder, Moritz A. Graule, Clark B. Teeple, and Robert J. Wood.</p>
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.