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2,639 results for “Robotic”
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 30
<p>These data were collected from robot controller during solar tanks welding operation.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 24
<p>These data were collected from robot controller during solar tanks welding operation.</p>
PROGRAMS project. Robot controller data from Calpak-Cicero Hellas SA on 2020 Week 25
<p>These data were collected from robot controller during solar tanks welding operation.</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>
A Kalman Filter Approach to the Fusion of Acceleration, GNSS position and Rotation Sensor Data from Robot Motions
<p><strong>GNSS data:</strong></p> <ul> <li>Instrument: Javad antenna and Septentrio receiver</li> <li>sampling rate: 100 Hz</li> <li>Bandwidth of loop filter: auto adjust</li> <li>Relative positioning </li> <li>Baseline: ultra short with distance of 5 m</li> <li>files in Rinex format: Rover (moving antenna) and Base (stationary antenna), .20G (GLONASS Navigation data), .20N (GPS Navigation data), .20L (Galileo Navigation data), .20O (Observations)</li> </ul> <p><strong>Accelerometer data:</strong></p> <ul> <li>Instrument: EpiSensor and Centaur Digitizer</li> <li>Sampling rate: 250 Hz</li> <li>Unit: counts</li> <li>unfiltered</li> <li>file: XKUK_centaur-6_1233_20200908_114500.seed</li> </ul> <p><strong>Angular rate data:</strong></p> <ul> <li>Instrument: IMU KvH 1750 (includes accelerometer and rotational sensor)</li> <li>Sampling rate: 250 Hz</li> <li>Unit gyro: rad/s</li> <li>Unit accelerometer: g (gravitational acceleration)</li> <li>file: LOGGING_1750_IMU_1308K004_11_57_25_250.csv</li> </ul> <p><strong>Robot Feedback:</strong></p> <ul> <li>Instrument: KUKA model AGILUS KR 6 R900 sixx</li> <li>Sampling rate: 250 Hz</li> <li>Unit translation: m</li> <li>Unit rotation: degree</li> <li>files: kuka_motion_*.txt, 1-4 are consecutive in time.</li> </ul> <p><strong>Experiments:</strong></p> <ul> <li>T: translations, R: rotations, XL, L, S denote the relative amplitudes</li> <li>10 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TLRS, TSRS, TSRS (Robot feedback (1,2), angular rate, GNSS data)</li> <li>9 experiments: TLRXL, TLRXL, TLRL, TLRL, TLRS, unfinished TLRS, TLRS, TSRS, TSRS (Robot feedback (3,4), accelerometer data</li> </ul>
Robots for Microfarms (ROMI) - Plant Scanner Video - D5.3
<p><strong>The following video shows the functionalities and usage of the Plant Scanner developed within the Robots for Microfarms (ROMI) project funded by EU Grant 773875</strong></p> <p><em>Videos are available in:</em></p> <ul> <li><em>hi-res (4K Apple ProRes)</em></li> <li><em>mid-red (4K H264)</em></li> <li><em>low-res (1080p H264)</em></li> </ul> <p><em>You can also watch it on <a href="https://www.youtube.com/watch?v=LtcDBj2Y2uM">Youtube</a></em></p> <p><strong>Video script:</strong></p> <p>The ROMI Plant Scanner is a plant phenotyping robot that generates high quality and high precision imaging data. It allows us to analyze the shoot architecture of a medium sized plant in indoor conditions in three dimensions.</p> <p>The image acquisition is non-destructive, allowing life-time analysis of the same plant, or the option to reuse a plant imaged in the Plant Scanner in multiple analyses. The robot creates a phenotype of a single plant in a few minutes and can make up to a hundred in a day. Also, automated analysis pipelines have been optimized to take raw data as input and directly deliver the final analysis in a format for biologist end-users.</p> <p>This phenotyping is dedicated to Research & Development teams in plant science. Precision phenotyping is becoming invaluable to current questions of modern biology, seeking a quantitative understanding of the mechanisms governing plant growth and development.</p> <p>In relation to their sessile lifestyle, plant shoot systems generally explore the above-ground space in three dimensions. To make some shoot traits accessible to routine or exploratory phenotyping Automation is crucial. Moving the camera rather than the plant ensures that the plant can remain still - providing more precision.</p> <p>The Romi Plant Scanner could be used to automate the phenotyping of any trait of the shoot system of a single plant.</p> <p>The plant Scanner is a fixed phenotyping station. Its reasonable size can easily fit into 2 to 3 meters squared, ideally near to a facility where plants are individually cultured in moveable pots.</p> <p>The ROMI has developed a proof-of-concept scenario using a challenging task: measuring the in-flor-escence phyllo-taxis of the model plant Arabodopsis thaliana.<br> The hardware design shares many components from the Romi Rover and it shares the same camera module with the Romi Cable Bot, ensuring reusability and maintainability across the full ROMI stack.</p> <p>The rover is available as an Open Source project. All of the source code and plans are freely available. This allows us to improve the design over time using input from farmers and engineers. That is also why we made the design modular using components that can be found “off-the-shelf” or that can be produced using 3D printers and laser cutters. People with development skills can also contribute. Our software is available online on Github. This makes the Romi Scanner a good platform to experiment with innovative tools for farming research.</p>
planar robot PRRP
<p>The robot consists of two prismatic joints sliding along two perpendicular axes. The positions<br> of the prismatic joints are denoted by x (along x-axis) and q (along q-axis). These<br> prismatic joints are connected through rigid links of lengths a, l and b respectively,<br> with l is strictly larger than max(a, b). The three links are connected to each other<br> by two joints each of which is rotatable 360 degrees.</p>
Dataset Interactive Audio Augmented Reality in Participatory Performance _Please Confirm You Are Not A Robot_
<p>This dataset gathers the different data from the study "Interactive Audio Augmented Reality in Participatory Performance". </p>
Robot Self-Assembly as Adaptive Growth Process: Collective Selection of Seed Position and Self-Organizing Tree-Structures
<p>Autonomous self-assembly allows to create structures and scaffolds on demand and automatically. The desired structure may be predetermined or alternatively it is the result of an artificial growth process that adapts to environmental features and to the intermediate structure itself. In a self-organizing and decentralized control approach the robots interact only locally and form the structure collectively. Designing a complete approach that allows the robot group to collectively decide on where to start the self-assembly, that adapts at runtime to environmental conditions, and that guarantees the structural stability is challenging and does not yet exist. We present an approach to self-assembly inspired by diffusion-limited aggregation that generates an adaptive structure reacting to environmental conditions in an artificial growth process. During a preparatory stage the robots collectively decide where to start the self-assembly also depending on environmental conditions. In the actual self-assembly stage, the robots create tree-like structures that grow towards light. We report the results of robot self-assembly experiments with 50 Kilobots. Our results demonstrate how an adaptive growth process can be implemented in robots. We explain how our approach will be extended to a 3-d growth process and how robot self-assembly as an open-ended adaptive growth process opens up a multiplicity of future opportunities.</p>
Dataset of the scientific paper "A Comparative Analysis of 2D and 3D Tasks for Virtual Reality Therapies Based on Robotic-Assisted Neurorehabilitation for Post-stroke Patients" (Front. Aging Neurosci.)
<p> There are three files with the following information:<br> - data_2d.bin, binary file with information of the different parameters of the nine subjects during 2d tasks<br> - data_3d.bin, binary file with information of the different parameters of the nine subjects during 3d tasks<br> - survey.bin, binary file with the score of the System Usability Scale (SUS) survey of each subject</p>
Generic, Scalable and Decentralized Fault Detection for Robot Swarms
<p>This raw data archive includes the data on fault detection in a simulated swarm of 20 e-puck robots. The data was used in the paper Generic, Scalable and Decentralized Fault Detection for Robot Swarms by D. Tarapore et al. (2017).</p> <p>See readme.txt for more details.</p>
World Map of Agricultural Robot Manufacturer & services
<p>This Data Base is a World Map of all the manufacturers of agricultural robot for field. this data base was created by 3 student of the University of UniLaSalle Beauvais. You can fin the LINK of the GOOGLE EARTH "only reading" on the PDF document.</p>
Dataset for the paper "A framework for robotic excavation and dry stone construction using on-site materials"
<p>Stone data from the <i>Science Robotics</i> paper "A framework for robotic excavation and dry stone construction using on-site materials" containing:</p><ul><li>Mesh files of 1,100 stones (quarried boulders, erratics, and concrete debris) that were digitized by the autonomous excavator HEAP<ul><li><a href="https://zenodo.org/api/records/10038881/draft/files/1100%20Unprocessed%20Stone%20Meshes.zip/content">1100 Unprocessed Stone Meshes.zip: </a>Raw mesh files directly from the poisson reconstruction of accumulated LiDAR points, containing some artifacts and floating geometries</li><li><a href="https://zenodo.org/api/records/10038881/draft/files/1100%20Closed%20Stone%20Meshes.zip/content">1100 Closed Stone Meshes.zip: </a>Clean, closed, downsampled meshes</li><li><a href="https://zenodo.org/api/records/10038881/draft/files/Stone_Shape_Properties.csv/content">Stone_Shape_Properties.csv: </a>Properties file with a list of the stone IDs (IDs in the 1xxx and 3xxx range typically correspond to concrete elements) and select shape properties</li></ul></li><li>A dataset of candidate placements from automatically generated stone walls. The candidate placement data zip files contain:<ul><li><a href="https://zenodo.org/api/records/10038881/draft/files/Candidate_Placement_Data-npy.zip/content">Candidate_Placement_Data-npy.zip: </a>SDF (.npy) representation of each candidate, with three channels of 32x32x32 for distances to the stone, the already-placed stones, and the target wall</li><li><a href="https://zenodo.org/api/records/10038881/draft/files/Candidate_Placement_Data-pcd.zip/content">Candidate_Placement_Data-pcd.zip: </a>Point cloud (.pcd) representations of each candidate, with separate files for the placed stone, target wall (search volume), and already-placed stones (where they exist)</li></ul></li><li><a href="https://zenodo.org/api/records/10038881/draft/files/sdf_classifier.zip/content">sdf_classifier.zip: </a>Python examples:<ul><li>Rendering the three channel SDF data to mesh geometry using marching cubes and libigl</li><li>Candidate SDF classification using the pretrained model</li></ul></li><li>Candidate attributes and labels<ul><li><a href="https://zenodo.org/api/records/10038881/draft/files/candidate_attributes_labels.csv/content">candidate_attributes_labels.csv: </a>CSV file containing a list of UUID's corresponding to each candidate placement in the dataset. For each candidate, additional information is included about the dimensions and location of the solution, together with the (subjectively) hand-labelled binary value for placement viability. </li></ul></li><li><a href="https://zenodo.org/api/records/10038881/draft/files/README.md/content">README.md: </a>An additional readme with some details on the attributes file</li></ul><p>If you use this data in your research, please cite the <a href="https://www.science.org/doi/10.1126/scirobotics.abp9758">journal article</a>.</p><p> </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>
The AFFECT-HRI data set: physiological data for affective computing in human-robot interaction with anthropomorphic service robots
<p>We provide a comprehensive data set <strong>AFFECT-HRI </strong>containing physiological data labeled with human affect (i.e., mood and emotion) gathered during an empirical study consisting of a complex human-robot interaction (HRI). A realistic retail scenario served as an experimental environment. In prior research, we showed the necessity to combine the expertise of the research fields of psychology, computer science, and law in the design of a responsible human-centered HRI. Therefore, we implemented five conditions (neutral, transparency, liability, moral, and immoral) covering the perspectives from these three research fields and used two different anthropomorphic service robots. Our study followed a multi-method approach, resulting in a data set containing and combining objective physiological sensor data with subjective human-affect assessments. Additionally, the data set includes insights from 146 participants regarding affect, demographics, and socio-technical questionnaire ratings, as well as robot gestures and robot speech. Our study can be split into three scenes: a consultation regarding products, a request for sensitive personal information while opening a customer account, and a successful or failing handover when buying a mold remover. Thus, this data set offers for the first time the possibility to prove established or develop new emotion recognition methods and technological capabilities for HRI. Further, our data set provides the possibility to combine affective computing with research about robot behavior (gestures, speech, and handover), liability (questionnaire), transparency (questionnaire), and psychological aspects, allowing an encompassing, human-centered view of HRI.</p> <p>The detailed data descriptor has been published in Nature Scientific Data. For more details on the data set, please check the paper below.</p> <p><strong>Please cite the following paper if the dataset is used in a publication:</strong><br>Heinisch, J.S., Kirchhoff, J., Busch, P. <em>et al.</em> Physiological data for affective computing in HRI with anthropomorphic service robots: the AFFECT-HRI data set. <em>Sci Data</em> <strong>11</strong>, 333 (2024). https://doi.org/10.1038/s41597-024-03128-z</p> <p><strong>Acknowledgements</strong><br>This research was conducted as part of RoboTrust, a project of the Centre Responsible Digitality, supported by the Hessian Minister for Digital Strategy and Innovation. The authors would like to thank all participants for their participation in the study. We particularly want to thank Ruth Stock-Homburg for her support and for making Elenoide available. Further, we want to thank Mona Kegel, Vignesh Prasad, and all the research assistants who supported the study. We also thank the leap in time lab for serving as study location. A special thanks goes to Amer Altizini, who supported us by helping to prepare the data for publication. We want to thank Niklas Jungermann for his valuable comments on the statistical evaluation.</p>
Data and trained models for: Human-robot facial co-expression
<p>Large language models are enabling rapid progress in robotic verbal communication, but nonverbal communication is not keeping pace. Physical humanoid robots struggle to express and communicate using facial movement, relying primarily on voice. The challenge is twofold: First, the actuation of an expressively versatile robotic face is mechanically challenging. A second challenge is knowing what expression to generate so that they appear natural, timely, and genuine. Here we propose that both barriers can be alleviated by training a robot to anticipate future facial expressions and execute them simultaneously with a human. Whereas delayed facial mimicry looks disingenuous, facial co-expression feels more genuine since it requires correctly inferring the human's emotional state for timely execution. We find that a robot can learn to predict a forthcoming smile about 839 milliseconds before the human smiles, and using a learned inverse kinematic facial self-model, co-express the smile simultaneously with the human. We demonstrate this ability using a robot face comprising 26 degrees of freedom. We believe that the ability co-express simultaneous facial expressions could improve human-robot interaction.</p>
Simulation, robot codes and figure data from collective phototactic robotectonics
<p>The collective construction of complex architectures by social insects via stigmergy is known to be modulated by spatio-time signals that modulate and are modulated by the environment. Inspired by these observations, we show that a robot collective can successfully nucleate a construction site via a trapping instability and then cooperatively either construct organized structures or de-construct them by (modifying a single parameter associated with the pick-up action of the robot). We quantify these observations in terms of a two-dimensional phase space, encompassing agent-agent interaction (cooperation) and the agent-environment interaction (collection and deposition). Our approach to complex task execution eliminates global representation, planning or optimization/control algorithms in favor of local rules for sensing and action. We observe that collectives can leverage the environment as both a communication channel and a spatio-temporal memory, which is likely to be applicable to broader contexts associated with embodied intelligence(s).</p>
Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"
<pre># Reproduction code and data for the plot of "Synthesizing survival robot behavior through reinforcement learning for homeostasis"<br>Author: Naoto Yoshida<br><br>How to use:<br>1. Clone https://github.com/ugo-nama-kun/journalpaper_robot_2024 from github.<br>2. Extract data_20241119.zip in the cloned repository.<br>3. Run each plot_Fig*.py</pre>
Dataset related to the publication "Accelerating Pinned Specimen Digitization: A Deep Learning Pipeline for Collaborative Robots"
<p>The dataset related to the publication "Accelerating Pinned Specimen Digitization: A Deep Learning Pipeline for Collaborative Robots". 250 training images and 50 annotated test images (YOLOv8 format).</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>
ScienceDex guides
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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.