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2,639 results for “Robotic”
Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot
<p>This video is a supporting material of the paper "Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot". The paper presents a plant root behavior-based approach to defining the control architecture of a plant-root-inspired robot, which is composed of three root-agents for nutrient uptake and one shoot-agent for nutrient redistribution. By taking inspiration and extracting key principles from the uptake of nutrient, movements and communication strategies adopted by plant roots, we developed an uptake–kinetics feedback control for the robotic roots. Exploiting the proposed control, each root is able to regulate the growth direction, towards the nutrients that are most needed, and to adjust nutrient uptake, by decreasing the absorption rate of the most plentiful one. Results from computer simulations and implementation of the proposed control on the robotic platform, Plantoid, demonstrate an emergent swarming behavior aimed at optimizing the internal equilibrium among nutrients through the self-organization of the roots. Plant wellness is improved by dynamically adjusting nutrients priorities only according to local information without the need of a centralized unit delegated for wellness monitoring and task allocation among the agents. Thus, the root-agents can ideally and autonomously grow at the best speed, exploiting nutrient distribution and improving performance, in terms of exploration capabilities and exploitation of resources, with respect to the tropism-inspired control previously proposed by the same authors.</p> <p>The supplementary video (Supplementary Video S1) shows how each agent independently moves according to their internal state and local perception, and the immediate response of the uptake–kinetics mechanism that, as soon as the missing nutrient is inserted in the environment, leads to a decreasing of the imbalance of nutrients in the whole plant.</p>
Figure 6. Forward walking image sequence-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The image sequences of sideward walking and turning are shown in Figure 6 respectively.</p>
Force/Torque Sensor Measurements for Estimating the Mass Center of an Unknown Robot End Effector
<h1>Introduction</h1> <p>This dataset was created as part of a study on a novel geometric method to estimate the mass center of an unknown robot end effector. A conference paper from this study was accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR 2024) [1]. </p> <p>A force/torque sensor (FTS) was attached to the flange of a serial robot, and an unknown end effector was attached to the FTS. Vougioukas [2] described a method to calculate the FTS bias, as well as the mass and mass center using Least Squares Estimation (LSE). His method requires FTS samples from 24 specific orientations of the sensor. See his paper for a description of this calibration method. This dataset was used to evaluate and compare the estimates from the proposed geometric technique to the estimates from Vougioukas' method. </p> <p>The hardware used to generate this dataset were:</p> <ul> <li>KUKA Agilus KR6 R900 sixx (KUKA AG, Germany)</li> <li>ATI Gamma FTS (ATI Industrial Automation, Inc., USA)</li> <li>ATI Netbox (ATI Industrial Automation, Inc., USA)</li> </ul> <h1>Dataset</h1> <p>The robot was used to move the FTS with high precision and accuracy as required by the calibration method from Vougioukas. Each line in the dataset is the measured force and torque, the direction of gravity in the FTS frame, and the orientation of the FTS expressed in the world frame. The lines are ordered and correspond to the orientations described by Vougioukas in his paper. </p> <p><strong>fx,</strong> <strong>fy, fz</strong> - The force components as measured by the FTS.<br><strong>tx, ty, tz </strong>- The torque components as measured by the FTS.<br><strong>gx,gy,gz </strong>- The direction of the gravitational vector in the FTS frame.<br><strong>r11, r12, r13, r21, r22, r23, r31, r32, r33 </strong>- The components of the rotation matrix that represents the FTS orientation in the world frame.</p> <h1>References</h1> <p>[1] A. Skrede, "A Geometric Perspective on Moment Arm Estimation Using Force/Torque Sensors", Accepted for the 2024 IEEE International Conference on Real-time Computing and Robotics (RCAR), Ålesund, Norway, June 2024 </p> <p>[2] S. Vougioukas, “Bias Estimation and Gravity Compen- sation For Force-Torque Sensors,” in Recent Advances in Simulation, Computational Methods and Soft Computing. WSEAS Press, 2001, pp. 82–85. </p>
Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users' Readings
<p># Dataset of adaptive Children-Robot Interaction for Education based on Autonomous Multimodal Users’ Readings </p> <p>## Background</p> <p>This dataset is generated from multiple interactions between a Social Robot (NAO) and 5th grade students from a private school in São Paulo, Brazil. </p> <p>In the interaction, the robot approached the content that teachers were approaching at the time with the participants students about the wasting system in Brazil.</p> <p>The measures here are the readings that the R-CASTLE system did for each answer the students gave to the questions the robot asked. </p> <p>For more information about how these measures were collected, please refer to this thesis at: https://doi.org/10.11606/T.55.2020.tde-31082020-093935</p> <p>Since the goal of the R-CASTLE is to provide autonomous adaptation, we built a ground-truth dataset based on human feedback of an expert in education operating the robot in loco. The person was teleoperating the robot to change its behaviour (or not) according to observed values of the participants as Face Gaze, Facial emotion displayed, Number of spoken words, the correctness of the answer (based on pre-defined answers), and the time students took to answer. These measures are the 5th columns of this csv file. The evaluator could decide to increase (1), maintain (0), or decrease (-1) the level of difficulties of the following questions depending on the mentioned observed measures. This is the human true label, stored in the 6th column. </p> <p>## Description:<br>Each row of this file is a tuple of the autonomous reading the robot made in the 5 first columns, plus the true label in the 6th row (True Value) and the Final Crisp Value using fuzzy classification in the 7th row (Final Crisp Value).</p> <p><br>Deviations (integer): number of face deviations of the participant during the question answering identified by the system.</p> <p>EmotionCount (integer): a balance between "good" and "bad" emotions (good - bad) identified by the system.</p> <p>NumberWord (integer): number of words comprised in the sentence the participant gave.</p> <p>SucRate/Ans/RWa: (between 0 and 1, where 0 is completely wrong and 1 is completely right): The success rate of the participant’s answer to that question, based on the expected answer programmed by their teachers.</p> <p>Time2ans (float): The time spent to answer the question since the robot has finished the question until the end of the participant’s speech in seconds.</p> <p>True Value (-1, 0, 1): Ground-truth value. Value of adaptation chosen by the human observing the interaction if the system needed to decrease, maintain, or increase the level of difficulty of asked questions. </p> <p>Final Crisp Value (float): value of calculated fuzzy output based on the implementations in the paper: https://doi.org/10.1145/3395035.3425201</p> <p><br>## Creators <br>Daniel Tozadore: dtozadore@gmail.com<br>Roseli Romero: rafrance@icmc.usp.br</p> <p><br>## License: <br>[Creative Commons Licenses](https://creativecommons.org/share-your-work/cclicenses/)</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>
Usability data (Drapebot Robot Cell/Dallara)
<p>In the Drapebot project, a worker collaborates with a large industrial manipulator in two tasks: collaborative transport of carbon fibre patches and collaborative draping.</p> <h3>Data collection</h3> <p>At Dallara, each participant was introduced to the robot work cell and received verbal instructions on how to perform the collaborative draping task. Each participant performed the task ten times. The ten tasks were performed with the same cut piece. Once the draping along the mould was complete, the participant would return the cut piece to the pick-up table before signaling the robot to continue by raising their hand (gesture condition). After 10 task repetitions, the participants filled out the questionnaire battery.</p> <h3>Data organization</h3> <p>The data consists of an Excel file with six sheets:</p> <p>1. SUS: Results from Standard Usability Scale (Brooke et al. 1996)</p> <ul> <li>Column 1: Test site</li> <li>Column 2: participant ID</li> <li>Column 3: User interface type (Gesture)</li> <li>Column 4-13: SUS items</li> <li>Column 14: SUS score between 1-100</li> </ul> <p>2. UMUX: Results from Usability Metric for User Experience (Finstad 2010)</p> <ul> <li>Column 1: Test site</li> <li>Column 2: participant ID</li> <li>Column 3: User interface type (Gesture)</li> <li>Column 4-7: UMUX items</li> <li>Column 8: UMUX score between 1-100</li> </ul> <p>3. Trust: Results from Trust perception scale - HRI (Schaefer 2016)</p> <ul> <li>Column 1: Test site</li> <li>Column 2: participant ID</li> <li>Column 3: User interface type (Gesture)</li> <li>Column 4-17: Questionnaire items</li> <li>Column 8: Trust score between 1-100</li> </ul> <p>4. Trust: Results from Trust in industrial human robot collaboration (Charalambous, et.al. 2016)</p> <ul> <li>Column 1: Test site</li> <li>Column 2: participant ID</li> <li>Column 3: User interface type (Gesture)</li> <li>Column 4-13: Questionnaire items</li> <li>Column 8: Trust score between 1-100</li> </ul> <p>5. NASA TLX: Results from Task Load Index (Hart 1986)</p> <ul> <li>Column 1: Test site</li> <li>Column 2: participant ID</li> <li>Column 3-20: Questionnaire items</li> <li>Column 21: TLX score between 1-100</li> </ul> <p>6. UTAUT: Results from Unified Theory of Acceptance and Use of Technology (Venkatesh et al. 2003)</p> <ul> <li>Column 1: Test site</li> <li>Column 2: participant ID</li> <li>Column 3-33: Questionnaire items</li> <li>Column 34-41: Subcategory scores from 1-100</li> </ul> <h3>References:</h3> <p>J. Brooke et al., “Sus-a quick and dirty usability scale,” Usability evaluation in industry, vol. 189, no. 194, pp. 4–7, 1996</p> <p>G. Charalambous, S. Fletcher, and P. Webb, “The development of a scale to evaluate trust in industrial human-robot collaboration,” International Journal of Social Robotics, vol. 8, pp. 193–209, 2016.</p> <p>S. G. Hart, “Nasa task load index (tlx),” 1986.</p> <p>K. Finstad, “The usability metric for user experience,” Interacting with computers, vol. 22, no. 5, pp. 323–327, 2010</p> <p>K. E. Schaefer, Measuring Trust in Human Robot Interactions: Development of the “Trust Perception Scale-HRI”. Boston, MA: Springer US, 2016, pp. 191–218.</p> <p>V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS quarterly, pp. 425–478, 2003.</p>
European funded projects related to integrated robotic sensing
<p>This repository contains the metadata of 1371 projects in tabular form (combinedProjectData.xlsx). The data was extracted from the European Union’s COmmunity Research and Development Information Service (CORDIS) repository.</p> <p>The dataset was used to support a review of the latest advancements in integrated robotic sensing. CORDIS was interrogated using a Boolean search, combining multiple chosen search terms using precise logical relationships, such as AND and OR. This search approach was used to obtain precise and relevant search results by specifying the relationships among the search terms, saving time and effort while minimising the likelihood of encountering irrelevant or unrelated material. The following Boolean search string was used: “(‘robot’ OR ‘robotic’ OR ‘robotically’ OR ‘roboti?ed’) AND (‘non-destructive’ OR ‘inspection’ OR ‘evaluation’ OR ‘NDT’ OR ‘NDE’ OR ‘sensing’ OR ‘sensor’)”. This resulted in searching projects whose title and short description (teaser) contained at least one of the words in the first set of brackets and at least one in the second set. Note that the “?” in ‘roboti?ed’ allowed looking for the presence of both the British English word “robotised” and the respective American English version “robotized”.</p> <p>Additionally, the search results were filtered according to the funding schemes. For the sake of reviewing the recent landscape, only projects funded through the HORIZON 2020 and HORIZON EUROPE schemes were considered. The described filtered search returned 1371 projects. The resulting metadata was extracted from the CORDIS repository for each of the found projects: the project start date, the end date, the total cost, the total EU contribution, the fields of science related to the project, the coordinating institution, and the participating institutions. The fields of science of each project are given as a list of strings detailing the fields of science related to the project. Each string shows a variable-depth hierarchy from the broadest classification to specific fields (e.g., “engineering and technology/materials engineering/composites”), following the hierarchical framework adopted by the European Commission. Finally, whereas each project has one and only one coordinator, it can have none, one or multiple participants. For the coordinator and each participant (if present), the following information was extracted: country of the coordinating/participating institution, amount of EU contribution received, amount of other funds available to the institution and the project outcome in terms of peer-reviewed journal papers, conference contributions and filed patents.</p> <p>Thus, the project metadata extracted from CORDIS was thoroughly analysed. The "fieldsOfScience_SunburstPlot.xlsx" file contains a sunburst chart that offers a lucid overview of the diverse scientific disciplines of the selected projects. The analysis of the fields of science strings has revealed a hierarchical depth going up to the seventh classification level, showing great permeance of robotic NDT and robotic sensing into numerous and specific fields.</p>
Event-Driven Figure-Ground organisation model for the humanoid robot iCub
<p>Figure-ground organisation is a perceptual grouping mechanism for detecting objects and boundaries, essential for an agent interacting with the environment. Current figure-ground segmentation methods rely on classical computer vision or deep learning, requiring extensive computational resources, especially during training. Inspired by the primate visual system, we developed a bio-inspired perception system for the neuromorphic robot iCub. The model uses a hierarchical, biologically plausible architecture and event-driven vision to distinguish foreground objects from the background. Unlike classical approaches, event-driven cameras reduce data redundancy and computation. The system has been qualitatively and quantitatively assessed in simulations and with event-driven cameras on iCub in various scenarios. It successfully segments items in diverse real-world settings, showing comparable results to its frame-based version on simple stimuli and the Berkeley Segmentation dataset. This model enhances hybrid systems, complementing conventional deep learning models by processing only relevant data in Regions of Interest (ROI), enabling low-latency autonomous robotic applications.</p>
Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) Dataset - Anonymized
<p>Human-Robot Interaction Conversational User Enjoyment Scale (HRI CUES) and this corresponding dataset aim to provide tools for measuring user enjoyment from an external perspective to supplement self-reported user enjoyment responses in human-robot interaction research, with future potential application for autonomous detection of user enjoyment in real-time in robots and agents for adapting conversations contingently to provide enjoyable and long-lasting interactions.</p> <p>The dataset consists of 25 older adults' (12 men, 13 women) open-domain dialogue with an autonomous companion robot with an integrated large language model (GPT-3.5, text-davinci-003) from participatory design workshops conducted in March 2023. The conversations are annotated for user enjoyment based on HRI CUES by 3 expert annotators, as described in the paper (arXiv:2405.01354). Robot architecture and participatory design workshops are described in DOI: 10.21203/rs.3.rs-2884789/v1.</p> <p><strong>Exchanges</strong> file contains the participant ID, the number of the turn (conversation exchange by Robot-Participant response), the start and end of the turn, the anonymized transcript for the turn, and three annotator scores for the user enjoyment in the exchange. </p> <p><strong>Overall </strong>file contains the participant ID, self-reported user perception scores from the questionnaire ("I was satisfied with my conversation with the robot", "It was fun talking to the robot", "The conversation with the robot was interesting", "It felt strange talking to the robot") and three annotator scores for the user enjoyment in the overall interaction.</p> <p>The conversations are in Swedish. Participants' mean age is 74.6 (SD=5.8). 20 participants had no prior interaction with a robot, and only one had previously talked with a robot. The average interaction duration is 7.4 min (SD=1.5) with 12 to 29 turns. Each turn lasts 5 to 61 seconds (M=17.7, SD=7.2). The total duration of the interactions is 174 min, corresponding to 590 turns. </p> <p><em>Videos of the interactions are available upon request, contingent upon a signed agreement to maintain data confidentiality in accordance with GDPR regulations.</em></p> <p>Anonymization macros:</p> <p>[P_NAME]: Participant's name (may include surname). The robot always uses the first name even when the surname is given.</p> <p>[NAME_REMOVED]: A name of another person mentioned by the participant.</p> <p>[LOCATION_REMOVED]: Small town/village/area where the participant lives or lived.</p> <p>[MEDICAL_INFO_REMOVED]: Medical information shared by the participant.</p> <p>[AGE_REMOVED]: Participant's or other person's age.</p> <p>[INFORMATION_REMOVED]: Sensitive information shared by the participant.</p> <p>[MISTAKEN_NAME]: Speech recognition error resulted in the name being misunderstood.</p>
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
<p><strong>Introduction</strong></p> <p>The Robot Inverse Dynamics Dataset is a collection of trajectories and joint torque measurements of two robotic manipulators, a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Additionally, the dataset contains the inverse dynamical models and other useful quantities learned to reproduce the results reported on our reference paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification". The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~700MB.</li> <li>The dataset contains</li> <ul> <li>collections of joint trajectories and joint torque measurements of two robot manipulators: a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL.</li> <li>models of the inverse dynamics of the two manipulators learned on the datasets</li> </ul> <li>The main directories are</li> <ul> <li>Simulated_PANDA/ contains the trajectories, models and results obtained on different configurations of a Franka Emika PANDA robot, simulated in sympybotics.</li> <li>Robots/ contains the data, models and results obtained on two real robots, a Franka Emika PANDA and a Mitsubishi Electric MELFA RV4FRL</li> </ul> <li>See the README.md file for a detailed description of the directories.</li> </ul> <p><strong>Other Resources</strong></p> <p>Python code to train the models and reproduce the results in the paper are available <a href="https://github.com/merlresearch/LIP4RobotInverseDynamics">here</a>.</p> <p><strong>Citation</strong></p> <p>If you use the Robot Inverse Dynamics dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{ title={A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}, author={Giacomuzzo, G., Dalla Libera, A., Romeres, D.,}, booktitle={IEEE Transaction on Robotics}, year={2024} } </code></pre> <p><strong>License</strong></p> <p>The Robot Inverse Dynamics dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Validation Videos - Robotic System for Reproducible Mobile Networking Experimentation in Anechoic Chambers (Master Thesis)
<p><strong>Note on Robot's Referential:</strong></p> <p>The robot's referential can be inferred in the recording via the "Safety Position." The safety position is the same for both the Digital Model (Gazebo) and the Real Robot (Joint Position = [0.0, -1.57, 1.57, 0.0, 0.0, 0.0]).</p> <p>In the safety position, the robot is approximately aligned with the X-axis, with its end-effector on the positive side of the axis. The end-effector faces perpendicular to the Y-axis. The positive Z-axis points upwards towards the ceiling.</p> <p> </p>
Supporting material for the paper "Testing Robot Challenge: a Serious Game for Testing Learning"
<p><strong>Experimental data</strong></p> <p>Table reporting the frequencies of student answers to the 29 questions (1 = Strongly Disagree, 5 = Strongly Agree) to the survey about the use of the Testing Robot Challenge Web application.</p> <p> </p> <p><strong>Figures</strong></p> <ul> <li>Game Main Page</li> <li>End Game Page</li> <li>Game mechanics of the Testing Robot Challenge</li> <li>Game Selection Page</li> <li>Home Page</li> <li>Software Architecture</li> </ul>
Aerial Imagery From Flights in Robotics Simulator
<p>Dataset contains aerial images captured in a simulated 3D environment. Ortho photo images from USGS Aerial Imagery dataset was used as ground view. Gazebo simulator with PX4 flight controller software and a plane model was used to simulate flights at different altitude and trajectories over different maps.</p> <p>Each dataset is provided in a zip file, named under two character and number abbreviation, which can be interpreted by the first letter for map type (U - urban, F - forest), second letter stands for trajectory type (L - straight line, C - circular trajectory, R - rectangular trajectory) and the number stands for altitude in meters, e.g. FC-300, means forest map, circular trajectory at 300 meters altitude. Alongside image data, a text file containing CSV data is included, which contains aircraft attitude and geographical information of each image.</p> <p>Additionally, maps used for simulation environment are included in this dataset, maps of the same area captured on different years are also included, which can be used to evaluate algorithms matching against maps.</p> <p>ROS Publisher node is available in GitHub: <a href="https://github.com/jureviciusr/AIRDatasetPublisher">https://github.com/jureviciusr/AIRDatasetPublisher</a></p>
Video for Robotic assisted milling for increased productivity
<p>Video in MP4 - Robotic Assisted Milling.mp4</p>
Videos of a robot controlled by Belousov-Zhabotinsky liquid marbles. Supplementary materials to the paper "Belousov-Zhabotinsky liquid marbles in robot control".
<p>Videos and snapshots of experiments with robot controlled by liquid marbles made of Belousov-Zhabotinsky solution. </p> <p>Supplementary materials for the paper </p> <p> </p> <p>M.-A. Tsomapanas, C. Fullarton, A. Adamatzky. Belousov-Zhabotinsky liquid marbles in robot control. (2018). </p> <p>Abstract </p> <p>We show how to control the movement of a wheeled robot using on-board liquid marbles made of Belousov-Zhabotinsky solution coated by polyethylene powder. Two platinum-iridium electrodes were inserted in a marble and the electrical potential recorded was used to control the robot's motor. We stimulated the marble with a laser beam. It responded to the stimulation by pronounced change of the electrical potential output. The electrical output was detected by robot. The robot was changing its trajectory in response to the stimulation.</p> <p>This research was supported by the EPSRC with grant EP/P016677/1 ``Computing with Liquid Marbles''.</p> <p> </p>
Surface Type Classification for Autonomous Robot Indoor Navigation - Dataset
<p>Surface Type Recognition with Inertial Measurement Unit (IMU).</p> <p>The dataset contains time series samples with 10 features each, related to orientation, velocity and acceleration. Each time series (of lenght 128) includes its corresponding surface type annotation.</p> <p>The data has been also divided in groups for easier cross-validation (80 groups present)</p> <p>A total of 9 different surface types are present in the dataset.</p> <p>"X_data.npy" contains the time series samples of dimension 7626x10x128<br> "label.npy" contains the label information for each sample (dimension 7626x1)<br> "groups.npy" contains the group information for each sample (dimension 7626x1)<br> "details.csv" contains for each sample the group information and the corresponding label</p> <p> </p>
Evolution of Formal Model-based Assurance Cases for Autonomous Robots: Supplemental Material
<p>This report contains supplemental material for the paper Evolution of Formal Model-based Assurance Cases for Autonomous Robots accepted at Software Engineering and Formal Methods 2019 in Oslo. This material provides more details about the two discussed assurance case patterns, their implementation in Isabelle/SACM the instantiation of these patterns for the presented example, as well as Isabelle skripts for the theoretical part and the example.</p>
Measurement and identification of the joint stiffness on a serial articulated industrial robot
<p>This document exemplifies elastostatic compliance calibration on an articulated industrial robot, which has been calibrated at KTH Royal Institute of Technology in 2019 using procedure outlined in the CWA-17384. All data processing is done in Matlab 2018b® using the Peter Corke’s as well as Computer Vision System toolbox for robotics. In case of questions do not hesitate to send an e-mail to <a href="mailto:theissen@kth.se">theissen@kth.se</a> to obtain data and algorithms in c++ or other formats.</p>
Probing result for robotic assisted machining form error paper
<p>This is the probing result during our maching trials for form error prediction for robotic assisted machining.</p>
Data for the prediction of chatter vibrations in robotic milling of aluminium parts based on previous experiences using neural network
<p>This data has been used for the validation of the software developed by DFKI in collaboration with IDEKO for the prediction of stability in robotic milling of aluminium parts, in the framework of COROMA research project funded by the European Union. www.coroma-project.eu</p> <p>The source of information is stability lobes obtained from FRFs obtained mixing by receptance coupling experimental FRFs of the robot, spindle and toolholder with FRFs of the tool obtained analitycally using beams theory. Real machinings have not been done since they would be very time consuming. Once the stability lobes where available random sampling has been done in the lobes between certain boundaries of axial depth of cut and spindle speed to represent machining with different conditions.</p> <p>The information contained here includes:</p> <p>- Data sets for different conditions, with tools of different diameters and different number of cutting teeth. (in the naming of the folder D represents diameter, Z represents number of teeth).</p> <p>- Most of the data sets also include figures with the milling stability lobe charts for different radial depths of cut and different diameters and number of teeth. In these figures the random sampling representing machining tests has been marked with a black X.</p> <p>- There are also versions of the data sets with different number of samples (20 or 40) in order to test the prediction algorithm with a different number of information.</p> <p>- In the data sets an extended version has been created, representing the know-how of the operator that if a machining is unstable all the machinings with higher axial depth of cut will be unstable, and if a machining is stable all the machinings with lower axial depth of cut will be stable.</p> <p>- Companion documents in PDF format in order to provide more detailed information on the datasets and results.</p> <p>Keywords: Milling, machining, vibration, chatter, stability, prediction, neural network, robot, robotic, AI, artificial intelligence.</p> <p>www.ideko.es<br> www.dfki.de</p> <p>Asier Barrios<br> IDEKO research centre<br> Arriaga Kalea, 2<br> Elgoibar 20870, Spain<br> Phone: +34 943748000<br> abarrios@ideko.es</p> <p>October 2019</p>
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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)
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DANDI Archive for NWB datasets
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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.