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2,639 results for “Robotic”
3rd National Competition of Line Follower and Fire Fighter Robots
<p>S.A. Tafrishi and H. Karimkhani. “3rd National Competition of Line Follower and<br /> Fire Fighter Robots”. Tabriz, Iran , First line follower programmed with Cortexm3<br /> Processor, Rougeknight Robot, 20-21 Sep. 2012.</p> <p>Link to board designs and all the codes:<br /> https://www.dropbox.com/sh/ag1m801kepnhadf/AACJF5FN6j7897DpWxmF82b1a?dl=0</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>
Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots
<h2><strong>License</strong></h2> <p>The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>.</p> <h2><strong>Summary</strong></h2> <p>The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions. </p> <p>It is obligatory to cite the following paper in every work that uses the dataset: <br><strong>Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. <em>Sci Data</em> 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3</strong></p> <h2><strong>Data description</strong></h2> <p>The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people.</p> <h2><strong>Dataset structure<br></strong></h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> <p><em>Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png.</em></p> <p>Total Images (Images per Place)</p> <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2><br>Further information</h2> <p>For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.</p>
Data for: Mapping aids using output-directed programming increase novices' performance in programming mobile robotic systems
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Predictive Design of Ultrastretchable Electrodes with Strain-Insensitive Performance via Robotics- and Machine Learning-Integrated Workflow
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CSAPSO-BPNN based modeling of end airbag stiffness of nursing transfer robot
<p>The use of nursing transfer robots is a vital solution to the problem of daily mobility difficulties for semi-disabilities. However, the fact that care-receivers have different physical characteristics leads to force concentration during human-robot interaction, which affects their comfort. To address this problem, this study installs an array of double wedge-shaped airbags onto the end-effector of a robot, and analyses airbag mechanical properties. Firstly, this study performed the mechanical testing and data collection of the airbag, including its external load and displacement, at various gas masses. Then the performance of the Back Propagation (BP) neural network is improved by using chaos (C) theory and simulated annealing particle swarm optimization (SAPSO), resulting in the establishment of the CSAPSO-BP neural network. By this method, a fitting model is developed to determine the mechanical parameters of the wedge-shaped airbag stiffness, and the fitting relation of external load-displacement is obtained. Data analyses show that the wedge-shaped airbag stiffness increases quadratically, linearly, and with a constant rate as the gas mass increases. The airbag stiffness regulation and model describe its 3 distinct phases with quadratic, linear, and linear invariant characteristics as the gas mass changes. These findings contribute to the structural optimization of airbags.</p>
CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification
<p>Video of submitted paper entitled "CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification"</p>
Weta Robot Autonomous Mission with AgRob4Demeter Supervision and Mobile User Interface
<p>This is a demonstration video of <a href="https://gitlab.inesctec.pt/agrob/agrob4demeter/-/tree/ros-foxy?ref_type=heads">AgRob4Demeter </a>Supervision System during a spraying autonomous mission of <a href="https://scorpion-h2020.eu/weta-robot-from-scorpion-wins-the-if-prize/">Weta </a>Robot, with a mobile user interface to follow the mission execution. </p><p> </p>
AgRob Robot's Control Systems
<p><a href="https://zenodo.org/api/records/10093413/draft/files/Weta_Robot_Mission.mp4/content">Weta_Robot_Mission</a> - Demonstration of an agricultural robot (WETA) in an autonomous spraying mission following a trajectory with <a href="https://gitlab.inesctec.pt/agrob/model_e/parametric_trajectories_stack/-/tree/foxy_devel?ref_type=heads">Parametric Trajectory controller </a>at Quinta do Seixo, Portugal. </p><p><br><a href="https://zenodo.org/uploads/10103970">FollowMe_Demo </a>- Demonstration of <a href="https://gitlab.inesctec.pt/agrob/follow_me/virtual_leash_stack/-/tree/fixed_trajectories?ref_type=heads">Follow ME</a> system at Quinta do Seixo, Portugal. </p>
Complete Data Set for the Master Thesis: Acceptance of Robotics in Tourism
<p>Complete data set for the master thesis "Acceptance of Robotic Technology in Tourism" including the SPSS file. </p><p><strong>Abstract</strong></p><p>Robotics, artificial intelligence, and automation have undergone significant evolution across various industries, from manufacturing to healthcare. The tourism industry is also embracing these technological advancements, accelerated by factors such as the global pandemic and labor shortages. Despite their transformative potential, the academic exploration of robots in tourism remains limited, particularly regarding customer attitudes and acceptance.</p><p>This master thesis addresses the gap in research by investigating the acceptance of robots in the tourism industry. The study aims to identify the factors influencing acceptance, explore potential initiatives for increased acceptance over time, and highlight challenges in integrating robotics into tourism. The research questions guide the exploration of these facets. The literature review reveals a scarcity of research on robot acceptance in tourism, with existing studies often focusing on engineering aspects rather than social science perspectives. To address this topic, this thesis formulates a comprehensive research goal, seeking to understand the factors shaping acceptance and offering insights into the opportunities and challenges of robot integration in tourism. The thesis then explores the fundamental aspects of robotics and their potential applications in tourism, presenting a micro, meso, and macro level analysis. A hypothesis model is developed based on prior research, laying the groundwork for data collection through surveys in three distinct scenarios. Upon gathering and processing the survey data using SPSS, the results are analyzed, with a focus on influencing factors affecting robot acceptance. The findings are compared to established models such as TAM, UTAUT, and the Service Robot Acceptance Model.</p><p>Keywords: robotics, artificial intelligence, adoption, technology acceptance model, tourism, perceived usefulness, perceived ease of use, anthropomorphism </p><p><strong>Use not permitted without permission of the author.</strong></p>
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>
DEDICAT 6G Robot metrics for different levels of intelligence (computation) distribution
<p>Battery consumption, CPU and RAM metrics for locobots used in testing of intelligence distribution. </p>
Dataset to "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture"
<p>This dataset is related to the paper "A method for the reproduction of cello bow kinematics using a robotic arm and motion capture".</p> <p>Contents:</p> <p>Recordings comparing a human performance and two robot performances (named case A and case B).</p> <ul> <li>Synchronised motion-capture data (time, bow markers, nut marker and bridge marker; Fs = 240 fps - .csv files)</li> <li>Synchronised audio recordings (sound in front of the instrument; Fs = 44100 Hz - .wav files)</li> <li>Music score</li> <li>Read-me file with further details</li> </ul>
Industrial application of Multi-Robot Partial Destructive Disassembly Line Balancing for Multi-Product Scenarios
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Mechanoreceptive soft robotic molluscoids made of granular hydrogel-based organoelectronics
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Efficient CO₂ and CH₄ Flux Monitoring in Soil Microcosms Using an Automated Chamber with a Cartesian Robot
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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>
Establishing autonomous testing of bacterial expression systems using a robotic platform.
<p>Data of fluorescence and optical density measurements for cultivations of <em>E. coli</em> and <em>B. subtilis</em> in an active learning experiment using a life science automation platform. Additionally SBOL and Genbank files of the genetic constructs are supplied. Furthermore scripts for running the experiment on a Analytik Jena automation platform as well as the software controlling the experiment.</p>
Data for "Edible Aquatic Robots with Marangoni Propulsion"
<p>This dataset contains all the data and CAD models needed to replicate the study presented in "<span>Edible Aquatic Robots with Marangoni Propulsion</span>".</p>
Data set from: Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial.
<p>Data set from the paper "Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial" doi: <a href="https://dx.doi.org/10.3390%2Fmedicina57070713">10.3390/medicina57070713</a></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.