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2,639 results for “Robotic”
Figure 8. Artificial Intelligence Algorithm-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>This module receives information from Artificial Intelligent unit. Total functions about<br> Robot Behavior such as stability motors actions, robot path planning, turn camera, walking,<br> shooting, dribbling; motion and etc are controlled in this section.</p>
Figure 1. PERSIA Humanoid Robot in Robocup IranOpen2010 Competition-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>In this paper, we will at first describe the general hardware design of the PERSIA Humanoid<br> Robocup Team, (section 2) and after that focus on our scientific approaches in sensor fusion and<br> learning (section 3). Finally, section 4 concludes this paper. This document describes the current<br> state of the project as well as the intended development for the RoboCup 2010 competition.</p>
Figure 3. (a) Our Humanoid soccer robot, (b) Overview of the Control System-Design and Implementation of an Autonomous Humanoid Robot Based on Fuzzy Rule-Based Motion Controller
<p>The PERSIA Humanoid robot designed for has multipurpose capability. This robot<br> equipped with main board for motion control, vision sensor, other balancing sensors, servo motors<br> and etc. Figure 3 shows picture of the robot and overview of the Persia humanoid robot control<br> system.</p>
A model-based approach to acoustic reflector localization with a robotic platform
<p>Constructing a spatial map of an indoor environment, e.g., a typical office environment with glass surfaces, is a difficult and challenging task. Current state-of-the-art, e.g., camera- and laser-based approaches are unsuitable for detecting transparent surfaces. Hence, the spatial map generated with these approaches are often inaccurate. In this paper, a method that utilizes echolocation with sound in the audible frequency range is proposed to robustly localize the position of an acoustic reflector, e.g., walls, glass surfaces etc., which could be used to construct a spatial map of an indoor environment as the robot moves. The proposed method estimate the acoustic reflector’s position, using only a single microphone and a loudspeaker that are present on many socially assistive robot platforms such as the NAO robot. The experimental results show that the proposed method could robustly detect an acoustic reflector up to a distance of 1.5 m in more than 60% of the trials and works efficiently even under low SNRs. To test the proposed method, a proof-of-concept robotic platform was build to construct a spatial map of an indoor environment.</p> <p>This dataset is made available with IROS 2020 paper: https://ieeexplore.ieee.org/abstract/document/9341437</p> <p>code could be found on Github: https://github.com/irtiq7/iROS2020</p>
A framework for spatial map generation using acoustic echoes for robotic platforms
<div> <div> <div> <div> <div> <div> <p>In this work, we present a framework for constructing a spatial map of an indoor environment using the concept of echolocation. More specifically, we propose a non-linear least squares (NLS) estimator which is combined with a spatial filtering technique, e.g., beamforming, to estimate both the time-of-arrival (TOA) and direction-of-arrival (DOA) of the acoustic echoes. The proposed framework is complemented with an echo detector to classify a spurious estimate and an acoustic reflector, i.e., a wall. Based on these estimators, we propose two algorithms that complement existing range sensors and aid robotic platforms in acoustic reflector localization and mapping: single-channel localization and mapping (ScLAM) and a multi-channel localization and mapping (McLAM). Compared to commonly used sensors, such as lidar, cameras and ultrasonic sensors, our proposed model-based approach can detect transparent surfaces that are typically found in an office environment and could work in audible frequency ranges. A proof-of-concept robotic platform was built to test our algorithms. According to our evaluation, both qualitative and quantitative experiments reveal that the proposed methods can detect an acoustic reflector up to a distance of 1.5 m at a signal-to-diffuse-noise ratio (SDNR) of 0 dB in a simulated environment and 10 dB in a real environment with an accuracy of 80%.</p> </div> </div> </div> </div> </div> </div>
Dataset fOr herding and predatoR detectIon with the use of robotS (DORIS)
<p>Dataset with images and annotations (semantic masks for YOLO and other architectures) of sheep, wolves, persons and depth images in order to carry out experiments of robots used for herding sheep and detect potential predators as wolves. It is divided into two different URLs since it is greater than 50GB. This dataset contains images of persons, wolves, sheep and the depth images that can be used to simulated environments and they have the depth estimated from the images using Depth Anything. We gratefully acknowledge the financial support of Grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR".</p> <p>Since the original dataset exceeds 50 GB (https://doi.org/10.57967/hf/2059), there are three datasets associated:</p> <ul> <li>a dataset that contains YOLO annotations for these images.</li> <li>a second dataset contains images of Sheep, (10.5281/zenodo.11313800)</li> <li>a third dataset contains Person, Wolf classes, and depth maps generated using Depth Anything, which can be utilized for simulating external environments or outdoor navigation. (this dataset 10.5281/zenodo.11313966)</li> </ul>
Dataset fOr herding and predatoR detectIon with the use of robotS (DORIS)
<p>Dataset with images and annotations (semantic masks for YOLO and other architectures) of sheep, wolves, persons and depth images in order to carry out experiments of robots used for herding sheep and detect potential predators as wolves. It is divided into two different URLs since it is greater than 50GB. This dataset contains images of persons, wolves, sheep and the depth images that can be used to simulated environments and they have the depth estimated from the images using Depth Anything. We gratefully acknowledge the financial support of Grant TED2021-132356B-I00 funded by MCIN/AEI/10.13039/501100011033 and by the "European Union NextGenerationEU/PRTR".</p> <p>Since the original dataset exceeds 50 GB (https://doi.org/10.57967/hf/2059), there are three datasets associated:</p> <ul> <li>a dataset that contains YOLO annotations for these images.</li> <li>a second dataset contains images of Sheep, (this dataset 10.5281/zenodo.11313800)</li> <li>a third dataset contains Person, Wolf classes, and depth maps generated using Depth Anything, which can be utilized for simulating external environments or outdoor navigation. ( 10.5281/zenodo.11313966)</li> </ul> <p> </p>
Sketch With the Robot - data
<p>This repository holds the data collected during the experimental sessions for the project "Sketch it for the robot! How child-like robots' joint attention affects humans' drawing strategies" accepted at ICDL (2024) Conference. </p> <p>The repository contains the drawings of the categories, the raw data file, the quantitative data file and elaborated data for the analysis.</p> <ul> <li>The folder /Experimental_data contains: <ul> <li>The folder /no_robot contains all the the drawings (.png format), produced in the <strong>individual condition</strong>, organized in subfolders. Each subfolder corresponds to a participant and the presence of the 'i' in the name means the partcipant was Italian , while the presence of the 's' means that the participant was Slovakian.</li> <li>The folder /robot contains all the the drawings (.png format), produced in the <strong>robot condition</strong>, organized in subfolders. As the previous case, the letter 'i' and 's' stands for the nationality of the participant.</li> <li>The file <em>all_drawings.ndjson </em>contains all the raw data (all the coordinates and timestamp of each drawing), where we also included the features extraction data. Thanks to the raw data (triplets (x, y, t)) it is possible to extract all the features needed.</li> <li>The file <em>quantitative_data.ndjson </em>contains all the quantitative rankings data collected during the experiment (<em>category, </em><em>condition, </em><em>latency time, total time, number of strokes, enjoyment_rank, difficulty_rank, likeability_rank</em>).</li> </ul> </li> <li>The folder /Analysis_data contains the datasets used for the analysis. They are basically subsets specifically generated for the different analysis, containing all the features extraction and questionnaires data: <ul> <li>The file <em>all_drawings_social_influence_all.csv </em>is the 'mother' file containing all the relevant data.</li> <li>The file <em>all_drawings_social_influence_no_repetitions.csv </em>contains the data relative to the categories that were drawn just 1 time. The file has been generated to study the effects of the social influence due to the robot's presence.</li> <li>The file <em>all_drawings_social_influence_only_repetitions.csv </em>contains data relative just to the categories that were drawn more than 1 time. This file has been generated to compare results between categories repeated 2 and 3 times, to highlight the effect introduced by repeating a category for the third time.</li> <li>The file <em>all_drawings_repetition_influence.csv </em>contains data relative to just the categories repeated 3 times, to study the repetition effect.</li> </ul> </li> </ul> <p>It is possible to finde the code used to collect the data at the DOI: 10.5281/zenodo.10944480</p>
Movement data set for trust assessment (Drapebot robot cell/Profactor)
<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. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).</p> <p>For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.</p> <p>The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).</p> <p>Items for Trust perception scale - HRI, Schaefer 2016:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:</p> <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do </li> <li>The speed at which the gripper picked up and released the components made me uneasy </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul> <p> </p> <p><span>K.</span><span> </span><span>E.</span><span> </span><span>Schaefer,</span><span> </span><span>Measuring</span><span> </span><span>Trust</span><span> </span><span>in</span><span> </span><span>Human</span><span> </span><span>Robot</span><span> </span><span>Interactions: </span><span>Development</span><span> </span><span>of</span><span> </span><span>the</span><span> </span><span>“Trust</span><span> </span><span>Perception</span><span> </span><span>Scale-HRI”</span><span>.</span><span> </span><span>Boston, </span><span>MA:</span><span> </span><span>Springer</span><span> </span><span>US,</span><span> </span><span>2016,</span><span> </span><span>pp.</span><span> </span><span>191–218.</span><span> </span></p> <p><span>G. Charalambous, S. Fletcher, and P. Webb, “The development of </span><span>a scale to evaluate trust in industrial human-robot collaboration,” </span><span>International Journal of Social Robotics</span><span>, vol. 8, pp. 193–209, 2016.</span></p>
Movement data set for trust assessment (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. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).</p> <p>For this data set, data has been collected for the draping task in a near-production setting from 5 participants all familiar with carbon-fibre draping. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 5 files for 5 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.</p> <p>The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).</p> <p>Items for Trust perception scale - HRI, Schaefer 2016:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:</p> <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do </li> <li>The speed at which the gripper picked up and released the components made me uneasy </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul> <p> </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>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>
Movement data set for trust assessment (Drapebot robot cell/DLR)
<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. To realize data-driven trust assessement, the worker is equipped with a motion tracking suit and the body movement data is labeled with the trust scores from two standard Trust questionnaire (1. Trust perception scale - HRI, Schaefer 2016; 2. Trust in industrial human robo collaboration, Charalambous, et.al. 2016).</p> <p>For this data set, data has been collected for the draping task from 21 participants all familiar with working with large industrial manipulators. For all sessions, body tracking was performed using the Xsens MVN Awinda tracking suit. It consists of a tight-fitting shirt, gloves, headband, and a series of straps used to attach 17 IMUs to the participant. After calibration the system uses inverse kinematics to track and log the movements of the participant at a rate of 60 Hz. The measurements include linear and angular speed, velocity, and acceleration of every skeleton tracking point (see <a href="https://www.xsens.com/hubfs/Downloads/Manuals/MVN_real-time_network_streaming_protocol_specification.pdf">XSENS manual</a> for a detailed description of avaiable measurements).</p> <p><strong>Data organization</strong></p> <p>There are 21 files for 21 participants. The name of the files is PID01, where the number 01 is the participant. Each file contains all the data that was generated from the XSENS motion capture system. The files are xlsx files and for each sheet inside the excel file there are different types of data:</p> <ul> <li>Segment Orientation - Quat</li> <li>Segment Orientation - Euler</li> <li>Segment Position</li> <li>Segment Velocity</li> <li>Segment Acceleration</li> <li>Segment Angular Velocity</li> <li>Segment Angular Acceleration</li> <li>Joint Angles ZXY</li> <li>Joint Angles XZY</li> <li>Ergonomic Joint Angles ZXY</li> <li>Ergonomic Joint Angles XZY</li> <li>Center of Mass</li> <li>Sensor Free Acceleration</li> <li>Sensor Magnetic Field</li> <li>Sensor Orientation - Quat</li> <li>Sensor Orientation - Euler</li> </ul> <p>See also: <a href="https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US">https://base.movella.com/s/article/Output-Parameters-in-MVN-1611927767477?language=en_US</a></p> <p>For more information on each specific data and/or sensors please see the xsens manual (Link above)</p> <p><strong>Data Annotation</strong></p> <p>In each .xlsx file the first tab (sheet) is called "Markers". It annotates the starting frame of the individual tasks. The annotations are pickup, draping, return and some files may contain a also a "fail" annotation. Failed attempts should not be taken into consideration for model training.</p> <p>The file trustscores.xlsx includes the results of the trust questionaires for each participant (scores for the individual items as well as the calculated overall trust scores).</p> <p>Items for Trust perception scale - HRI, Schaefer 2016:</p> <ul> <li>Which % of time does the robot <ul> <li>Function successfully</li> <li>Act consistently</li> <li>Communicate with people</li> <li>Provide feedback</li> <li>Malfunction</li> <li>Follow directions</li> <li>Meet the needs of the mission</li> <li>Perform exactly as instructed</li> <li>Have errors</li> </ul> </li> <li>Which % of the time is the robot: <ul> <li>Unresponsive</li> <li>Dependable</li> <li>Reliable</li> <li>Predictable</li> </ul> </li> </ul> <p>Items for Trust in industrial human robo collaboration, Charalambous, et.al. 2016:</p> <ul> <li>The way the robot moved made me uncomfortable</li> <li>I felt I could rely on the robot to do what it was supposed to do </li> <li>The speed at which the gripper picked up and released the components made me uneasy </li> <li>I felt safe interacting with the robot</li> <li>I knew the gripper would not drop the components</li> <li>The size of the robot did not intimidate me</li> <li>The robot gripper did not look reliable</li> <li>I was comfortable the robot would not hurt me</li> <li>I trusted that the robot was safe to cooperate with</li> <li>The gripper seemed like it could be trusted</li> </ul> <p> </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>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>
Dataset of "Social Robots and Sensors for Enhanced Ageing at Home: A Focus on Mobility and Socioeconomic Factors."
<p>This dataset supports the article:</p> <p>"Social Robots and Sensors for Enhanced Aging at Home: A Focus on Mobility and Socioeconomic Factors."</p> <p>For further details see the Readme.txt file.</p>
Usability data (Drapebot Robot Cell/Profactor)
<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 the Profactor work cell the draping task was simulated with reusable cut pieces. The participant would occupy a safe zone outside the reach of the robot, when the robot was in motion. One task repetition consisted of the robot retrieving a large and narrow cut piece from a table and placing it on the mould, holding it at the seeding point. The participant would then approach the robot and perform the draping motions after which they retreated to the safe area of the HRC cell and signaled to proceed to the next repetition.</p> <p>At this test site each participant performed the task repeatably in two sessions where we compared two methods of signaling the robot. In the first session they repeated the task ten times, communicating to the robot to retrieve the next cut piece by stepping on a pedal in the safe area of the work cell (NoNUI condition). In the second session the participants performed five task repetitions, signaling the robot using a gesture input by reaching up towards the robot (Gesture condition).</p> <p>The usability questionnaires, SUS and UMUX, as well as trust questionnaires were administered after each session, after both gesture and non-NUI conditions. The NASA TLX and UTAUT were only administered once after both sessions were concluded.</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 (NoNUI, 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 (NoNUI, 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 (NoNUI, 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 (NoNUI, 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> <p> </p>
Usability data (Drapebot Robot Cell/DLR)
<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 the DLR work cell one task repetition consisted of the robot retrieving a 30x30 cm cut piece from a table and placing it on the mould, holding it at the seeding point. The participant would then approach the robot from the safe zone and drape the cut piece on the mould, deforming it. When the participant had finished the draping, they retreated to the safe zone and signaled to proceed to the next repetition, and the robot retrieved the next cut piece. With 10 repetitions each piece was positioned and draped at different positions along the mould, starting at one end and evenly spread along the length of the mould.</p> <p>In the first session participants did the draping task ten times and signaled the robot by pressing a button mounted to their hip (NoNUI condition). In the second and third session they signaled the robot five times using one of two NUI in counter-balanced order (voice condition and gesture condition).</p> <p>The usability questionnaires, SUS and UMUX were administered after each session, the trust questionnaires only after the NoNUI condition. The NASA TLX and UTAUT were only administered once after both sessions were concluded.</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 (NoNUI, Voice, 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 (NoNUI, , Voice, 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 (NoNUI)</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 (NoNUI)</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>
Dataset: Global X Robotics & Artificial Intelligence ETF (BOTZ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Themes Robotics & Automation ETF (BOTT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Arbe Robotics Ltd. (ARBE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Arbe Robotics Ltd. (ARBEW) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Arbe Robotics Ltd. (ARBE) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Serve Robotics Inc. (SERV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.