Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
47
datasets available to search
ShareScore release 0.9.0
Dataset results
47 results for “mobile robots”
Mobile Service Robots Crash Testing with Pedestrians: Safety Assessment with Child and Adult Dummies
<p>Data published with the manuscript: “<em>Estimating risks posed by personal mobility devices and service robots to pedestrians: comparative crash testing of adult versus child dummies</em>”. 2021 (Paez-Granados & Billard, 2021)<br> <strong>Summary:</strong></p> <p>This dataset contains injury measures during collisions between a mobile service robot - Qolo - (Paez-Granados, et al, 2018) and pedestrian dummies: male adult Hybrid-III (H3) and child model 3-years-old (Q3). We present multiple collision scenarios for the assessment of pedestrian safety, considering possible impacts at the legs for adult pedestrians, and legs, chest and head for children. In these tests, we followed known methods of safety analysis used in car crash testing and used a standing wheelchair robot "Qolo" as a representative system of mobile service robots, such as delivery bots (robot without occupant), person carrier robots, autonomous wheelchairs, standing mobility vehicles, and other transport robots expected to operate in pedestrian and public areas.</p> <p>The robot was equipped with an experimental front structure allowing different bumper heights and measurement of reaction forces. On the other hand, the human dummies were equipped with standard instrumentation calibrated in accordance with SAE J211-1 for impact tests, thus, the child dummy, Q3 provided head accelerations, neck forces and moments, chest deflections, and accelerations; and pelvis accelerations. The dummy H3 provided forces and moments at the tibia and femur, and accelerations at the pelvis, chest, and head. You will find scripts to read and plot the data, as well as, analysis of the injury risk based on standard crash testing metrics: Head Injury Criteria (HIC-15), head acceleration (a_3ms), Neck Injury (Nij), Chest deflection (CD), and tibia injury (TI).</p> <p><strong>Instructions: </strong></p> <p><em>This dataset contains the following main files:</em></p> <ol> <li><strong><em>Data Description.pdf</em>: </strong>Highly recommended to read through this file for understanding the setup of the collected dataset, as well as, the submitted manuscript.</li> <li><em><strong>collision_test_rawdata.zip</strong>: </em>This file contains all the raw data for each sensor as mentioned in table 3, organized in independent subfolders as described in table 2.<em> ‘test_name’/01_values/’testName’_CFC1000.xlsx</em></li> <li><em><strong>collision_test_analysis.zip</strong>: </em>This file contains all the processed data for each sensor in order to apply known injury metrics (Nij, HIC15, acc_3ms, TI, CC, VCI), organized in independent subfolders as described in table 2.<em>‘test_name’/01_values/’testName’_Analysis_v2.xlsx --> </em>Dataset with filtered sensor data accordingly to SAEJ21.</li> <li><em><strong>collision_data_matlab_structure.zip</strong>:</em><em> Matlab containers with all data - also available as .mat files for easy reading from Code Ocean capsule.</em></li> <li><em><em><strong>scripts-crash-test-service-robots.zip</strong>:</em> processing of the dataset is provided in this file with structure of data in Matlab containers and scripts for visualizing the data (see section III), further analysis scripts in the linked GitHub: <a href="https://github.com/epfl-lasa/crash-tests-service-robots">https://github.com/epfl-lasa/crash-tests-service-robots</a></em></li> </ol>
Context-Aware 3D Object Anchoring for Mobile Robots Dataset
<p>This dataset accompanies the following publication:</p> <p>Günther, M.; Ruiz-Sarmiento, J. R.; Galindo, C.; González-Jiménez, J. & Hertzberg, J. <strong>Context-Aware 3D Object Anchoring for Mobile Robots.</strong> <em>Robot. Auton. Syst.</em>, 2018 (accepted)</p> <p>The dataset consists of 15 scenes inspected by a robot equipped with a RGB-D camera driving around a table and turning towards it from different locations. The table contained a number of objects in varying table settings. In total, the dataset contains 1387 seconds of observation and 144 unique objects from 9 categories:</p> <ul> <li>SugarPot</li> <li>MilkPot</li> <li>CoffeeJug</li> <li>MobilePhone</li> <li>Mug</li> <li>Dish</li> <li>Fork</li> <li>Knife</li> <li>Spoon</li> <li>TableSign</li> </ul> <p>Segmentation, tracking and local object recognition was run on the recorded sensor data, and its output (tracked objects and local recognition results) was added to the dataset. Since the objects were observed from multiple perspectives and tracking was lost while the robot was moving from one observation pose to another, the dataset contains more than one track ID for most objects (one for each subsequent observation of the object). Each track ID was manually labeled with the ground truth category of the object it represented. Additionally, all track IDs belonging to the same object were manually grouped together to allow evaluation of the anchoring process. Track IDs that did not correspond to any object on the table (but instead to objects on different tables, pieces of the table itself or other artifacts) were manually removed. In total, out of 432 track IDs, 410 (94.9 %) were associated with true objects, while 22 (5.1 %) were removed as artifacts.</p> <p><br> <strong>File contents</strong></p> <p>All data is provided as rosbags. The naming scheme is as follows:</p> <ul> <li>`*-sensordata.bag.bz2`: The raw sensor data from the robot and all transform data, including localization in a map.</li> <li>`*-perception.bag.bz2`: The object recognition results and ground truth information for the tracked objects.</li> <li>`scene??-pr2-*.bag.bz2`: 5 scenes that were recorded using the PR2 robot.</li> <li>`scene??-calvin-*.bag.bz2`: 10 scenes that were recorded using the Calvin robot.</li> </ul> <p>Both robots used an ASUS Xtion Pro Live as 3D camera.</p> <p>`race_vision_msgs.tar.bz2`: The custom messages used in the `-perception` rosbags, as a ROS Kinetic package.</p> <p><br> <strong>Videos</strong></p> <p>To get a first impression of the dataset, `scene10.mp4` and `scene19.mp4` show the corresponding scenes from the point of view of the robot's RGB camera.</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>
RSSI-based mobile robot localization datasets
<p>Experimental datasets used for an MSc project, titled "Outdoor Localization System for Mobile Robots Based on Radio-Frequency Signal Strength", for performing trajectory recovery of a mobile ground robot by data fusion of odometry, gyroscope and Received Signal Strength Indicator (RSSI), through Extended and Augmented-Extended Kalman Filter algorithms.</p> <p><br> "experiment_1" contains the robot's data in "rosbags" (i.e. ROS' compressed robot data), recorded in a parking lot with ground-truth from an RTK GPS. The measurements recorded were wheel odometry, IMU accelerations/angular velocities, RSSI from three receiver-transmitter pairs, regular GPS position and RTK GPS position.</p> <p>"experiment_2" contains the robot's data in "rosbags", recorded in the parking lot and in a second environment, a garden with tall trees and a nearby building. Both environments were recorded for later comparison, to see how the localization solution performed in a GPS-denied environment. All measurements were the same as in experiment 1, except for the exclusion of the common and RTK GPS.</p>
Autonomous Mobile Robots: Past, Present and Future of SLAM 2013
<p>“Autonomous Mobile Robots: Past, Present and Future of SLAM” 2013. In:Workshop<br /> at the First RSI/ISM International Conference on Robotics and Mechatronics by<br /> Sharif University of Technology. Presenter: Prof. Hamid D. Taghirad, 2013.</p>
Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms
<h2>Causal HRSI Dataset: Human-Robot Spatial Interaction Dataset for Causal Analysis from Mobile Platforms</h2> <div>The dataset captures a Human-Robot Spatial Interaction (HRSI) scenario between a person and the TIAGo robot. It focuses specifically on human-goal and human-robot spatial interaction in an indoor environment, captured from the perspective of a 3D Velodyne VLP-16 LiDAR mounted on the TIAGo robot. It includes:</div> <ul> <li>rosbags containing: Velodyne LiDAR point clound, robot and human state (position, orientation and velocities);</li> <li>CSV files containing trajectories of the person and the robot generated by post-processing the rosbags;</li> <li>the map of the environment extracted from the TIAGo robot.</li> </ul> <p><strong>15 participants</strong> took part in the experiment, with the dataset capturing <strong>5 minutes of HRSI motion for each participant</strong>.</p> <h3>Experiment Description</h3> <p>The experiment and data collection occurred in a laboratory room of the University of Lincoln (UK), measuring 5 x 8.2m. <br>Fifteen participants (6 females, aged between 25 and 55) took part in the experiment. Seven of them were used to work with a robot. They were required to walk between four goal positions and avoid the robot if a cross occurs. A predefined rectangular path was set for the TIAGo robot to navigate along the room and generate frequent interactions with the participants.</p> <p>The experimental procedure can be described as follows. Each participant started from one of the four target positions. The next target position was randomly chosen by the participant, who then started moving towards it. Upon reaching the goal position, the participant stopped there and randomly chose the next goal, repeating the process for 5 minutes. In this experimental setting, the robot was considered by the participant as an obstacle to avoid while walking towards their target positions.</p> <h3>Directory Structure</h3> <p>Dataset<br>|<br>|____Map: folder containing the map of the environment extracted from the TIAGo robot<br>|<br>|____RosBags: forder containing the rosbag for each partipant<br>| |____A1.bag<br>| |____A2.bag<br>| |____A3.bag<br>| |____A4.bag<br>| |____A5.bag<br>| |____A6.bag<br>| |____A7.bag<br>| |____A8.bag<br>| |____A9.bag<br>| |____A10.bag<br>| |____A11.bag<br>| |____A12.bag<br>| |____A13.bag<br>| |____A14.bag<br>| |____A15.bag<br>|<br>|____Trajectories: postprocessed trajectories extracted for the rosbag files <br> |____A1_traj.csv<br> |____A2_traj.csv<br> |____A3_traj.csv<br> |____A4_traj.csv<br> |____A5_traj.csv<br> |____A6_traj.csv<br> |____A7_traj.csv<br> |____A8_traj.csv<br> |____A9_traj.csv<br> |____A10_traj.csv<br> |____A11_traj.csv<br> |____A12_traj.csv<br> |____A13_traj.csv<br> |____A14_traj.csv<br> |____A15_traj.csv</p>
3D Point Cloud Data for LiDAR-based Mobile Robot
<p>LiDAR point cloud data serves as an machine vision alternative other than image. Its advantages when compared to image and video includes depth estimation and distance measurement. Low-density LiDAR point cloud data can be used to achieve navigation, obstacle detection and obstacle avoidance for mobile robots. autonomous vehicle and drones. In this metadata, we scanned over 1400 objects and classified it into 6 groups of object namely, human, cars, motorcyclist, signboard, road divider and others.</p>
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>
Path Planning for Mobile Robot
<p>Implementation of Rapid exploring Random Tree Star (RRT*) Optimization based on Ellipsoid Equation. The simulation shows two different scenario of obstacles to test the implementation. The algorithm is running on MATLAB. The communication between Gazebo simulation and MATLAB is done through Robot Operating System (ROS) framework.</p>
Robotic Walking Device to Improve Mobility in Parkinson's Disease
ClinicalTrials.gov study NCT03751371. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Affordable Mobile Robots for the Elderly
ClinicalTrials.gov study NCT02807506. IPD Sharing: NO. Countries: 1. Publications: 1.
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
Open the record for dataset details and reuse information.
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>
Description of mobile robotics competitions
<p>This dataset contains relevant information about mobile robotics competitions that take place over the last few years, as well as the URLs to papers related to each competition. The dataset was obtained from a systematic mapping literature review.</p>
Self-Organised Braiding on Mobile Thymio Robots Experiment Videos
<p>Self-organised braiding without any global control was implemented on mobile Thymio robots. The videos of the final experiments are provided here. Each experiment type was recorded three times: braiding with four, six, and eight robots and removing two from six robots.</p>
The behaviour of commercial broilers in response to a mobile robot - Behavioural dataset
<p>The data files associated with the paper titled 'The behaviour of commercial broilers in response to a mobile robot'.</p> <p> </p>
A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots
<p>This dataset is a part of the supplementary materials to the 2017 RAL <a href="https://ieeexplore.ieee.org/document/7358076">article</a> with the same title.</p> <blockquote> <p>A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots</p> <p>IEEE Robotics and Automation Letters</p> <p>Alessandro Giusti, Jerome Guzzi, Dan Ciresan, Fang Lin He, Juan Pablo Rodriguez, Flavio Fontana, Matthias Faessler, Christian Forster, Jurgen Schmidhuber, Gianni A. Di Caro, Davide Scaramuzza, Luca Gambardella</p> </blockquote> <p>You can find more information on the <a href="http://bit.ly/perceivingtrails">project web page</a> (alessandrog@idsia.ch).</p> <p><strong>Dataset</strong></p> <p>Folders 001..010 contain the dataset used to train the networks. Folder 000 contains preliminary test data. Folders 011..014 contain data for testing the system.</p> <ul> <li>000 and 003 were shot with an handheld cellphone.</li> <li>001 and 002 were shot with 3 GOPRO Hero 3 cameras, fixed on the head with straps.</li> <li>004..014 were shot with 3 Bluefox cameras, fixed on a rigid helm (the same model and with the same lens as the camera mounted on the quadcopter).</li> </ul>
Can Guided Decomposition Help End-Users Write Larger Block-Based Programs? A Mobile Robot Experiment. (supplementary materials)
<p>Supplementary Material for the research paper "<strong>Can Guided Decomposition Help End-users Write Larger Block-based Programs? a Mobile Robot Experiment</strong>", published at the <em>Conference on Object-oriented Programming, Systems, Languages (OOPSLA 22).</em></p> <p>Paper DOI: <a href="http://doi.org/10.1145/3563296">http://doi.org/10.1145/3563296</a> </p> <p>This archive contains:</p> <ul> <li>An interactive demonstration of the used tutorials and tasks (in the folders tut1-3/ and task1-3/).</li> <li>An interactive playground to test out the presented development environments (onecanvas.html and twocanvas.html in the root folder).</li> <li>The raw data collected throughout the controlled experiment presented in the paper (mobilerobots-data.csv and mobilerobots-data.html)</li> <li>An overview page that allows convenient access to each of the previously described contents.</li> </ul> <p>A non-archival version of the contents is also available here: <a href="https://vcuse.github.io/alvo/first-experiment/">vcuse.github.io/alvo/first-experiment/</a></p> <p><em>Note: The interactive demonstrations work locally. However, several other files and folders contained in the archive are required to run the interactive demonstrations. Deleting or moving any parts of the archive may break the interactive elements. This demo was tested to work as intended on all commonly used web browsers in their most recent versions of 2022. Use at own risk</em></p>
Indoor Surface Classification for Mobile Robots
<p>In this project, we generated a dataset that contains three different types of indoor floor surfaces: carpet, tile and wood. Then, we used this dataset to train eight CNN-based models, including our proposed model, <em><strong>MobileNetV2-modified</strong></em>.</p> <ul> <li>The dataset comprises a total of 2081 samples, consisting of images captured with cameras in various indoor environments and lighting conditions.</li> <li>These images were taken from different angles in accordance with the overall dimensions of the indoor robots.</li> <li>This dataset includes samples collected from more than 20 different indoor environments.</li> <li>The dataset consists of 870 carpet samples, 638 tile samples and 573 wood surface samples.</li> </ul> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.