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5 results for “Agricultural Robots”
World Map of Agricultural Robot Manufacturer & services
<p>This Data Base is a World Map of all the manufacturers of agricultural robot for field. this data base was created by 3 student of the University of UniLaSalle Beauvais. You can fin the LINK of the GOOGLE EARTH "only reading" on the PDF document.</p>
Multimodal Agricultural Aerial and Ground Robotics Simulation Dataset
<p><strong>Dataset description</strong></p><p>This dataset was generated using an aerial robot and a ground robot in the Webots simulator with the <a href="https://github.com/opendr-eu/opendr/tree/master/projects/python/simulation">OpenDR agricultural dataset generator tool</a>.</p><p>It consists of 13980 RGB images and their semantic segmentation counterparts taken at different lighting conditions and robot positions in an agricultural field. It also includes the annotation data comprised of the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box. Furthermore, it includes gps and inertial unit sensor data for UAV and gps, inertial and lidar sensor data for UGV.</p><p><strong>Folder configuration</strong></p><p>The dataset contains 4 folders for different lighting conditions:</p><ul><li>noon cloudy</li><li>noon stormy</li><li>dawn cloudy</li><li>dusk</li></ul><p>Each contains UAV and UGV folders. UAV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>camera: contains generated RGB images.</li><li>gps: contains the three-axis location of global positioning sensor saved in TXT files.</li><li>inertial unit: contains the inertial unit date in TXT files.</li></ul><p>UGV folder includes:</p><ul><li>annotations: contains segmented images in JPG files and annotations in TXT files.</li><li>front_bottom_camera: contains generated RGB images.</li><li>Hemisphere_v500: contains the three-axis location of the global positioning sensor saved in TXT files.</li><li>imu_robotti: contains the inertial unit date in TXT files.</li><li>velodyne: contains lidar data in PCD files.</li></ul><p><strong>Data format</strong></p><p>The dataset includes</p><ul><li>The inertial measurement TXT files include Euler angles in order of Roll, Pitch, and Yaw.</li><li>The GPS measurement TXT files include the robot position in x, y, and z order.</li><li>Object annotation TXT files include the class of the object, x, and y of the top left pixel of the object bounding box, and the width and height of the object bounding box at each line for the corresponding frame.</li></ul><p><strong>File naming convention</strong></p><p>Each data is named "s_i{_segmented, _annotation}.ext", where:</p><ul><li><strong>s</strong> denotes the simulated time in seconds.</li><li><strong>i</strong> denotes the index counting every 10ms of simulated time.</li><li><strong>ext</strong> denotes the extension, "jpg" for images, "pcd" for lidar, and "txt" for the rest.</li><li>Labels <strong>_segmented</strong> and <strong>_annotation </strong>appended to the name for segmentation image and object annotations, respectively.</li></ul><p>Each segmented image uses the following RGB color mapping:</p><ul><li>Tree: 0.1, 0.4, 0.0</li><li>Apple Tree: 0.85, 0.49, 0.57</li><li>Cow: 0.380, 0.220, 0.137</li><li>Sheep: 0.937, 0.921, 0.862</li><li>Fox: 0.992, 0.376, 0.086</li><li>Barn: 0.625, 0.293, 0.226</li><li>Cat: 0.870, 0.580, 0.0</li><li>Deer: 0.415, 0.364, 0.302</li><li>Human: 1.0, 0.855, 0.672</li></ul>
Dataset for Sound-based Anomalies Detection in Agricultural Robotics Application
<p>This data set contains data related to a Mowing Intelligent Tool (MowIT).</p> <p>Two different microphones were used to collect the sound samples, recording the audio with just one single channel, with a sampling rate of 44100 Hz and 16 bits resolution.</p> <p>The data provided by an inertial measurement unit (IMU) was also recorded since that was already integrated into the MowIT.</p> <p>Two different data collections were performed in different open-air environments with grass to cut.</p> <p>In each collection, eight different sample sets were made, five with the machine cutting using a trimmer line and the other three using the blades. Various combinations were used in each set, and tools were or were not placed on each of the three cutting axes of the MowIT. For each group, the acquisitions were designated from 0 to 7.</p> <p>Each folder of the first collection is a combination containing two audio files, one for each microphone used, the IMU data and a photograph of the lower part of the MowIT to understand the configuration used.</p> <p>In the second collection, to improve the variety of data, three distinct sub-sets were performed for combination: the first with the MowIT turned on but not cutting grass and the next two cutting grass. </p> <p>In samples 4 and 7, there is one audio where the MowIT cuts but stops due to motor stress. In sample 6, the initial recording was not made without cutting grass, and only the two recordings were made cutting grass.</p> <p> </p> <p> </p> <p> </p>
FollowMe - A Pedestrian Following Algorithm for Agricultural Logistic Robots (video results)
<p>Video result of submitted paper "FollowMe - A Pedestrian Following Algorithm for Agricultural Logistic Robots" in ICARSC confrence</p>
smallSSD: The Small Robot Company's detection dataset for agriculture
<p>The Small Robot Company's detection dataset for agriculture, consisting of images collected by the Tom robot labelled with bounding boxes to delineate wheat and weeds.</p> <p>This data is shared under a <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons Attribution-NonCommercial-4.0 International</a> license.</p> <p>More information about this data is available at <a href="https://github.com/SmallRobotCompany/smallssd">https://github.com/SmallRobotCompany/smallssd</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
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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.