This dataset comprises three degrees of freedom (3 DOF) sensory data and simulation data collected from a Kinova robotic arm. The sensory data includes real-time measurements from the robotic arm’s joint positions, velocities, and torques, providing a detailed account of the arm’s dynamic behavior. The dataset also includes simulated data generated using a high-fidelity physics engine, accurately modeling the Kinova arm’s kinematics and dynamics under various operational scenarios.
This dataset contains both the artificial and real flower images of bramble flowers. The real images were taken with a realsense D435 camera inside the West Virginia University greenhouse. All the flowers are annotated in YOLO format with bounding box and class name. The trained weights after training also have been provided. They can be used with the python script provided to detect the bramble flowers. Also the classifier can classify whether the flowers center is visible or hidden which will be helpful in precision pollination projects.
This data port serves as a valuable extension to the article titled "Algorithmic Framework for Analyzing and Simulating Multi-axial Robotic Transformations in Spatial Coordinates." It provides Python script implementations of the simulation algorithm detailed in the paper. These scripts are designed to allow seamless adoption and experimentation with the proposed algorithm, enhancing its usability for researchers and practitioners alike.
Lettuce Farm SLAM Dataset (LFSD) is a VSLAM dataset based on RGB and depth images captured by VegeBot robot in a lettuce farm. The dataset consists of RGB and depth images, IMU, and RTK-GPS sensor data. Detection and tracking of lettuce plants on images are annotated with the standard Multiple Object Tracking (MOT) format. It aims to accelerate the development of algorithms for localization and mapping in the agricultural field, and crop detection and tracking.
In this investigation, the researchers have used a commercially available millimeter-wave (MMW) radar to collect data and assess the performance of deep learning algorithms in distinguishing different objects. The research looks at how varied ambiance factors, such as height, distance, and lighting, affect object recognition ability in both static and dynamic stages of the radar.
Dataset asscociated with a paper in 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems
"Talk the talk and walk the walk: Dialogue-driven navigation in unknown indoor environments"
If you use this code or data, please cite the above paper.
This is the original data and the fitted data from the paper Scalability in Computing and Robotics by Heiko Hamann and Andreagiovanni Reina. For more information please refer to the paper.
Given the difficulty to handle planetary data we provide downloadable files in PNG format from the missions Chang'E-3 and Chang'E-4. In addition to a set of scripts to do the conversion given a different PDS4 Dataset.
Data validation of the article "A framework for assessing, comparing and predicting the performance of autonomous RFID-based inventory robots for retail"
Dataset of rosbags collected during autonomous drone flight inside a warehouse of stockpiles. PCD files created using reconstruction method proposed by article.
Data still being move to IEEE-dataport.
