Robotics
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.
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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.
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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.
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Visual perception can be used by robotic leg prostheses and exoskeletons to improve the accuracy and speed of transitions between different locomotion mode controllers (e.g., level-ground walking to stair ascent) by sensing the walking environment prior to physical interactions. Here we developed the StairNet dataset to support the development of vision-based stair recognition systems.
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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.
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Synergistic prostheses enable the coordinated movement of the human-prosthetic arm, as required by activities of daily living. This is achieved by coupling the motion of the prosthesis to the human command, such as residual limb movement in motion-based interfaces. Previous studies demonstrated that developing human-prosthetic synergies in joint-space must consider individual motor behaviour and the intended task to be performed, requiring personalisation and task calibration.
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