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153 results for “robot control”
A Randomized Controlled Trial of Robotic Support for Enhanced Later Life (RoSELL)
ClinicalTrials.gov study NCT07102017. IPD Sharing: NO. Countries: 1. Publications: 0.
Impact of Robotic Glove Use on Quality of Life, Grip Strength and Fine Motor Control in ALS
ClinicalTrials.gov study NCT07298486. IPD Sharing: YES. Countries: 1. Publications: 4.
Data from: Aerobatic maneuvers in insect-scale flapping-wing aerial robots via deep-learned robust tube model predictive control
Open the record for dataset details and reuse information.
Desktop fabrication of monolithic soft robotic devices with embedded fluidic control circuits
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Data from: Noninvasive electroencephalogram based control of a robotic arm for reach and grasp tasks
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Software for control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms
<p>This software implements the autonomous control of a robot by using a fuzzy logic controller tuned by a genetic algorithm. The software was written in C programming language for Windows (SDK). A description of the software can be found in the research publication "Arsene, C.T.C., & Zalzala, A.M.S., "Control of autonomous robots using fuzzy logic controllers tuned by genetic algorithms", In Proc Congress on Evolutionary Computation, Vol. 1, pp. 428-35, Washington DC, 1999, IEEE Computer Science Press, ISBN 0-7803-5536-9". Possibly the software to be used also for simulation of Nano-robots.</p>
AgRob Robot's Control Systems
<p><a href="https://zenodo.org/api/records/10093413/draft/files/Weta_Robot_Mission.mp4/content">Weta_Robot_Mission</a> - Demonstration of an agricultural robot (WETA) in an autonomous spraying mission following a trajectory with <a href="https://gitlab.inesctec.pt/agrob/model_e/parametric_trajectories_stack/-/tree/foxy_devel?ref_type=heads">Parametric Trajectory controller </a>at Quinta do Seixo, Portugal. </p><p><br><a href="https://zenodo.org/uploads/10103970">FollowMe_Demo </a>- Demonstration of <a href="https://gitlab.inesctec.pt/agrob/follow_me/virtual_leash_stack/-/tree/fixed_trajectories?ref_type=heads">Follow ME</a> system at Quinta do Seixo, Portugal. </p>
Data set from: Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial.
<p>Data set from the paper "Robot-Assisted Gait Training in Patients with Multiple Sclerosis: A Randomized Controlled Crossover Trial" doi: <a href="https://dx.doi.org/10.3390%2Fmedicina57070713">10.3390/medicina57070713</a></p>
Dataset associated to the "ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots" paper (manuscript DOI: 10.1109/LRA.2022.3141658)
<pre><code class="language-markdown">This dataset contains data accompanying the work: @ARTICLE{9676410, author={Viceconte, Paolo Maria and Camoriano, Raffaello and Romualdi, Giulio and Ferigo, Diego and Dafarra, Stefano and Traversaro, Silvio and Oriolo, Giuseppe and Rosasco, Lorenzo and Pucci, Daniele}, journal={IEEE Robotics and Automation Letters}, title={ADHERENT: Learning Human-like Trajectory Generators for Whole-body Control of Humanoid Robots}, year={2022}, volume={7}, number={2}, pages={2779-2886}, doi={10.1109/LRA.2022.3141658}} The dataset is organized in folders, whose content can be summarized as follows: - mocap: motion capture data collected from human motion - retargeted_mocap: motion capture data retargeted on the robot - IO_features: input and output features extracted from the retargeted mocap data to train the trajectory generator - training_D2_D3_subsampled_mirrored_4ew_98%: training data - inference: data collected while generating trajectories - trajectory_control_simulation: data collected while controlling trajectories in simulation - trajectory_control_real_robot: data collected while controlling trajectories on the real robot - additional_figures: additional data to reproduce some figures in the paper and portions of the supplementary video A more detailed description of the content of each folder is provided in the README.txt file included in the dataset.</code></pre>
Magnetically actuated gearbox for the wireless control of millimeter-scale robots
<p>The limited force or torque outputs of miniature magnetic actuators constrain the locomotion performances and functionalities of magnetic millimeter-scale robots. Here, we present a magnetically actuated gearbox with a maximum size of 3 millimeters for driving wireless millirobots. The gearbox is assembled using microgears that have reference diameters down to 270 micrometers and are made of aluminum-filled epoxy resins through casting. With a magnetic disk attached to the input shaft, the gearbox can be driven by a rotating external magnetic field, which is not more than 6.8 millitesla, to produce torque of up to 0.182 millinewton meters at 40 hertz. The corresponding torque and power densities are 12.15 micronewton meters per cubic millimeter and 8.93 microwatt per cubic millimeter, respectively. The transmission efficiency of the gearbox in the air is between 25.1 and 29.2% at actuation frequencies ranging from 1 to 40 hertz, and it lowers when the gearbox is actuated in viscous liquids. This miniature gearbox can be accessed wirelessly and integrated with various functional modules to repeatedly generate large actuation forces, strains, and speeds; store energy in elastic components; and lock up mechanical linkages. These characteristics enable us to achieve a peristaltic robot that can crawl on a flat substrate or inside a tube, a jumping robot with a tunable jumping height, a clamping robot that can sample solid objects by grasping, a needle-puncture robot that can take samples from the inside of the target, and a syringe robot that can collect or release liquids.</p>
Supplemental material: Comparing approaches for evolving high-level robot control based on behaviour repertoires
<p>Supplemental material for the paper:</p> <p>Comparing approaches for evolving high-level robot control based on behaviour repertoires<br> Jorge Gomes, Anders Lyhne Christensen</p> <p>Videos of the highest-performing solutions evolved with each method. In all videos, the schematic of the robot indicates the current wheel angles and robot speed. In the repertoire-based methods, the visualization on the bottom left indicates which primitives are being executed.</p> <p><strong>vid_evorbc_2_4_f1.12.mp4 </strong><br> Evolved by NEAT-EvoRBC. The blue cross indicates the behaviour selection vector.</p> <p><strong>vid_gp_2_2_f1.12_best.mp4</strong><br> Evolved by GP-DT. The primitives highlighted in green are the ones that exist in the decision tree.</p> <p><strong>vid_gp_8_4_f0.92_paper.mp4 </strong><br> Evolved by GP-DT. Smallest tree with fitness above 0.9. Shown in the paper.</p> <p><strong>vid_neatsub_9_5_f0.89.mp4 </strong><br> Evolved by NEAT-Subset. Only the primitives that are in the subset are shown. The numbers near each primitive indicate the activation level of the corresponding output (or maximum activation level if there are multiple outputs corresponding to the same primitive).</p> <p><strong>vid_neattr_24_f0.28.mp4 </strong><br> Evolved by NEAT-TR.</p> <p>The videos have been verified to reproduce correctly with the <strong>VLC player</strong>. But any other modern player should handle them.</p>
Data from: Noninvasive neuroimaging enhances continuous neural tracking for robotic device control
Brain-computer interfaces (BCIs) using signals acquired with intracortical implants have achieved successful high-dimensional robotic device control useful for completing daily tasks. However, the substantial amount of medical and surgical expertise required to correctly implant and operate these systems greatly limits their use beyond a few clinical cases. A noninvasive counterpart requiring less intervention that can provide high-quality control would profoundly improve the integration of BCIs into the clinical and home setting. Here, we present and validate a noninvasive framework using electroencephalography (EEG) to achieve the neural control of a robotic device for continuous random target tracking. This framework addresses and improves upon both the "brain" and "computer" components by increasing, respectively, user engagement through a continuous pursuit task and associated training paradigm and the spatial resolution of noninvasive neural data through EEG source imaging. In all, our unique framework enhanced BCI learning by nearly 60% for traditional center-out tasks and by more than 500% in the more realistic continuous pursuit task. We further demonstrated an additional enhancement in BCI control of almost 10% by using online noninvasive neuroimaging. Last, this framework was deployed in a physical task, demonstrating a near-seamless transition from the control of an unconstrained virtual cursor to the real-time control of a robotic arm. Such combined advances in the quality of neural decoding and the practical utility of noninvasive robotic arm control will have major implications for the eventual development and implementation of neurorobotics by means of noninvasive BCI.
Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control
<p>If you use any of this data, please cite the following publication:</p> <p><span>@inproceedings{monforte2023fast,</span><br><span> title={Fast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control},</span><br><span> author={Monforte, Marco and Gava, Luna and Iacono, Massimiliano and Glover, Arren and Bartolozzi, Chiara},</span><br><span> booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},</span><br><span> pages={4035--4043},</span><br><span> year={2023}</span><br><span>}</span></p> <p>Event-based datasets of synthetic and real trajectories of a bouncing ball.</p> <p>The synthetic trajectories were obtained converting frames taken using Unreal Engine to events. The ground truth is provided along with objects and camera settings.</p> <p>The real trajectories wer dumped from a real event camera located in front of the robot workspace.</p> <p>To import .log files containing events, we suggest <a href="https://github.com/event-driven-robotics/bimvee">bimvee</a> Python library.</p> <p>Specifically use the functions to import .log files:</p> <p>data = importIitYarpBinaryDataLog(filePathOrName=input_path)<br> </p> <p> </p> <p> </p>
Dataset - On the performance of online adaptation of robots controlled by nanowire networks
<p>Dataset of the experiments considered in the article in the title</p>
Brainwave Control of a Wearable Robotic Arm for Rehabilitation and Neurophysiological Study in Cervical Spine Injury
ClinicalTrials.gov study NCT02443558. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
A Randomized Controlled Trial of a Robotic Simulation Curriculum to Teach Robotic Suturing
ClinicalTrials.gov study NCT01811095. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Temporary Artery Clipping for Robotically-assisted Myomectomy, a Multicentric Randomized Controlled Trial
ClinicalTrials.gov study NCT06269809. IPD Sharing: NO. Countries: 3. Publications: 12.
A Multicenter, RAndomIzed, coNtrolled, umBrella Trial fOr Minimally Invasive Neurosurgery With AI-assisted Robotic guidanCe for Hemorrhagic Stroke
ClinicalTrials.gov study NCT06459427. IPD Sharing: NO. Countries: 1. Publications: 1.
Robotic Cholecystectomy Using the Chinese Micro Hand S Robot: A Controlled, Randomized, Prospective Clinical Study
ClinicalTrials.gov study NCT04803487. IPD Sharing: YES. Countries: 1. Publications: 20.
Ergonomics of Laparoscopy: Robotic Camera Controlled Versus Human Camera Controlled
ClinicalTrials.gov study NCT03339960. IPD Sharing: NO. Countries: 1. Publications: 1.
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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.