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39 results for “Robot Navigation”
A dataset for robotic outdoor visual navigation with multiple passages through trajectory segments
<p>The images were captured by a fisheye camera and a magnetic compass was used to acquire the orientation data. The datasets are split in two folders:<br> 1) LEARN: In order to learn a new place, the robot camera captures 15 images over a 360 degrees panorama. During this process, the robot stays still in order to avoid distortions in the representation of the place.<br> 2) EXPLO: When exploring the environment (i.e. the rest of the time), the robot only captures 7 images per panorama, for the purpose of faster place recognition. Images are captured while the robot is moving. Various exploration panoramas are recorded around the trajectory performed in the learning panoramas (see traj.pdf).<br> <br> The average distance between two learning panoramas is 0.93 +/- 0.03 meters<br> The average distance traveled during an exploration panoramas is 0.71 +/- 0.01 meters<br> <br> DATASET A<br> ---------<br> - 20 meters long<br> - 22 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 5 exploration trajectories<br> - A_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - A_parallel: 29 exploration panoramas<br> - A_diagonal1: 28 exploration panoramas<br> - A_diagonal2: 30 exploration panoramas<br> - A_diagonal3: 29 exploration panoramas<br> <br> DATASET B<br> ---------<br> - 20 meters long<br> - 21 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 4 exploration trajectories<br> - B_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - B_parallel: 29 exploration panoramas<br> - B_diagonal1: 29 exploration panoramas<br> - B_diagonal2: 29 exploration panoramas<br> <br> DATASET C<br> ---------<br> - 23.1 meters long<br> - 25 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 2 exploration trajectories<br> - C_on_learned: 34 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - C_parallel: 34 exploration panoramas<br> <br> <br> <br> PANO_INFO FILE STRUCTURE<br> ------------------------<br> Every folder containing images also contains an info file, named either learn_pano_info.SAVE or explo_pano_info.SAVE. Each line corresponds to an image. The structures is the following:<br> - column 1: id = image_id + 1<br> - column 2: azimuth of the center of the image in degrees/360 (value in [0,1])<br> - column 3: elevation of the center of the image. irrelevant in this database (equal to 0).<br> - column 4: type of panorama: equal to 1 if learning and to 0 if exploration.<br> - column 5: end of panorama: equal to 1 if it corresponds to the last image of a panorama.<br> <br> <br> REFERENCES<br> ----------<br> The dataset was used in the paper: Belkaid, M., Cuperlier, N., and Gaussier, P. Combining local and global visual information in context-based neurorobotic navigation. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 4947-4954, doi: 10.1109/IJCNN.2016.7727851, 2016.<br> <br> </p>
Robot navigation measurements driving in and out of the van
<p>This dataset is a rosbag which contains the laser scans and the pose of a Husky mobile robot, using the robot's odometry and two SICK S300 laser scans to navigate.</p> <p><strong>List of available topics:</strong></p> <ol> <li>/colrobot/hardware/scan_pointcloud</li> <li>/colrobot/navigation/perfect_match/debug/LaserScanfiltered</li> <li>/colrobot/navigation/perfect_match/LocalisationPoseResult</li> <li>/colrobot/navigation/map</li> <li>/tf</li> <li>/tf_static</li> </ol> <p><strong>Topics contents:</strong></p> <ol> <li>Pointcloud containing the merge of the two laser scans </li> <li>Pointcloud containing the laser scans which matched with the given map</li> <li>Robot's pose estimate</li> <li>Map of the van (robot's working area)</li> <li>6. Transforms of the system</li> </ol>
A comparison between mouse, in silico, and robot odor plume navigation reveals advantages of mouse odor-tracking
<p>Localization of odors is essential to animal survival, and thus animals are adept at odor-navigation. In natural conditions animals encounter odor sources in which odor is carried by air flow varying in complexity. We sought to identify potential minimalist strategies that can effectively be used for odor-based navigation and asses their performance in an increasingly chaotic environment. To do so, we compared mouse, <i>in silico</i> model, and Arduino-based robot odor-localization behavior in a standardized odor landscape. Mouse performance remains robust in the presence of increased complexity, showing a shift in strategy towards faster movement with increased environmental complexity. Implementing simple binaral and temporal models of tropotaxis and klinotaxis, an <i>in silico</i> model and Arduino robot, in the same environment as the mice, are equally successful in locating the odor source within a plume of low complexity. However, performance of these algorithms significantly drops when the chaotic nature of the plume is increased. Additionally, both algorithm-driven systems show more successful performance when using a strictly binaral model at a larger sensor separation distance and more successful performance when using a temporal and binaral model when using a smaller sensor separation distance. This suggests that with an increasingly chaotic odor environment, mice rely on complex strategies that allow for robust odor localization that cannot be resolved by minimal algorithms that display robust performance at low levels of complexity. Thus, highlighting that an animal's ability to modulate behavior with environmental complexity is beneficial for odor localization.</p>
IROS 22 - A Hybrid Primitive-Based Navigation Planner for the Wheeled-Legged Robot CENTAURO (Extended)
<p>IROS 22 - A Hybrid Primitive-Based Navigation Planner for the Wheeled-Legged Robot CENTAURO - Extended Version</p> <p>A. De Luca, L. Muratore and N. Tsagarakis. Italian institute of Technology, IIT.</p> <p> </p> <p>Hybrid Primitive Based planner executed on the real CENTAURO robot. The planner searches for a plan to reach the goal assigned with the available primitives. In this experiment, the primitives are: whole robot driving, and single wheel action. The latter can be divided into single-wheel driving and stepping, based on the elevation difference during the trajectory of the wheel. In addition, single-wheel driving can be merged to obtain the Macro "Reshape", speeding up the execution.</p>
IROS 22 - A Hybrid Primitive-Based Navigation Planner for the Wheeled-Legged Robot CENTAURO
<p>IROS 22 - A Hybrid Primitive-Based Navigation Planner for the Wheeled-Legged Robot CENTAURO</p> <p>A. De Luca, L. Muratore and N. Tsagarakis. Italian institute of Technology, IIT.</p> <p> </p> <p>Hybrid Primitive Based planner executed on the real CENTAURO robot. The planner searches for a plan to reach the goal assigned with the available primitives. In this experiment, the primitives are: whole robot driving, and single wheel action. The latter can be divided into single-wheel driving and stepping, based on the elevation difference during the trajectory of the wheel. In addition, single-wheel driving can be merged to obtain the Macro "Reshape", speeding up the execution.</p>
Bilateral Human-Robot Control for Semi-Autonomous UAV Navigation
<p><strong>This video demonstrates the work towards a novel control architecture for UAV navigation. In general, UAVs are not easy to operate and skilled pilots are required for a good performance in manual flight. However, currently it is impossible to capture every possible situation an UAV could encounter in the autonomous control. To avoid overly complicated control, a semi-autonomous control approach can be used, so the drone is partly autonomously and partly manually piloted. The novelty of the approach presented here is in the way this semi-autonomy is defined. </strong></p> <p><strong>As the UAV regularly operates autonomously, it is not desirable to switch to manual control in dangerous procedures. Instead, a more supervisory method of control can be applied in which the UAV is always controlled by the onboard computer, but the boundaries of control are controlled by the operator. Whenever a situation requires bigger risks, the operator is informed requested by the drone for help, which he\she can offer by softening certain boundaries of the UAV.</strong></p> <p><strong>This video demonstrates the concept.</strong></p>
Surface Type Classification for Autonomous Robot Indoor Navigation - Dataset
<p>Surface Type Recognition with Inertial Measurement Unit (IMU).</p> <p>The dataset contains time series samples with 10 features each, related to orientation, velocity and acceleration. Each time series (of lenght 128) includes its corresponding surface type annotation.</p> <p>The data has been also divided in groups for easier cross-validation (80 groups present)</p> <p>A total of 9 different surface types are present in the dataset.</p> <p>"X_data.npy" contains the time series samples of dimension 7626x10x128<br> "label.npy" contains the label information for each sample (dimension 7626x1)<br> "groups.npy" contains the group information for each sample (dimension 7626x1)<br> "details.csv" contains for each sample the group information and the corresponding label</p> <p> </p>
A comparison between mouse, in silico, and robot odor plume navigation reveals advantages of mouse odor-tracking
Open the record for dataset details and reuse information.
CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification
<p>Video of submitted paper entitled "CBF-Based Motion Planning for Socially Responsible Robot Navigation Guaranteeing STL Specification"</p>
Data for "What it Takes to Get There: Spatial Cognition and Autonomous Indoor Robot Navigation"
Open the record for dataset details and reuse information.
European Robotics League Consumer - Lisbon 2019 contest - FBM 2 - Navigation functionality
<p>Benchmark logs for the FBM2 benchmark: navigation functionality.</p> <p>ERL Consumer competition in Lisbon.</p>
Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise
<p>Video of submitted paper entitled "Robust CBF-based STL motion planning for socially responsible robot navigation in the presence of measurement noise"</p>
Supplementary Video for Hierarchical Vision Navigation System for Quadruped Robots with Foothold Adaptation Learning
<p>This file contains video of the real world experiments presented in the paper "Hierarchical Vision Navigation System for Quadruped Robots with Foothold Adaptation Learning". </p> <p>It also provides a high-level overview of the motivation and the theory developed in the paper.<br> </p>
Comparative Performance of Robotic, Electromagnetic Navigation and Fluoroscopy Drives Optimal Diagnostic Strategy for Peripheral Pulmonary Lesions
ClinicalTrials.gov study NCT06701448. IPD Sharing: NO. Countries: 1. Publications: 1.
nCLE For Diagnosis Of Peripheral Lung Nodules By Robotic Navigational Bronchoscopy
ClinicalTrials.gov study NCT04441749. IPD Sharing: Not stated. Countries: 2. Publications: 1.
Robotized Navigation Compared to Conventional Technique in Total Knee Replacement
ClinicalTrials.gov study NCT04525950. IPD Sharing: NO. Countries: 1. Publications: 18.
Assessment of Tool Placement in Pulmonary Nodule(s) Using a Robotic Navigational Bronchoscopy System
ClinicalTrials.gov study NCT05867953. IPD Sharing: NO. Countries: 1. Publications: 1.
Clinal Evaluation of Navigation Based Functional Ear Surgery Using Image Guided and Robotically Assisted Techniques
ClinicalTrials.gov study NCT03746613. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Catheter Ablation of Atrial Fibrillation Using Hansen Medical Robotic Navigation
ClinicalTrials.gov study NCT01037296. IPD Sharing: Not stated. Countries: 1. Publications: 2.
PSMA-PET/MRI-Ultrasound Multimodal Fusion Navigation for Da Vinci Robot-Assisted Radical Prostatectomy: A Randomized Controlled Trial
ClinicalTrials.gov study NCT07272317. IPD Sharing: NO. Countries: 1. Publications: 4.
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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)
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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.