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6 results for “Aerial manipulation”
A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants
<p>This folder contain the data relative to the contact based pipe inspection presented on M. Tognon et al. "A Truly-Redundant Aerial Manipulator System With Application to Push-and-Slide Inspection in Industrial Plants." IEEE Robotics and Automation Letters 4.2 (2019): 1846-1851.</p>
Cooperative aerial tele-manipulation with haptic feedback
<p>Data-set regarding the work "Cooperative aerial tele-manipulation with haptic feedback".</p> <p>Citation bibtex:</p> <p>@inproceedings{mohammadi2016cooperative,<br> title={Cooperative aerial tele-manipulation with haptic feedback},<br> author={Mohammadi, Mostafa and Franchi, Antonio and Barcelli, Davide and Prattichizzo, Domenico},<br> booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},<br> pages={5092--5098},<br> year={2016},<br> organization={IEEE}<br> }<br> </p>
Heterogeneous Collaborative Aerial Manipulation
<p>The video shows the final experiments for heterogeneous collaborative aerial manipulation using two hexacopters with different manipulation capabilities and system properties.</p> <p>An exponentially stable controller is used on each vehicle such that the system consistently converges to the required setpoints, both on a free and locked manipulator scenarios. </p> <p>Experiments show that the controller can effectively transport a load using two different agents, with two different manipulation capabilities, to perform the task and still be robust to external unmodeled disturbances.</p>
Compliant Aerial Manipulators: Developing the New Generation of Aerial Robotic Workers
<p><strong>This video demonstrates the results found in the research of a new topic in aerial manipulation. We attempt to successfully collide on the environment with the UAV without crashing. This can be useful to robustly establish contact with the environment in realistic outdoor scenarios, where precise knowledge on the position of the drone might not always available.</strong></p> <p> </p> <p><strong>In the video three different experiments are shown. In all of these experiments, the manipulator arm (in this case a rotating rod) is in front of the drones center of mass.</strong></p> <p><strong>The first experiment shows the collision of the drone with the environment when the manipulator is rigidly connected to the drone. This causes a severe impact, which destabilizes the drone. It is simply too much energy for the drone to handle. In the second experiment the manipulator arm is connected to the drone via a spring-damper system to reduce the severeness of the impact. This shows significant improvement, but the drone is unable to maintain contact and bounces. The key to success in this work was to add a mechanical one-direction stop on the manipulator. This stop allows the arm to be pressed in during impact, but prevents the arm from releasing the energy afterwards. The third experiment shows how this works and demonstrates a beautiful smooth impact to achieve contact.</strong></p>
Agile and cooperative aerial manipulation of a cable-suspended load
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Real-time Dense Surface Reconstruction for Aerial Manipulation
<p>This video illustrates the content of the paper referenced below.</p> <p><strong> Reference:</strong></p> <p>Marco Karrer, Mina Kamel, Roland Siegwart and Margarita Chli, "Real-time Dense Surface Reconstruction for Aerial Manipulation", in Proceedings of the IEEE/RSJ Conference on Intelligent Robots and Systems (IROS), 2016.</p> <p><strong>Abstract:</strong></p> <p>With robotic systems reaching considerable maturity in basic self-localization and environment mapping, new research avenues open up pushing for interaction of a robot with its surroundings for added autonomy. However, the transition from traditionally sparse feature-based maps to dense and accurate scene-estimation imperative for realistic manipulation is not straightforward. Moreover, achieving this level of scene perception in real-time from a computationally constrained and highly shaky and agile platform, such as a small an Unmanned Aerial Vehicle (UAV) is perhaps the most challenging scenario for perception for manipulation. Drawing inspiration from otherwise computationally constraining Computer Vision techniques, we present a system combining visual, inertial and depth information to achieve dense, local scene reconstruction of high precision in real-time. Our evaluation testbed is formed using ground-truth not only in the pose of the sensor-suite, but also the scene reconstruction using a highly accurate laser scanner, offering unprecedented comparisons of scene estimation to ground-truth using real sensor data. Given the lack of any real, ground-truth datasets for environment reconstruction, our V4RL Dense Surface Reconstruction dataset is publicly available.</p>
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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.