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2,639 results for “Robotic”
IntelliMan_WP4_Adaptive Shared Autonomy_T4.2_Advanced human-robot interaction modalities_human robot handover_v0
<p><span>The dataset contains data related to the experiments presented in the publication:</span></p> <p><em><span>M. Costanzo, C. Natale and M. Selvaggio, "Visual and Haptic Cues for Human-Robot Handover*," 2023 32nd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN), Busan, Korea, Republic of, 2023, pp. 2677-2682, doi: 10.1109/RO-MAN57019.2023.10309480.</span></em></p>
"WLRI-HRC" - A Dataset of Infrared Images for Human-Robot Collaboration in Manufacturing Environment
<p>This repository contains all needed data sets for the contribution in Journal of Sensors and Sensor Systems "Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset WLRI-HRC and evaluation of convolutional neural networks". You may use this data for scientific, non-commercial purposes, provided that you give credit to the owners when publishing any work based on this data.</p> <p><strong>DOI: 10.5194/jsss-14-37-2025</strong></p> <p> </p> <p><strong>or as BibTex:</strong></p> <div> <div>@article{sume_enhancing_2025,</div> <div> title = {Enhancing human–robot collaboration with thermal images and deep neural networks: the unique thermal industrial dataset {WLRI}-{HRC} and evaluation of convolutional neural networks},</div> <div> volume = {14},</div> <div> issn = {2194-8771},</div> <div> shorttitle = {Enhancing human–robot collaboration with thermal images and deep neural networks},</div> <div> url = {https://jsss.copernicus.org/articles/14/37/2025/},</div> <div> doi = {10.5194/jsss-14-37-2025},</div> <div> abstract = {This contribution introduces the use of convolutional neural networks to detect humans and collaborative robots (cobots) in human–robot collaboration (HRC) workspaces based on their thermal radiation fingerprint. The unique data acquisition includes an infrared camera, two cobots, and up to two persons walking and interacting with the cobots in real industrial settings. The dataset also includes different thermal distortions from other heat sources. In contrast to data from the public environment, this data collection addresses the challenges of indoor manufacturing, such as heat distortions from the environment, and allows for it to be applicable in indoor manufacturing. The Work-Life Robotics Institute HRC (WLRI-HRC) dataset contains 6485 images with over 20 000 instances to detect. In this research, the dataset is evaluated for implementation by different convolutional neural networks: first, one-stage methods, i.e., You Only Look Once (YOLO v5, v8, v9 and v10) in different model sizes and, secondly, two-stage methods with Faster R-CNN with three variants of backbone structures (ResNet18, ResNet50 and VGG16). The results indicate promising results with the best mean average precision at an intersection over union (IoU) of 50 (mAP50) value achieved by YOLOv9s (99.4 \%), the best mAP50-95 value achieved by YOLOv9s and YOLOv8m (90.2 \%), and the fastest prediction time of 2.2 ms achieved by the YOLOv10n model. Further differences in detection precision and time between the one-stage and multi-stage methods are discussed. Finally, this paper examines the possibility of the Clever Hans phenomenon to verify the validity of the training data and the models’ prediction capabilities.},</div> <div> language = {English},</div> <div> number = {1},</div> <div> journal = {Journal of Sensors and Sensor Systems},</div> <div> author = {Süme, Sinan and Ponomarjova, Katrin-Misel and Wendt, Thomas M. and Rupitsch, Stefan J.},</div> <div> month = feb,</div> <div> year = {2025},</div> <div> note = {Publisher: Copernicus GmbH},</div> <div> pages = {37--46},</div> <div>}</div> </div>
Mari4_YARD - Collabortive Robots - Dataset
<div> <div>Welcome to the Plasma Cut App with collaborative robot Demonstration Results Dataset, a unique collection of data showcasing the information needed for localizing the robot and performing an automatic cut in the target structure. This dataset is designed to facilitate research in robotics and perception, particularly in the areas of collaborative application.This dataset is generated during the demonstration of the projection technology at NODOSA and AIMEN facilities during technology demonstrations.</div> </div>
Tiny Robotics Dataset and Benchmark for Continual Object Detection
<p>Dataset for <strong>TiROD</strong>: Tiny Robotics Dataset and Benchmark for Continual Object Detection<br><br>Official Website -> <a href="https://pastifra.github.io/TiROD/">https://pastifra.github.io/TiROD/</a></p> <p>Code -> <a href="https://github.com/pastifra/TiROD_code">https://github.com/pastifra/TiROD_code</a></p> <p>Video -> <a href="https://www.youtube.com/watch?v=e76m3ol1i4I">https://www.youtube.com/watch?v=e76m3ol1i4I</a></p> <p>Paper -> <a href="https://arxiv.org/abs/2409.16215">https://arxiv.org/abs/2409.16215</a></p>
Function of a multimodal signal: a multiple hypothesis test using a robot frog
<p>1. Multimodal communication may evolve because different signals may convey information about the signaller (content-based selection), increase efficacy of signal processing or transmission through the environment (efficacy-based selection), or modify the production of a signal or the receiver's response to it (inter-signal interaction selection).</p> <p>2. To understand the function of a multimodal signal (aggressive calls + toe flags) emitted by males of the frog Crossodactylus schmidti during territorial contests, we tested two hypotheses related to content-based selection (quality and redundant signal), one related to efficacy-based selection (efficacy backup), and one related to inter-signal interaction selection (context). For each hypothesis we derived unique predictions based on the biology of the study species.</p> <p>3. In a natural setting, we exposed resident males to a robot frog simulating aggressive calls (acoustic stimulus) and toe flags (visual stimulus), combined and in isolation, and measured quality-related traits from males and local levels of background noise and light intensity.</p> <p>4. Our results provide support to the context hypothesis, as toe flags (the context signal) are insufficient to elicit a receiver's response on their own. However, when toe flags are emitted together with aggressive calls, they evoke in the receiver qualitatively and quantitatively different responses from that evoked by aggressive calls alone. In contrast, we found no evidence that toe flags and aggressive calls provide complementary or redundant information about male quality, which are key predictions of the quality and redundant signal hypotheses, respectively. Finally, the multimodal signal did not increase the receiver's response across natural gradients of light and background noise, a key prediction of the efficacy backup hypothesis.</p> <p>5. Toe flags accompanying aggressive calls seem to provide contextual information that modify the receiver's response in territorial contests. We suggest this contextual information is increased motivation to escalate the contest, and discuss the benefits to the signallers and receivers of adding a contextual signal to the aggressive display. Examples of context-dependent multimodal signals are rare in the literature, probably because most studies focus on single hypotheses assuming content- or efficacy-based selection. Our study highlights the importance of considering multiple selective pressures when testing multimodal signal function. </p>
Dataset - Swarm of Micro Flying Robots in the Wild
<p>Dataset for manuscripts "Swarm of Micro Flying Robots in the Wild".</p> <p>The file "data_benchmark.zip" contains data files of the simulation and real-world experiments of the manuscript: "Swarm of Micro Flying Robots in the Wild". And it also contains MATLAB scripts to recreate the plots and graphs as presented in the manuscript.<br> Please see [data_out/ReadMe.txt] for code usage.</p> <p>The file "hardware.zip" contains PCB files and mechanical drawings of our micro flying robots.</p> <p>The file "realworldflight_software.zip" contains the source code of object detection and localization drift correction used in real-world experiments.<br> </p> <p> </p>
3D Point Cloud Data for LiDAR-based Mobile Robot
<p>LiDAR point cloud data serves as an machine vision alternative other than image. Its advantages when compared to image and video includes depth estimation and distance measurement. Low-density LiDAR point cloud data can be used to achieve navigation, obstacle detection and obstacle avoidance for mobile robots. autonomous vehicle and drones. In this metadata, we scanned over 1400 objects and classified it into 6 groups of object namely, human, cars, motorcyclist, signboard, road divider and others.</p>
Motor-Imagery EEG Dataset During Robot-Arm Control
<p><strong>Experiment Description:</strong></p> <p>This experiment involved <strong>12 healthy subjects</strong> with no prior experience on neurofeedback or BCI, and without any known neurological disorders. All participants are right-handed, except one ambidextrous (participant #5). All participants have provided their signed informed consent for participating in the study in accordance with the 1964 Declaration of Helsinki.</p> <p>The experiment had been conducted in a laboratory environment under controlled conditions. The subjects went through <strong>three sessions</strong> lasting maximum two hours, during three consecutive days and each day at approximately at the same hour.</p> <p>During each session, participants underwent <strong>three different conditions</strong>. The first condition was always the ”<em>resting-state</em>”: the user was asked to keep the eyes open for two minutes staring at a screen with a green cross and a red arrow pointing up, and then closed for the other two minutes. After this, two more conditions followed related to a Motor Imagery (MI) task performed in a randomized order between left|right-hand movement. The two MI conditions consisted of<strong> two phases</strong> each: a training phase and a test phase. The general experimental routine for both of them was the same: each trial lasted 6 seconds (2 seconds baseline and 4 seconds MI), forewarned by the appearance of a green cross on the screen and a concomitant beep-sound a second before the onset of the task.</p> <p>Then, an arrow was appearing pointing left or right, and the subject had to imagine the movement of the corresponding arm reaching an object in front of the Baxter Robot (Rethink Robotics, Bochum, Germany). For both phases, 20 trials from left and 20 trials for right MI were generated in a randomized order, for a total of 40 trials. Finally, there was an inter-trial interval that extended randomly between 1.5 and 3.5 seconds.</p> <p>Overall, this study resulted into <strong>180 EEG </strong>datasets.</p> <p> </p> <p><strong>Data Description:</strong></p> <table> <tbody> <tr> <td><strong>Data Format</strong></td> <td>General Data Format (GDF)</td> </tr> <tr> <td><strong>Sampling Rate</strong></td> <td>250 Hz</td> </tr> <tr> <td><strong>Channels</strong></td> <td>32 EEG + 3 ACC.</td> </tr> <tr> <td><strong>EEG system</strong></td> <td>LiveAmp 32 with active electrodes actiCAP (Brain Products GmbH, Gilching, Germany)</td> </tr> </tbody> </table> <p> </p> <p><strong>Events:</strong></p> <table> <caption> </caption> <tbody> <tr> <td><strong>Code</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>32775</td> <td>Baseline Start</td> </tr> <tr> <td>32776</td> <td>Baseline Stop</td> </tr> <tr> <td>768</td> <td>Start of Trial, Trigger at t=0s</td> </tr> <tr> <td>786</td> <td>Cross on screen (BCI experiment)</td> </tr> <tr> <td>33282</td> <td>Beep</td> </tr> <tr> <td>769</td> <td>class1, Left hand - cue onset</td> </tr> <tr> <td>770</td> <td>class2, Right hand - cue onset</td> </tr> <tr> <td>781</td> <td>Feedback (continuous) - onset</td> </tr> <tr> <td>800</td> <td>End Of Trial</td> </tr> <tr> <td>1010</td> <td>End Of Session</td> </tr> <tr> <td>33281</td> <td>Train</td> </tr> <tr> <td>32770</td> <td>Experiment Stop</td> </tr> </tbody> </table> <p> </p> <p><strong>Directory Tree:</strong></p> <p>ROOT<br> | chanlocs.locs<br> |<br> |<br> +--- USER #<br> | +---SESSION #<br> | | +---CONDITION #<br> | | | \---RESTING_STATE<br> | | | +---1st_PERSON<br> | | | | TRAINING<br> | | | | ONLINE<br> | | | +---3rd_PERSON<br> | | | | TRAINING<br> | | | | ONLINE</p>
Ammonite Robot Dataset
<p>Dataset for ammonite robotics project. This dataset is comprised of a .zip folder containing: 1) the Arduino code uploaded to the robot microcontroller, 2) sample footage of the movement for each biomimetic robot during 3D motion tracking, and 3) 3D models in .stl format for each biomimetic robot (serpenticone, oxycone, sphaerocone, and morphospace center). Each robot morphotype has their own file including models of: both batteries (3.7V and 7.4V), counterweights cast with bismuth, the electronics cartridge, electronic components (microcontroller, charger/regulator, motor driver, wires, LED indicator, and IR sensor), the PETG impeller, chamber liquid and water pump liquid (LiquidALL.stl), a brushed DC motor, PETG parts 1-4, PETG lid, self-healing rubber valve, and the water displaced by the external model. The PETG parts were printed in natural colored PETG with solid infill and 0.12 mm vertical resolution.</p>
Video-Trajectory Robot Dataset
<p>This dataset consists of color and depth videos of Panda robot motions and their corresponding joint and Cartesian trajectories. The dataset also includes the trajectories of a receiver robot for the purpose of an object handover. Each motion sample comprises 6 files (RGB video, depth video and 4 giver/receiver trajectories in time series form). Total number of motion samples: 38393.</p> <p>Structure: MPEG-4 videos of robot motion and corresponding Python serialized (or “pickled”) files, containing joint and Cartesian trajectories. Dataset is divided into four parts: simulation dataset (PandaHandover_Sim.zip), real train dataset (PandaHandover_Real_Train.zip), real validation dataset (PandaHandover_Real_Val.zip), real test dataset (PandaHandover_Real_Test.zip). Extract using 7-Zip or similar software. Video files (.avi) can be opened using VLC media player or any other video player that supports MPEG-4 codec. The .pkl files can be loaded using Python (>=3.7) and the Python library Pandas (>=1.1.3).</p>
Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback [Video]
<p>Video of the paper submitted at RO-MAN 2022 </p> <p>Human-robot co-manipulation of soft materials: enable a robot manual guidance using a depth map feedback<br> Giorgio Nicola, Enrico Villagrossi, Nicola Pedrocchi</p> <p>Code for trainings and test available at:</p> <p>https://github.com/giorgionicola/SMAHRCO</p>
Magnetic Soft Robotic Bladder for Assisted Urination.
<p>Dataset</p>
Dataset - Literature on service robots in the hospitality industry
<p>List of fifty-nine articles retrieved from Web of Science, Google Scholar, and Scopus databases upon search inquiries with "robot," "hotel," and "hospitality" keywords. Data collection between October 2021 and March 2022. Figures of analysis in three clusters: robot*-customer relationship, robot-employee relationship, and robot-firm relationship. </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>
Robotics and Digital Systems Engineering at the Tec de Monterrey
<p><b>Abstract</b></p><p class="dhik-abstract-content">This talk addresses the core competencies of Robotics and Digital Systems Engineers nowadays as well as their integration into undergraduate-level curriculum. Background, curricular map, and examples of international collaboration are detailed based on the experience of Tecnológico de Monterrey.</p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (DOI:<a href="https://zenodo.org/record/7123800">10.5281/zenodo.7123800</a>)</li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (DOI:<a href="https://zenodo.org/record/7123814">10.5281/zenodo.7123814</a>)</li><li><b>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (<a href="#collapseTwo">Video</a>)</b></li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Swimming robots - exploring the unterwater from the surface
<p><b>Abstract</b></p><p class="dhik-abstract-content">In this presentation we introduce the principles of fusion filters, in particular the Kalman Filter, to achieve autonomous navigation of underwater robots. We present the results of autonomous swimming inside a pool using the BlueROV2 robot and also the first experiences of deploying the underwater robot for exploration of a water dam using sonars and cameras.</p><p></p><p><b>Weitere Beiträge aus dem DHIK-Forum 2022 auf Zenodo:</b></p><p class="dhik-session-list"></p><ul><li>Session #1: Viktor Sigrist: Internationalisierung - Partnerschaften für den Ausbau von Forschung und Entwicklung (DOI:<a href="https://zenodo.org/record/7123701">10.5281/zenodo.7123701</a>)</li><li>Session #2: Dieter Leonhard: DHIK- Strategien der internationalen Zusammenarbeit in Forschung und Lehre (DOI:<a href="https://zenodo.org/record/7123456">10.5281/zenodo.7123456</a>)</li><li>Session #3: Stephen Wittkopf: Wissens- und Innovationstransfer - Interdisziplinäre Zusammenarbeit mit Unternehmen und Institutionen (DOI:<a href="https://zenodo.org/record/7025707">10.5281/zenodo.7025707</a>)</li><li>Session #4: Xiao Feng: CDHAW - Chinesisch-Deutsche Hochschule für Angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123458">10.5281/zenodo.7123458</a>)</li><li>Session #5: Antonio Pita und Isabel Kreiner: Academy-Industry-Collaboration - Outreach Strategy (DOI:<a href="https://zenodo.org/record/7123460">10.5281/zenodo.7123460</a>)</li><li>Session #6: Martin Sternberg: Promotionsrecht – aktueller Stand an deutschen Hochschulen für angewandte Wissenschaften (DOI:<a href="https://zenodo.org/record/7123757">10.5281/zenodo.7123757</a>)</li><li>Session #7: Adrian Derungs: Duo mit Innovationskraft - Zusammenspiel von Forschung und Wirtschaft in der Zentralschweiz (DOI:<a href="https://zenodo.org/record/7123767">10.5281/zenodo.7123767</a>)</li><li>Session #8: Theres Paulsen: Transdisziplinäre Forschung - komplexe gesellschaftliche Herausforderungen erfordern diverse Ansätze (DOI:<a href="https://zenodo.org/record/7123769">10.5281/zenodo.7123769</a>)</li><li>Session #9: Jörg Schneider: International research collaboration - New funding opportunities for universities of applied sciences (DOI:<a href="https://zenodo.org/record/7123771">10.5281/zenodo.7123771</a>)</li><li>Session #10: Cornelia Spycher und Matthew Whellens: Horizon Europe - overview of funding opportunities for your research and innovation (DOI:<a href="https://zenodo.org/record/7123773">10.5281/zenodo.7123773</a>)</li><li>Session #11: Janique Siffert: Eureka Eurostars - erfolgreiche Förderung für internationale Innovationsprojekte (DOI:<a href="https://zenodo.org/record/7123777">10.5281/zenodo.7123777</a>)</li><li>Session #12: Ludger Fischer: Energy Lab - ein Netzwerk für innovative Lösungen im Energiebereich (DOI:<a href="https://zenodo.org/record/7123779">10.5281/zenodo.7123779</a>)</li><li>Session #13: Jörg Worlitschek: Thermal energy storage - heating the north, cooling the south (DOI:<a href="https://zenodo.org/record/7123781">10.5281/zenodo.7123781</a>)</li><li>Session #14: Jonas Mühlethaler: Neues DC Microgrid-Konzept – netzunabhängige Elektrifizierung in Entwicklungsländern (DOI:<a href="https://zenodo.org/record/7123783">10.5281/zenodo.7123783</a>)</li><li>Session #15: Tommy Claussen: Dekarbonisierung des Gebäudesektors - digitale Transformation in der Gebäudetechnik und im Gebäudemanagement (DOI:<a href="https://zenodo.org/record/7123785">10.5281/zenodo.7123785</a>)</li><li>Session #16: Christoph Imboden: Flexibility solutions - making the power grid fit for the future (DOI:<a href="https://zenodo.org/record/7123787">10.5281/zenodo.7123787</a>)</li><li>Session #17: Uwe Schulz: Spielerisches Sarnetz - Simulationen für die fossile Unabhängigkeit einer Ortschaft (DOI:<a href="https://zenodo.org/record/7123790">10.5281/zenodo.7123790</a>)</li><li>Session #18: Jana Koehler: Künstliche Intelligenz – Erfolg durch Erwünschtheit, Machbarkeit und Wirtschaftlichkeit (DOI:<a href="https://zenodo.org/record/7123792">10.5281/zenodo.7123792</a>)</li><li>Session #19: Rolf Kamps: KI in der Prävention - Befragungsmethoden und Schulungen trainieren, Krankheitserreger erkennen (DOI:<a href="https://zenodo.org/record/7123794">10.5281/zenodo.7123794</a>)</li><li>Session #20: Gwendolyne Pascua: Artificial Intelligence in Space - CIMON assisting astronauts on the International Space Station (DOI:<a href="https://zenodo.org/record/7123796">10.5281/zenodo.7123796</a>)</li><li>Session #21: Tobias Matter et.al.: Augmented Reality Soundscapes - mit maschinellem Lernen Klangkulissen von zukünftigen Bauvorhaben generieren (DOI:<a href="https://zenodo.org/record/7123798">10.5281/zenodo.7123798</a>)</li><li>Session #22: Angela Nicoara: Internet of Things - transforming businesses, people's lives and driving growth in the coming years (DOI:<a href="https://zenodo.org/record/7123800">10.5281/zenodo.7123800</a>)</li><li>Session #23: Adrian Koller: Feldrobotik - unermüdliche und zunehmend intelligentere Hilfe in der Landwirtschaft (DOI:<a href="https://zenodo.org/record/7123802">10.5281/zenodo.7123802</a>)</li><li>Session #24: Widar von Arx et.al.: Realisierung der Verkehrswende - Einfluss der Preispolitik in der Mobilität (DOI:<a href="https://zenodo.org/record/7124000">10.5281/zenodo.7124000</a>)</li><li>Session #25: Andreas Liebrich: Tourismusdateninfrastruktur - Was die Schweiz von Europa lernen kann (DOI:<a href="https://zenodo.org/record/7123806">10.5281/zenodo.7123806</a>)</li><li>Session #26: Frank Pöhlau und Stefan May: Find life on Mars - Schülerprojekte zur mobilien Robotik (DOI:<a href="https://zenodo.org/record/7123808">10.5281/zenodo.7123808</a>)</li><li>Session #27: Jiayun Shen: Open Innovation - Innovationsmanagement bei der Schweizerischen Post (DOI:<a href="https://zenodo.org/record/7123810">10.5281/zenodo.7123810</a>)</li><li>Session #28: Tobias Specker: Interkulturelles Management – innovative Konzepte zum Ausbau der China-Kompetenzen an Hochschulen (DOI:<a href="https://zenodo.org/record/7123812">10.5281/zenodo.7123812</a>)</li><li><b>Session #29: Elena Algorri: Swimming robots - exploring the unterwater from the surface (<a href="#collapseTwo">Video</a>)</b></li><li>Session #30: Sergio Camacho: Robotics and Digital Systems Engineering at the Tec de Monterrey (DOI:<a href="https://zenodo.org/record/7123816">10.5281/zenodo.7123816</a>)</li><li>Session #31: Thomas Dorn: Industrie 4.0 - Forschungskooperationen mit der CDHAW und der Tongji Universität Shanghai (DOI:<a href="https://zenodo.org/record/7123818">10.5281/zenodo.7123818</a>)</li><li>Session #32: Walter Reichert et.al.: Kollaboration und Unterstützung - Mobile Robotik und Exoskelette in der flexiblen Produktion (DOI:<a href="https://zenodo.org/record/7123820">10.5281/zenodo.7123820</a>)</li><li>Session #33: Louis Palmer: Solar Butterfly - climate pioneer world tour supported by HSLU (DOI:<a href="https://zenodo.org/record/7123822">10.5281/zenodo.7123822</a>)</li></ul><p></p>
Dataset for Sound-based Anomalies Detection in Agricultural Robotics Application
<p>This data set contains data related to a Mowing Intelligent Tool (MowIT).</p> <p>Two different microphones were used to collect the sound samples, recording the audio with just one single channel, with a sampling rate of 44100 Hz and 16 bits resolution.</p> <p>The data provided by an inertial measurement unit (IMU) was also recorded since that was already integrated into the MowIT.</p> <p>Two different data collections were performed in different open-air environments with grass to cut.</p> <p>In each collection, eight different sample sets were made, five with the machine cutting using a trimmer line and the other three using the blades. Various combinations were used in each set, and tools were or were not placed on each of the three cutting axes of the MowIT. For each group, the acquisitions were designated from 0 to 7.</p> <p>Each folder of the first collection is a combination containing two audio files, one for each microphone used, the IMU data and a photograph of the lower part of the MowIT to understand the configuration used.</p> <p>In the second collection, to improve the variety of data, three distinct sub-sets were performed for combination: the first with the MowIT turned on but not cutting grass and the next two cutting grass. </p> <p>In samples 4 and 7, there is one audio where the MowIT cuts but stops due to motor stress. In sample 6, the initial recording was not made without cutting grass, and only the two recordings were made cutting grass.</p> <p> </p> <p> </p> <p> </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>
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>
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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.