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2,639 results for “Robotic”
Data from: Social robots as conversational catalysts: Enhancing long-term human-human dyadic interaction at home
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Data from: 3D printed digital pneumatic logic for the control of soft robotic actuators
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Data for: Adapting small jumping robots to compliant environments
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Real-world human-robot interaction data with robotic pets in user homes in the United States and South Korea
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Data from: Self-organizing nervous systems for robot swarms
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Datasets and images of publication: Self-healing and high interfacial strength in multi-material soft pneumatic robots via reversible Diels-Alder bonds
<p>Data and Figures of the publication:</p> <p>Terryn, S.; Roels, E.; Brancart, J.; Assche, G.V.; Vanderborght, B. Self-Healing and High Interfacial Strength in Multi-Material Soft Pneumatic Robots via Reversible Diels–Alder Bonds. <em>Actuators</em> <strong>2020</strong>, <em>9</em>, 34.</p>
Passive Morphological Adaptation for Obstacle Avoidance in a Self-Growing Robot Produced by Additive Manufacturing
<p>Dataset acquired for the obstacle negotiation experiments. The dataset collects the forces obtained by the growing robot when facing obstacles at different inclinations.</p> <p>You an find the related publication on https://doi.org/10.1089/soro.2019.0025</p>
Teleoperation with Baxter robot and haptic device: testing with experts user.
<p>In this video you can see the experiment with expert users developed by Robotics Group of the Universidad de León, as part of a research project that aims to demonstrate the effectiveness of the use of haptic devices in teleoperation environments.</p>
Sony AIBO robot dog accelerometer
<p>Dataset taken from https://www.cs.unm.edu/~mueen/robot_dog.txt</p> <p>Stored on Zenodo as backup for Stumpy Fast Pattern Matching Tutorial https://stumpy.readthedocs.io/en/latest/Tutorial_Pattern_Searching.html .</p>
Sony AIBO robot dog accelerometer query
<p>Dataset taken from https://www.cs.unm.edu/~mueen/carpet_query.txt</p> <p>Stored on Zenodo as backup for Stumpy Fast Pattern Matching Tutorial https://stumpy.readthedocs.io/en/latest/Tutorial_Pattern_Searching.html .</p>
Robot-aided Training of Propulsion During Walking: Effects of Torque Pulses Applied to the Hip and Knee Joints During Stance
<p>Dataset linked with the manuscript "Robot-aided Training of Propulsion During Walking: Effects of Torque Pulses Applied to the Hip and Knee Joints During Stance". Please see attached readme document for details</p>
Conspicuous animal signals avoid the cost of predation by being intermittent or novel: confirmation in the wild using hundreds of robotic prey
<p>Social animals are expected to face a trade-off between producing a signal that is detectible by mates and rivals, but not obvious to predators. This trade-off is fundamental for understanding the design of many animal sig- nals, and is often the lens through which the evolution of alternative communication strategies is viewed. We have a reasonable working knowl- edge of how conspecifics detect signals under different conditions, but how predators exploit conspicuous communication of prey is complex and hard to predict. We quantified predation on 1566 robotic lizard prey that per- formed a conspicuous visual display, possessed a conspicuous ornament or remained cryptic. Attacks by free-ranging predators were consistent across two contrasting ecosystems and showed robotic prey that performed a conspicuous display were equally likely to be attacked as those that remained cryptic. Furthermore, predators avoided attacking robotic prey with a fixed, highly visible ornament that was novel at both locations. These data show that it is prey familiarity—not conspicuousness—that determine predation risk. These findings replicated across different preda- tor–prey communities not only reveal how conspicuous signals might evolve in high predation environments, but could help resolve the paradox of aposematism and why some exotic species avoid predation when invad- ing new areas.</p>
Robots for Microfarms (ROMI) - Rover Video - D2.2
<p><strong>The following video shows the functionalities and usage of the Farmers Rover developed within the Robots for Microfarms (ROMI) project funded by EU Grant 773875</strong></p> <p><em>Videos are available in:</em></p> <ul> <li><em>hi-res (4K Apple ProRes)</em></li> <li><em>mid-red (4K H264)</em></li> <li><em>low-res (1080p H264)</em></li> </ul> <p><em>You can also watch it on <a href="https://www.youtube.com/watch?v=4XBq29rmo5E">Youtube</a></em></p> <p><strong>Video script:</strong></p> <p>The ROMI Rover is a farming tool that assists vegetable farmers in maintaining vegetable beds free of weeds. It does this by regularly hoeing the surface of the soil and thus preventing small weeds from taking root. It can do this task mostly autonomously and requires only minor changes to the organization of the farm. It is designed for vegetable beds between 70 cm and 120 cm wide and for crops up to 50 cm high. It currently handles two types of crops, lettuce and carrots. The lettuce can be planted out in any layout, most likely in a quincunx pattern.</p> <p>This robot is designed for smaller market farms of less than 5 ha but the size of the farm and the amount of crop you want to cover will determine the number of rovers you will use, A weekly passage of the robot should be sufficient to keep the population of weeds under control.</p> <p>The Romi Rover can help to control the pressure of weeds on crops. Too many weeds take resources such as water and sunlight away from the main crop and crop productivity will drop as a result. Because organic farming cannot use chemical herbicides, weed control is an important activity. In many cases, weeding is done manually which requires not only a lot of time but also is a demanding physical task. That’s where the Romi Rover steps in. It can alleviate this task from farmers so that they can concentrate on more rewarding activities.</p> <p>The Romi Rover is positioned over a vegetable bed, it cleans the top-soil with a rotating precision hoe. The rover must be taken to the field using the remote control or by simply pushing it. The use of the rover requires relatively flat beds. To assist the rover in navigating the length of a crop bed, it is necessary to install guide rails such as tubes or wooden boards along the bed.</p> <p>Once the rover is positioned along the rails at the beginning of a bed, it hoes the surface of the soil whilst moving itself the entire length of the bed.</p> <p>Two weeding methods are available. First, a precision weeding method in which the top-soil is turned over in between the plants. Second, a classical weeding method in which standard weeding tools are dragged behind the rover between the rows of vegetables.</p> <p>For the precision weeding method, the rover uses a camera to detect the plants that are underneath the rover. It then moves the precision weeding tool over the surface as it closely passes the detected vegetables.<br> Although the rover is autonomous for weeding a single bed, it is important to stay in proximity to the rover. A U-turn must also be manually performed at the end of the bed and the rover repositioned in line with the rails of the next bed.</p> <p>Multiple versions of the Rover have been assembled and tested in different locations to validate and showcase the adaptability of the design for different fabrication processes and field applications.</p> <p>The rover is available as an Open Source project. All of the source code and plans are freely available. This allows us to improve the design over time using input from farmers and engineers. That is also why we made the design modular using components that can be found “off-the-shelf” or that can be produced using 3D printers and laser cutters. People with development skills can also contribute. Our software is available online on Github. This makes the Romi Rover a good platform to experiment with innovative tools for farming.</p>
Deep Reinforcement Learning for END-To-END Local Motion Planning of Autonomous Aerial Robots in Unknown Outdoor Environments: Real-Time Flight Experiments
<p> </p> <p>Videos for the real flight tests and the simulation experiments </p>
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>
A laser-microfabricated electrohydrodynamic thruster forcentimeter-scale aerial robots
<p>To date, insect scale robots capable of controlled flight have used flapping wings for generating lift, but this requires a complex and failure-prone mechanism. A simpler alternative is electrohydrodynamic (EHD) thrust, which requires no moving mechanical parts. In EHD, corona discharge generates a flow of ions in an electric field between two electrodes; the high-velocity ions transfer their kinetic energy to neutral air molecules through collisions, accelerating the gas and creating thrust. We introduce a fabrication process for EHD thruster based on 355 nm laser micromachining and our approach allows for greater flexibility in materials selection. Our four-thruster device measures 1.8 * 2.5 cm and is composed of steel emitters and a lightweight carbon fiber mesh. The current and thrust characteristics of each individual thruster of the quad thruster is determined and agrees with Townsend relation. The mass of the quad thruster is 37 mg and the measured thrust is greater than its weight (362.6 µN). The robot is able to lift off at a voltage of 4.6 kV with a thrust to weight ratio of 1.38.</p>
The Robot Monster
The Robot Monster - costume by Eric Kurland, 3D Scanned at Monsterpalooza 2017 ASSET TRACKING ID: Monsterpalooza Saturday 015 Source: Objaverse 1.0 / Sketchfab
Robot List OA-Statistics
<p><strong>Standard Robot Exclusion List</strong><br /> used by OA-Statistics. Compilation of some popular black-list.</p> <p><strong>Status of this document </strong></p> <p>This document represents a consensus of the german project OA-Statistik. It is not an official standard backed by a standards body, or owned by any commercial organisation. It is not enforced by anybody, and there no guarantee that all current and future robots will use it. It has also been open for discussion.</p>
Dataset of paper: Supervised and Dynamic Neuro-Fuzzy Systems to Classify Physiological Responses in Robot-Assisted Neurorehabilitation (PLOS One)
<p>The data set contains number of user, user's physiological signals (Pulse, SCL, SCR, Respiration rate, Skin temperature), Label, Difficulty level from relax to stress. Label is codified from 1 to 5 corresponding to the Difficulty level.</p>
Raw onboard logs for "Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots"
<p>This raw data archive includes the onboard logs from the swarm of aquatic robots used to produce the paper Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots by M. Duarte et al. (2016).</p> <p>See readme.txt for more details.</p>
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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)
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.