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52 results for “Swarm robotics”
Data from: Secure and secret cooperation in robot swarms
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Robot swarms neutralize harmful Byzantine robots using a blockchain-based token economy
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Generic, Scalable and Decentralized Fault Detection for Robot Swarms
<p>This raw data archive includes the data on fault detection in a simulated swarm of 20 e-puck robots. The data was used in the paper Generic, Scalable and Decentralized Fault Detection for Robot Swarms by D. Tarapore et al. (2017).</p> <p>See readme.txt for more details.</p>
Change Detection in Dynamic Environments by Means of a Robotic Swarm - Digital Appendix
<p>Digital Appendix for my thesis Change Detection in Dynamic Environments by Means of a Robotic Swarm.</p> <p>In the zip-file you find a README that guides you through the structure and where to find the plots for the respective experiments.</p> <p><strong>Change Detection of Dynamic Environments by Means of a Robotic Swarm - Digital Appendix</strong></p> <p>The following folder structure holds all research data of my conducted experiments(h5-logfiles and plots). The Python-Script "show_h5.py" can be used to read out the logfile in h5-format (<em>$python3 show_h5.py expample_logfilename.h5</em>). However, this shouldn't be necessary because all plots are already generated.</p> <p>To find the results you want to see, this is a small guide through the structure:</p> <ol> <li> <p>First the trials are divided into the respective methods (BOCPD, PELT, DBB, DBBCPD). In the folders you find the experiments for the specific method.</p> </li> <li> <p>In the folders of BOCPD and PELT you find the results for the different feedback types and their combinations. The id for each feedback is noted in parentheses (e.g. XX_(id)_feedback_description). Feedback combinations have their ids added up (e.g. XX_(id1+...+idn)_feedback_description).</p> </li> <li> <p>In the folder to each feedback type the different test trials can be found. This means varying environment difficulties and parameter settings. In the name of the folders this information can be found (e.g. XX_method_environmentdifficulty_parametersetting).</p> </li> </ol> <p>All experiments follow the same procedure as long as it is stated otherwise. Each trial consists of 20 individual runs with a duration of 6000 seconds. At half time (3000 s) a change to the opposite fill ratio occurs (fill ratio of 1.0 defines a completely white and one of 0.0 a completely black environment).</p> <p><strong>Environment difficulty</strong></p> <ul> <li> <p>0901 --> easy environment, fill ratio changed from 0.9 to 0.1</p> </li> <li> <p>0703 --> easy environment, fill ratio changed from 0.7 to 0.3</p> </li> <li> <p>0604 --> easy environment, fill ratio changed from 0.6 to 0.4</p> </li> <li> <p>055045 --> easy environment, fill ratio changed from 0.55 to 0.45</p> </li> </ul> <p><strong>Parameter Setting</strong></p> <p>The setting is in the name of the folder composed of: feedbackID: intervalLength amountNeighbors</p> <ul> <li> <p>3c:50s3n --> feedback 3c with a 50s interval and 3 neighbors</p> </li> </ul> <p>In these folders all plots of the respective runs can be found showing a Boxplot of all 20 runs and for each run the swarm belief, the decision distribution and the reset histogram (before/after the change)</p>
Adaptive Online Fault Diagnosis in Robot Swarms
<p>Contained are the data sets generated and analysed for the paper 'Adaptive Online Fault Diagnosis in Robot Swarms' by James O'Keeffe, Danesh Tarapore, Alan G. Millard and Jon Timmis</p>
Data from: Testing the limits of pheromone stigmergy in spatially constrained robotic swarms
Area coverage and collective exploration are key challenges for swarm robotics. Previous research in this field has drawn inspiration from ant colonies, with real, or more commonly virtual, pheromones deposited into a shared environment to coordinate behaviour through stigmergy. Repellent pheromones can facilitate rapid dispersal of robotic agents, yet this has been demonstrated only for relatively small swarm sizes (N<30). Here, we report findings from swarms of real robots (Kilobots) an order of magnitude larger (N>300), and from realistic simulation experiments up to N=400. We identify limitations to stigmergy in a spatially constrained environment – a free but bounded two-dimensional workspace – using repellent binary pheromone. At larger N a simple, stigmergic avoidance algorithm becomes first no better, then inferior to, the area coverage of non-interacting random walkers. Thus, with ever-increasing swarm sizes, the assumption of robustness and scalability for such approaches may need to be re-examined. Instead, subcellular biology, and diffusive processes, may prove a better source of inspiration at large N in spatially constrained or high agent density environments.
Data from: Testing the limits of pheromone stigmergy in spatially constrained robotic swarms
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Supplementary Material: Emergent Orchestras: A Modular Framework for Musical Robot Swarms
<p>Supplementary Material containing the experiments described in the article.</p>
Fault detection in Robotic Swarm Aggregation using a Kalman Filter - Appendix
<p>The appendix for my bachelor thesis: Fault detection in Robotic Swarm Aggregation using a<br> Kalman Filter. In the images of the aggregation experiments the different lines refer to the number of clustering robots.</p>
Data from: Evolution of self-organized task specialization in robot swarms
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Coordinated movement of a swarm of nonholonomic wheeled robots modeled as virtual visco-elastic body - raw experiment data
<p>Raw experiment data from verification of 2 control algorithms on 5 nonholonomic mobile robots using OptiTrack motion capture system : virtual spring damper mesh control (algorithm A1) with and without obsticles and swarm selforganization using worm creep algorithm (algorithm A4). Conducted experiment allowed for adjustment of the control parameters (describend in Data.m files) to achieve better performance of the swarm movement.</p> <p>Abbreviations: d1, d2 - are desired interrobot distances, NO- no obsticles, Wo - with obsticles, Sor - self-organization</p> <p> </p>
Coordinated movement of a swarm of nonholonomic wheeled robots modeled as virtual visco-elastic body - videos from experiments
<p>Videos from experiments associated with raw experiment data for project titled: "Coordinated movement of a swarm of nonholonomic wheeled robots modeled as virtual visco-elastic body"</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.