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309 results for “swarm”
Tibial apophysis not bifid (D2) in An of Zelotibia (Araneae, Gnaphosidae), a spider genus with a species swarm in the Albertine Rift
Tibial apophysis not bifid (D2)
Data from: Phylogeography of Aegean green toads (Bufo viridis subgroup): continental hybrid swarm vs. insular diversification with discovery of a new island endemic
Background: Debated aspects in speciation research concern the amount of gene flow between incipient species under secondary contact and the modes by which post-zygotic isolation accumulates. Secondary contact zones of allopatric lineages, involving varying levels of divergence, provide natural settings for comparative studies, for which the Aegean (Eastern Mediterranean) geography offers unique scenarios. In Palearctic green toads (Bufo viridis subgroup or Bufotes), Plio-Pleistocene (~2.6 Mya) diverged species show a sharp transition without contemporary gene flow, while younger lineages, diverged in the Lower-Pleistocene (~1.9 Mya), admix over tens of kilometers. Here, we conducted a fine-scale multilocus phylogeographic analysis of continental and insular green toads from the Aegean, where a third pair of taxa, involving Mid-Pleistocene diverged (~1.5 Mya) mitochondrial lineages, earlier tentatively named viridis and variabilis, (co-)occurs. Results: We discovered a new lineage, endemic to Naxos (Central Cyclades), while coastal islands and Crete feature weak genetic differentiation from the continent. In continental Greece, both lineages, viridis and variabilis, form a hybrid swarm, involving massive mitochondrial and nuclear admixture over hundreds of kilometers, without obvious selection against hybrids. Conclusions: The genetic signatures of insular Aegean toads appear governed by bathymetry and Quaternary sea level changes, resulting in long-term isolation (Central Cyclades: Naxos) and recent land-bridges (coastal islands). Conversely, Crete has been isolated since the end of the Messinian salinity crisis (5.3 My) and Cretan populations thus likely result from human-mediated colonization, at least since Antiquity, from Peloponnese and Anatolia. Comparisons of green toad hybrid zones support the idea that post-zygotic hybrid incompatibilities accumulate gradually over the genome. In this radiation, only one million years of divergence separate a scenario of complete reproductive isolation, from a secondary contact resulting in near panmixia.
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>
Software system integration of heterogeneous swarms of robots
<p>Paper: Software system integration of heterogeneous swarms of robots</p> <p>Robots> E-puck, E-puck+Raspberry Pi 2 B, Khepera III, Khepera III+Raspberry Pi 2 B, Koala+Laptop.</p> <p>Using the ROS drivers a series of tests were devised to prove that the robots in the swarm can communicate at a software level using ROS. The robots have individual goals, in this case to follow simple movement commands. The robots move after receiving command information from their program whether it is local or from another robot. All programs needed for each test will be called individually. A desktop computer together with the programs SSH and X11VNC were used in the tests to connect remotely to the robots, i.e. the laptop and Raspberry PIs mounted on the robots.</p> <p><em>1. Single-Minded Robot Swarm.</em> A main robot Koala is running all the drivers and programs for each robot. The programs for the robots communicate through the roscore and send commands to their respective robots using Bluetooth.</p> <p><em>2. Swarm with Leader Robot and Standalone Basic Robots. </em>This test is done with three Standalone robots. Each robot runs it’s own drivers and they all connect to a single roscore. In this test a Koala robot is used as a Leader running roscore and rtab map program for ROS (http://wiki.ros.org/rtabmap_ros) to create maps with the Kinect sensor. The mapping information is needed for SLAM. To connect to a single roscore all the robots must have the following two ROS commands run in the terminal to tell the system where the roscore is run and the IP of each robot: <em>export ROS_MASTER_URI=http://Leader_IP:11311 </em>and<em> export ROS_IP=Current_Robot_IP .</em></p> <p><em>3. Swarm with Standalone Robots Connecting to a Central Robot. A</em>ll the robots run their drivers and other high-level programs standalone, but they connect and communicate with each other through a single roscore running on a single robot. In this test, the E-puck robot runs along with its driver and uses gmapping ROS nodes (http://wiki.ros.org/gmapping) for SLAM. The K3 is running the driver and a simple movement program. The Koala robot runs rtab map with the Kinect and has a wireless controller connected via USB for movement commands. The controller Linux drivers are linked to a ROS joy node (http://wiki.ros.org/joy) which will publish the controller data. To prove the communication between robots, the controller on the Koala is used to send movement commands to the Koala and E-puck.</p> <p><em>4. Swarm with Standalone Robots. </em>In this test each robot is completely independent, running roscore and all programs. The robots connect with each other using rosmultimaster nodes (http://wiki.ros.org/multimaster_fkie). All the robots in this test subscribe to the joy node running on the Koala, and thus move receiving commands from the controller.</p> <p><em>5. Combined Swarm - Single-Minded Robot Swarm(s) + Standalone Robots. </em>In the final test all Standalone robots run their own roscore and the rosmultimaster node. The Standalone robots have other simple robots connected to them using Bluetooth and run their own drivers, other more advanced programs and the simple robots’ ROS drivers. A tested goal here is to have simple robots such as those from the first test receive commands from another Standalone robots in the swarm through its Leader robot. The Koala controller joy node was used again publishing data on the ROS network. Any robot driver connected to this network can subscribe to the publishing joy node and thus the simple robots not connected to the Koala with the controller, receive commands from the controller through their own respective Leader robot.</p>
P colonies and P swarms for controlling robot swarms. Experimental setups and demonstration videos
<p>These eleven videos present different experimental scenarios used to test the flexibility and functioning of the LULU P colony/P swarm simulator and of the associated application Lulu_Kilobot for controlling robot swarms. Both applications will be published on Github under an open-source license. The input P colony (input) file, swarm configuration (config) file and V-REP scene (.ttt) are available for each video in the associated .zip archive.</p> <p>The LULU simulator was included as a Python module in Lulu_Kilobot in order to test robot controllers based on P colonies, XP colonies, and P swarms for swarms of up to 10 Kilobot robots.</p> <p>In the following sections, we present a small description for each of the eleven attached videos.</p> <p>-----------------------------------------------------------------------------------------------<br /> 1_clone_10_circle</p> <p>This video demonstrates the use of the robot cloning function of the vrep_bridge script in order to create 9 distinct copies of the source robot and distribute them on a circle around the source robot. The copies are so positioned by a distribution function that can be adapted to other forms. This cloning function allows one to generate large swarms of robots with ease.</p> <p>-----------------------------------------------------------------------------------------------<br /> 2_one_pcolony_for_three_kilobots</p> <p>In this video, we simulate a simple P minus colony using Lulu_Kilobot, on three different robots. At each subtraction, the robots move one step forward. At the beginning of the clip one can see the Robot - P colony association table, where each robot has a distinct copy of the original P colony.</p> <p>-----------------------------------------------------------------------------------------------<br /> 3_pswarm_5_robots_3_colonies</p> <p>This video demonstrates the flexibility offered by the config file of Lulu_Kilobot. From the config file we explicitly specify that the first two robots should use the go straight P colony. For the other colonies, we specify the number of robots that should be assigned, go left = 1 and go right = 2.</p> <p>From the Robot - P colony association table, one can see that the first robot that is assigned a P colony uses the original P colony while the others use an independent copy of the P colony.</p> <p>-----------------------------------------------------------------------------------------------<br /> 4_pswarm_2_robots_avoid_collision</p> <p>In this experiment, we test the msg_distance agent from the input module, by continuously checking the distance from another robot.</p> <p>If the distance is short, then we stop the movement and otherwise continue to subtract f objects from the environment and move forward.</p> <p>Each of the two robots has a different P colony that was designed to check the distance from the other robot (robot_0 checks the distance from robot_1).</p> <p>-----------------------------------------------------------------------------------------------<br /> 5_pswarm_2_robots_xp_colonies_15_steps</p> <p>In this video we employ the exteroceptive communication rules (denoted by <=>) in order to synchronize the movement of two robots. The first robot moves forward 15 steps and after it stops, it signals the second robot to start moving. At this signal, the second robot starts to turn left 15 steps.</p> <p>This shows the utility of exteroceptive rules that allow XP colonies (P colonies with exteroceptive rules) to communicate using the global P swarm environment.</p> <p>-----------------------------------------------------------------------------------------------<br /> 6_1_pswarm_10_robots_disperse_steps_infinite_loop</p> <p>In this video we run a more complex algorithm that involves the use of the following modules: msg_distance, led_rgb, and motion.</p> <p>This video demonstrates dispersion, which is a typical self-deploying scenario in swarm robotics. The robots should position themselves away from one another, so that each robot is at least at a minimum distance from each of its neighbours.</p> <p>All decisions are taken by the command module, on the basis of the received input data from msg_distance. A new direction of motion (and color) is randomly chosen if there are other robots closer than a pre-set threshold distance.</p> <p>-----------------------------------------------------------------------------------------------<br /> 6_2_pswarm_10_robots_optimized_disperse_infinite_loop</p> <p>This video is an optimized version of 6.1 (10 robots disperse).</p> <p>The optimization consists in only exchanging data with V-REP when a new input request is detected in the input agents or likewise a new command object is detected in the output agents.</p> <p>This results in a step-less movement of the robots and reduces the time needed for a new decision to be applied resulting in a faster overall simulation time.</p> <p>-----------------------------------------------------------------------------------------------<br /> 7_pswarm_10_robots_optimized_disperse_infinite_loop_with_intruder</p> <p>In this video we use the previously presented optimized dispersion algorithm (6.2) and introduce an intruder robot into the scene in order to evaluate the influence that this intruder has over the behaviour of the swarm.</p> <p>One can see that robots that have stopped their movement, restart dispersing when the intruder robot is brought close enough. This can cause a chain reaction and ultimately cause the swarm to reposition.</p> <p>-----------------------------------------------------------------------------------------------<br /> 8_1_pswarm_10_robots_secure_disperse_fast</p> <p>In this video we test the proposed security protocol (based on entity authentification and P colony based id check) using the non-optimized dispersion algorithm.</p> <p>In this film we see that the robots ignore the intruder robot even though it is placed in the middle of the swarm. On the other hand, if we bring a swarm member robot close to another swarm member robot, these two will start to disperse normally.</p> <p>-----------------------------------------------------------------------------------------------<br /> 8_2_pswarm_10_robots_secure_disperse_infinite_loop_2</p> <p>In this video we present the optimized (see 6.2 for details) version of the secured dispersion algorithm.</p> <p>As was the case of the secured un-optimized version (8.1), in this secured version we test the influence of the intruder on the behaviour of the swarm by moving the intruder close to the center of the swarm and also moving the intruder close to the swarm after the dispersion is finished. We also note that if two member robots approach, the algorithm continues to work normally.</p> <p>-----------------------------------------------------------------------------------------------<br /> 9_pswarm_10_robots_secure_d_min_disperse_infinite_loop</p> <p>In this clip we show the effects of transparent input data processing.</p> <p>The command agent always requests the smallest distance available from the neighbour list, by using the d_min command.</p> <p>When the intruder robot is the nearest robot (the smallest value in the list) d_min will always return the intruder robot which cannot be processed because it is unknown to the swarm members. For this reason, a member robot that is in this situation will be blocked by the intruder robot.</p>
Demonstration videos on using P swarms for deployment tasks in swarm robotics.
<p>In this video we present the capability of the Lulu P colony / P swarn simulator to control a Kilobot robot that is a member of a swarm using a P colony based controller. The swarm application presented here is the dispersion from neighbouring robots, until a certain distance from all neighbours is reached.</p> <p>In the first video entitled kilombo_disperse_1000.avi, we simulated the interactions between 1000 Kilobots using the Kilombo simulator. The i5-4240 CPU used in this test allowed the simulation of 1000 robots, each one controlled by an individual P colony at a peak speed of 29 x real world speed.<br /> <br /> The source code of the P colony used for the dispersion algorithm is also included. This P colony is defined in the input file format accepted by the Lulu P colony / P swarm simulator.</p>
Underlying dataset Bertolini and Pastres Understanding the influence of swarming timing on the dispersal of Ostrea edulis larvae in the Northern Adriatic Sea
<p>Output from opendrift simulations (final larval positions) </p>
Anisotropy of magnetic susceptibility (AMS) and rock magnetic data for Pachmarhi dyke swarm of Deccan
<p>The Pachmarhi dyke swarms, situated in the Eastern part of the Narmada-Satpura-Tapi (N-S-T) dykes of the Deccan Continental Flood Basalt (DCFB), are studied using the Anisotropy of Magnetic Susceptibility (AMS) technique. Here, we determine the direction and sense of magma flow within the dykes to gain insights into the depth, number, and location of magma chambers, as well as the geodynamics of their plumbing system. Petrography and rock magnetism analyses revealed high-titanium magnetite particles, predominantly of Pseudo-Single Domain (PSD) nature, with a smaller proportion of Multi-Domain (MD) grains. We identified four distinct magnetic fabric types (I-IV) within the Pachmarhi dykes. The imbrication of magnetic foliation (for oblate fabric) and magnetic lineation (for prolate fabric) has been employed to discern the direction of magma flow which revealed multiple trends of magma flow, ranging from vertical/sub-vertical to inclined, with one particular dyke exhibiting lateral flow. The intersection of imbrication within the dyke margins provided valuable information about the presence of multiple shallow sub-crustal magma chambers which are supported by independent geophysical evidence and are similar to those of the Nandurbar-Dhule dyke swarms in the western region of the N-S-T dykes. Consequently, it can be inferred that the emplacement of dykes in the Pachmarhi region of the NSL, which were possible feeders to the late-stage Deccan volcanism, was primarily facilitated by a "polycentric flow" mechanism, wherein magma was injected vertically through shallow crustal fissures, potentially feeding into the late-stage Deccan flow units like Ambenali or Mahabaleshwar formation.</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>
Seismic catalogs of the 2020-2021 seismic swarm at the Bransfield Strait, Antarctica
<p>Three seismic catalogs are available:</p> <p>1. Single station catalog for station JUBA based on the Lassie software<br> 2. Moment tensor inversion catalog with moment tensor classification<br> 2. Template matching catalog</p>
High-resolution catalog of the Maurienne swarm (2017-2019)
<p>High-resolution catalog of the Maurienne swarm described in the article: "Analysis of the spatio-temporal evolution of the Maurienne swarm (French Alps) based on earthquake clustering" (in review).</p> <p>The following list describes the columns found in the file:</p> <ul> <li>CLUSTID: cluster the event belongs to;</li> <li>ORTIME: unified origin time;</li> <li>LAT_DD, LON_DD, DEP_DD: latitude (decimal degrees), longitude (decimal degrees) and depth (meters) estimated with hypoDD using both catalog and crosscorrelation differential times;</li> <li>LAT_SP, LON_SP, DEP_SP: latitude (decimal degrees), longitude (decimal degrees) and depth (meters) of the events used to analyze the spatiotemporal variations of seismicity. If an event of a cluster has not been relocated with hypoDD its location corresponds to the centroid of its cluster;</li> <li>LAT_MW, LON_MW, DEP_MW: latitude (decimal degrees), longitude (decimal degrees) and depth (meters) of the events used to analyse the global b-value. If an event is not associated to a cluster relocated with hypoDD, its location is estimated with hypoDD using only catalog differential times. If this is not possible, the hypocenter of the original catalog is used;</li> <li>MW: moment magnitude.</li> </ul> <p> </p>
On the trail of fluids in the northernmost intracontinental earthquake swarm areas of the Leipzig-Regensburg fault zone, Germany
<p>rf.dat contains a migrated receiver functions profile (X, Z, Amplitude) between 50°N and 51.15°N.</p> <p>simul_vel.dat contains a 3D velocity model obtained with SIMULPS14 (Longitude, Latitude, Depth, Vp, Vs).</p> <p>velest_vel.dat contains a 1D velocity model obtained with VELEST (Vp or Vs, Depth, Weighting).</p>
FIG. 3. — A in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 3. — A "stowed" rafter (unggat type) with two colonies. Credit: C. Vuillier.
FIG. 2 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 2. — Two models of rafters: A, sunggau muke; B, sunggau bantai. Credits: C. Vuillier.
FIG. 7 in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 7. — Two rafters taking advantage of the same access path. Credit: N. Césard.
FIG. 4. — A in An emic understanding of honey bees and their environment: attracting bee swarms to nest on rafters in Belitung, Indonesia
FIG. 4. — A rafter (tingku) in Central Sulawesi. Credits: N. Césard (A), C. Vuillier (B).
Seismicity modulation due to hydrological loading in a stable continental region: a case study from the Jektvik swarm sequence in Northern Norway
<p>This folder contains earthquake catalog used in "Seismicity modulation due to hydrological loading in a stable continental region: a case study from the Jektvik swarm sequence in Northern Norway" by Shiddiqi et al. (2022) submitted to Geophysical Journal International.</p> <p>note: unrelocated earthquakes are marked with 999 in location errors.</p>
Earthquake catalog for "Seismicity modulation due to hydrological loading in a stable continental region: a case study from the Jektvik swarm sequence in Northern Norway"
<p>This folder contains earthquake catalog used in "Seismicity modulation due to hydrological loading in a stable continental region: a case study from the Jektvik swarm sequence in Northern Norway" by Shiddiqi et al. (2022) submitted to Geophysical Journal International.</p>
Strength-mass scaling law governs mass distribution inside honey bee swarms
<p>To survive during colony reproduction, bees create dense clusters of thousands of suspended individuals. How does this swarm, which is orders of magnitude larger than the size of an individual, maintain mechanical stability? We hypothesize that the internal structure in the bulk of the swarm, about which there is little prior information, plays a key role in mechanical stability. Here, we provide the first-ever 3D reconstructions of the positions of the bees in the bulk of the swarm using x-ray computed tomography. We find that the mass of bees in a layer decreases with distance from the attachment surface. By quantifying the distribution of bees within swarms varying in size (made up of 4000–10,000 bees), we find that the same power law governs the smallest and largest swarms, with the weight supported by each layer scaling with the mass of each layer to the ≈1.5 power. This arrangement ensures that each layer exerts the same fraction of its total strength, and on average a bee supports a lower weight than its maximum grip strength. This illustrates the extension of the scaling law relating weight to strength of single organisms to the weight distribution within a superorganism made up of thousands of individuals.</p>
rDNA 18S V4 metabarcoding tables (Swarm) for Tara Oceans Expedition (2009-2013), including Tara Polar Circle Expedition (2013)
<p>Reads were grouped into OTUs using the following swarm-based pipeline: paired-end reads were merged with vsearch’s --fastq_mergepairs command (version 2.15.1, allowing for staggered reads; Rognes et al., 2016), and trimmed with cutadapt (version 3.0; Martin, 2011), keeping only reads containing both forward and reverse primers. After trimming, the expected error per read was estimated with vsearch’s command --fastq_filter and the option --eeout. Each sample was then de-replicated, i.e. strictly identical reads were merged, using vsearch’s command --derep_fulllength, and converted into fasta format. Clustering was performed at the sample level with swarm 3.0 using default parameters (Mahé et al., 2015). Prior to global clustering, individual fasta files (one per sample) were pooled and further dereplicated with vsearch. Files containing per-read expected error values were also dereplicated to retain only the lowest expected error for each unique sequence. Global clustering was performed with swarm (using the fastidious option). Cluster representative sequences were then searched for chimeras with vsearch’s command --uchime_denovo using default parameters (Edgar et al., 2011).<br> Clustering results, expected error values, taxonomic assignments, and chimera detection results were used to build a “raw” occurrence table. Reads without primers, reads shorter than 32 nucleotides and reads with uncalled bases (“N”) were discarded. For a “filtered” occurrence table, non-chimeric sequences, sequences with an expected error per nucleotide below 0.0002, and clusters containing at least 2 reads were retained. Since primer trimming is not perfect, some sequences can still contain primer fragments or be excessively trimmed. These sub- or super-sequences were identified using vsearch and merged with their closest, most abundant perfectly trimmed sequence. Finally, occurrence patterns throughout our sample collection were used to further refine the occurrence table. Clusters that contain sub-clusters with only a single-nucleotide difference but with different ecological patterns (defined here as uncorrelated abundance values in at least 5% of the samples) were turned into distinct clusters (https://github.com/frederic-mahe/fred-metabarcoding-pipeline). On the other hand, clusters with similar sequences that had correlated abundance values in at least 95% of the samples, were merged using a re-implementation of lulu's method (Frøslev et al. 2017; https://github.com/frederic-mahe/mumu).</p>
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Annotated Behaviour and Observability Dataset (ABODe)
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