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309 results for “swarm”
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 12: Second obstacle with 100 robots before passing any robot (allows one group set to pass at a time)
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance.</p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 11: The second obstacle
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 16: The third obstacle with 100 robots after passing some robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance.</p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 10: First obstacle with 100 robots after passing all the robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance</p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 9: First obstacle with 100 robots before passing any robot
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 20: The fourth obstacle with 100 robots after passing some robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 8 : The first obstacle (allows one robot to pass at a time)
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 18: Fourth Obstacle (a part of the obstacle can be passed by a group set but the other part can be passed by only one robot at a time)
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15, 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 17: The third obstacle with 100 robots after passing all robots
<p>The swarm movement and obstacle avoidance are shown in Figures 8, 9 and 10 for the first<br> obstacle. Figures 11, 12 and 13 are to present the second obstacle and its avoidance. Figures 14, 15,<br> 16 and 17 are to present the third obstacle and its avoidance. Figures 18, 19, 20 and 21 are to<br> present the fourth obstacle and its avoidance. Figures 22, 23, 24 and 25 present the fifth obstacle<br> and its avoidance. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 4: Methodology flowchart
<p>The detailed methodology approach is given in Figure 4:</p> <p>a) Generate a random number of robots to have a swarm.<br> b) Organize the robots in circular formations, where every robot will be in a group related to<br> circular formation. This means every circular formation is a cycle that has cyclic groups of robots<br> on its circumference as shown in Figure 2 and Figure 3. We might have more than one circular<br> formation.<br> c) Move the robots forward in a steady state.<br> d) Avoiding obstacles in case of facing an obstacle, and the swarm must adapt itself based on<br> the type of the obstacle. Various types of obstacles will be considered.<br> e) The swarm reorganizes itself after avoiding the obstacle in the same way as it was before<br> facing the obstacle. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 6: Swarm Robotics Move Forward
<p>The pseudo code description to calculate the Swarm robotics circular formation is given in Algorithm 3 and Figure 6. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 7. The first obstacle with 100 robots after passing some robots
<p>The pseudo code description to move the group set to avoid the predefined first obstacle as shown later in experimentation section. First obstacle crossing is given in Algorithm 5 and Figure 7. </p> <p>Algorithm 5: Move Swarm Robotics Over First Obstacles</p> <p>Purpose: Avoid First Obstacles one robot can pass through obstacles</p> <p>.Input: Constant W= Width Of Gap on Obstacles, Oc= Center of Gap begin of Obstacle Oc2= Center of Gap end of Obstacle {p1,p2,p3} position of three robots on circumference of group set circle GSN group set number</p> <p>{P1,…,Pn}set of position for robots on group set {r1,…,rn }∈GSN DRH Distance Swarm Robotics Will Stop in Before Obstacles Cxy' New Circular Formation Center After Avoid Obstacles Cg: =cx, cy center of group set </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 2: A robot set with ten groups of robots
<p>Methodology and the proposed model :</p> <p>a) Generate a random number of robots to have a swarm.</p> <p>b) Organize the robots in circular formations, where every robot will be in a group related to circular formation. This means every circular formation is a cycle that has cyclic groups of robots on its circumference as shown in Figure 2 and Figure 3. We might have more than one circular formation.</p> <p>c) Move the robots forward in a steady state.</p> <p>d) Avoiding obstacles in case of facing an obstacle, and the swarm must adapt itself based on the type of the obstacle. Various types of obstacles will be considered.</p> <p>e) The swarm reorganizes itself after avoiding the obstacle in the same way as it was before facing the obstacle. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 5: Graph Illustrate the new points on the circumference of the circle
<p>Formulas (1) and (2) are used to determine the new points on the circumference of the circle to form the circular formation shown in Figure 5. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 1: Circular formation for nine hundred robots
<p>The previous works and studies have talked about swam robotics motion in different formations like V and Fish school in addition to areas covering capability for searching and saving missions. Our proposed approach is an attempt to reduce the computational complexity of some of the previous approaches and to make robots motion more simple and capable of avoiding obstacles regardless of the obstacles structure. </p>
BRAIN Journal-Swarm Robotics with Circular Formation Motion Including Obstacles Avoidance-Figure 1b.The five steps of Methodology Approach
<p>The General diagram of the proposed approach is shown in Figure 1.</p> <p>A fundamental problem in collective robotics is to have the group organize into global formations or patterns. These include simple patterns like circles, lines, uniform distribution within a circle or square, etc. In the presence of a central controller, these tasks are trivial, but this is not the case in a distributed system. The main goal is to have self-autonomy robots, where each behaves independently from the others based its surrounding environment including other robots' behavior. Each robot might not be aware that it works within a group. Ducatelle et al.(Ducatelle, Di Caro, Pinciroli, Mondada, & Gambardella, 2011) proposed a collective behavior based on network routing, capable of guiding a robot from a source area to a target. Similarly to what happens in packet routing, the robots keep a table of the distance of other robots with respect to the target. A robot can then use the entries in the table and reach the target. </p>
Adaptive Path Formation in Self-Assembling Robot Swarms by Tree-like Vascular Morphogenesis
<p>For self-assembly, robot swarms can be programmed to form predefined shapes. <br> However, if the swarm is required to adapt the assembled shapes to dynamic features of the environment at runtime, then the shapes' structures need to be dynamic, too. <br> Prerequisite for adaptation is exploration and detection of changes followed by appropriate rearrangements of the assembled structure. <br> We study a self-assembling robot swarm forming trees to explore its environment and searching for bright areas. <br> The tree-formation process is inspired by the vascular morphogenesis of natural plants. <br> Detecting light produces a virtual resource shared within the tree, helping to drop useless branches while reinforcing efficient paths between bright areas and the tree root.<br> We successfully verify our self-assembly approach in several swarm robot experiments in a dynamic environment showing that the robot swarm can collectively discriminate between light sources at different distances and of different qualities.</p>
Videos of evolved robot swarms in a simulated collective construction scenario
<p>The videos show robot swarms designed with population coding in an ARGoS simulation.</p> <p>In the videos 1 to 3 the swarm tries to shelter the pivot point in the middle by dragging cylinders in the gray target area.</p> <p>Viedeo 4 and 5 additionally try to collect or respectively avoid as much light as possible.</p>
SwarmTouch: Guiding a Swarm of Micro-Quadrotors with Impedance Control using a Wearable Tactile Interface
<p>The dataset "Flight_Experiments_Data.zip" represents flight experiments information of the SwarmTouch project. During the experiment, involved participants were given a task to navigate a formation of drones through the obstacles maze relying only on tactile or visual feedback. For the experiment, users overcame two different unknown setups of obstacles, each setup firstly with tactile and then with vision feedback (two trials with tactile and two with visual (without glove) feedback in total).</p> <p>The dataset "Patterns_Recognition_Data.zip " represents tactile patterns recognition experimental results. The tactile patterns were developed to represent the static and dynamic parameters of the drone swarm. This information is sent further to a human operator guiding a formation of drones with the help of tactile glove. During the experiment, each pattern was repeated once, and the subject was asked to enter the number of experienced stimuli. Each of the subjects experienced 64 stimuli (8 patterns were repeated 8 times in random order). The time of user response was also recorded.</p>
Fig. 1 in Metapolybia araujoi, a new species of swarming social wasp from the Brazilian Amazon rainforest (Vespidae: Polistinae)
Fig. 1. Metapolybia araujoi Somavilla & Andena new species: (A) dorsal view; (B) lateral view; (C) metasoma in lateral view; (D) head front view; (E) anterior and posterior wings. Scale bar = 01 mm. Figures by Agnièle Touret-Alby © MNHN.
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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.