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52 results for “swarm robotics”
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>
Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework
<h1><strong><span>Data Description and File Structure:</span></strong></h1> <p>This data repository contains the raw data collected across all the experiments describe from the paper entitled “Advancing Robotic Swarms with Blockchain Technology: A Dynamic Two-Factor Authentication Consensus Framework”. These are available as CSV files under the appropriate directories.</p> <p>Three main folders are found in this repository:</p> <ul> <li><code><strong>1FA-single-factor-auth/</strong></code> <ul> <li>Contains raw data from experiments using the Single-Factor Authentication (1FA) framework, where only on-chain consensus validation (OCV) is applied without the off-chain peer verification (OPV) phase.</li> </ul> </li> <li><code><strong>2FBC_two-factor-blockchain/</strong></code> <ul> <li>Includes data from experiments employing the Two-Factor Blockchain Consensus (2FBC) framework, which integrates both off-chain peer verification (OPV) and on-chain consensus validation (OCV) phases for enhanced security. This also contains the baseline results.</li> </ul> </li> <li><code><strong>BB_blockchain-base/</strong></code> <ul> <li>Stores the experimental data from the Blockchain Base (BB) framework, where a basic blockchain model was used without the multi-factor authentication features of 1FA or 2FBC. Most data points here are obtained from the work of Strobel et al. (2023) in their work, <u>doi/10.1126/scirobotics.abm4636</u></li> </ul> </li> </ul> <p>Under each directory, we have the following folders:</p> <ul> <li><code><strong>exp_1/</strong></code> <ul> <li>Contains data from scalability experiments, where swarm size was increased within a fixed 3.6 m² arena to evaluate the framework’s performance as the number of robots grows.</li> </ul> </li> <li><code><strong>exp_2/</strong></code> <ul> <li>Includes data from accuracy tests that varied the percentage of white tiles in the environment to assess the framework's ability to reach accurate consensus under different conditions.</li> </ul> </li> <li><code><strong>exp_3a/</strong></code> <ul> <li>Stores data from robustness experiments focused on testing the swarm's resilience to different numbers of Byzantine robots within the network.</li> </ul> </li> <li><code><strong>exp_3b/</strong></code> <ul> <li>Contains data from experiments evaluating the robustness of the swarm when subjected to various Byzantine attack types, testing the framework’s ability to handle adversarial behaviors.</li> </ul> </li> <li><code><strong>exp_4/</strong></code> <ul> <li>Holds data from the resource efficiency experiments, which measured the computational resource usage (CPU, RAM, and blockchain size) during a prolonged 10-hour swarm operation.</li> </ul> </li> </ul> <p>Each experiment configuration is carried out in 20 repetitions.</p> <h3><strong><em>Experiment 1 (exp_1):</em></strong></h3> <ul> <li><code><strong>8rob-2byz/</strong></code> Data for scalability experiments with 8 robots, 2 of which are Byzantine.</li> <li><code><strong>16rob-4byz/</strong></code> Data for scalability experiments with 16 robots, 4 of which are Byzantine.</li> <li><strong><code>24rob-6byz/</code> </strong>Data for scalability experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for scalability experiments with 48 robots, 12 of which are Byzantine.</li> </ul> <h3><strong><em>Experiment 2 (exp_2):</em></strong></h3> <ul> <li><code><strong>24rob-5floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 5% white floor tiles.</li> <li><code><strong>24rob-25floor-6byz/</strong></code> Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 25% white floor tiles.</li> <li><strong><code>24rob-45floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 45% white floor tiles.</li> <li><strong><code>24rob-75floor-6byz/</code> </strong>Data for accuracy experiments with 24 robots, 6 of which are Byzantine, and 75% white floor tiles.</li> </ul> <h3><strong><em>Experiment 3a (exp_3a):</em></strong></h3> <ul> <li><strong><code>24rob-0byz/</code> </strong>Data for robustness experiments with 24 robots and no Byzantine robots.</li> <li><strong><code>24rob-3byz/</code> </strong>Data for robustness experiments with 24 robots and 3 Byzantine robots.</li> <li><code><strong>24rob-6byz/</strong></code> Data for robustness experiments with 24 robots and 6 Byzantine robots.</li> <li><strong><code>24rob-9byz/</code> </strong>Data for robustness experiments with 24 robots and 9 Byzantine robots.</li> </ul> <h3><strong><em>Experiment 3b (exp_3b):</em></strong></h3> <ul> <li><strong><code>24rob-6byz-1style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 1 or 0% white tile estimate</li> <li><strong><code>24rob-6byz-2style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 2 or 100% white tile estimate</li> <li><strong><code>24rob-6byz-3style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 3 or attack from a Bernoulli distribution</li> <li><code><strong>24rob-6byz-4style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 4 or attack from a Uniform distribution</li> <li><code><strong>24rob-6byz-5style/</strong></code> Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 5 or flooding</li> <li><strong><code>24rob-6byz-6style/</code> </strong>Data for robustness experiments with 24 robots, 6 Byzantine robots, using attack style 6 or eavesdropping</li> </ul> <h3><strong><em>Experiment 4 (exp_4):</em></strong></h3> <ul> <li><strong><code>8rob-2byz/</code> </strong>Data for resource efficiency experiments with 8 robots, 2 of which are Byzantine.</li> <li><strong><code>16rob-4byz/</code> </strong>Data for resource efficiency experiments with 16 robots, 4 of which are Byzantine.</li> <li><code><strong>24rob-6byz/</strong></code> Data for resource efficiency experiments with 24 robots, 6 of which are Byzantine.</li> <li><code><strong>48rob-12byz/</strong></code> Data for resource efficiency experiments with 48 robots, 12 of which are Byzantine.</li> <li><strong><code>72rob-18byz/</code> </strong>Data for resource efficiency experiments with 72 robots, 18 of which are Byzantine.</li> <li><strong><code>96rob-24byz/</code> </strong>Data for resource efficiency experiments with 96 robots, 24 of which are Byzantine.</li> <li><strong><code>120rob-30byz/</code> </strong>Data for resource efficiency experiments with 120 robots, 30 of which are Byzantine.</li> </ul> <h3><strong>Relevant Files:</strong></h3> <ul> <li><code><strong>block.csv</strong></code> Contains information about each blockchain block generated during the experiment, including block number, size, timestamp, and the number of transactions. The TELAPSED column indicates the time elapsed since the previous block was generated.</li> <li><code><strong>estimate.csv</strong></code> Stores the estimates collected by each robot during the simulation. Each entry includes the time of the estimate and the estimated percentage of white tiles in the arena.</li> <li><code><strong>sc.csv</strong></code> Contains information on smart contract interactions, including the mean estimate across robots, vote counts, and whether consensus was achieved (C?).</li> <li><strong><code>extra.csv</code></strong> Records additional performance metrics during the experiments, including CPU and RAM usage, as well as the size of the blockchain data folder.</li> </ul> <h3><strong>Relevant Data Fields:</strong></h3> <ul> <li><code><strong>ID</strong></code> The identifier assigned to each robot participating in the experiment. It remains constant across all entries for a particular robot.</li> <li><code><strong>TIME</strong></code> The timestamp (in seconds) at which the data was recorded. This is relative to the start of the simulation.</li> <li><code><strong>TELAPSED</strong></code> Indicates the time elapsed between blocks or events, recorded in seconds.</li> <li><code><strong>TIMESTAMP </strong></code>Represents the Unix timestamp when a blockchain block was generated, denoting the actual system time.</li> <li><code><strong>BLOCK </strong></code>The blockchain block number created by the system during the simulation. This value increments as new blocks are added.</li> <li><code><strong>SIZE</strong></code> The size of each block in bytes, indicating the data storage requirement of each blockchain entry.</li> <li><strong><code>ESTIMATE</code> </strong>The estimate provided by the robot, representing the percentage of white tiles detected in the arena.</li> <li><strong><code>MEAN</code> </strong>The mean estimate across the swarm, as calculated on-chain via the smart contract.</li> <li><code><strong>VOTECOUNT</strong></code> Total number of estimates submitted to the smart contract for consensus validation.</li> <li><code><strong>VOTEOKCOUNT</strong></code> The number of valid votes that passed the validation process (e.g., not flagged as outliers).</li> <li><strong><code>C?</code> </strong> A Boolean value indicating whether consensus has been achieved for a given block of estimates.</li> <li><code><strong>CPU</strong></code> Percentage of CPU utilization, showing the computational load on the robot during the simulation.</li> <li><code><strong>RAM</strong></code> The amount of RAM used by each robot during the experiment, measured in percent or bytes.</li> <li><code><strong>KB</strong></code> The size of the blockchain data folder, measured in kilobytes (KB). This indicates how much data was stored by the blockchain system during the experiment.</li> </ul>
Three-dimensional CAD model of the robotic system used for acquiring samples from bacterial swarms
<p>This CAD model shows the robotic sampling system that was used in the scientific article "Simultaneous spatiotemporal transcriptomics and microscopy of <em>Bacillus subtilis</em> swarm development reveal cooperation across generations" by the following authors: Hannah Jeckel*, Kazuki Nosho*, Konstantin Neuhaus, Alasdair D. Hastewell, Dominic J. Skinner, Dibya Saha, Niklas Netter, Nicole Paczia, Jörn Dunkel, Knut Drescher. The symbol "*" indicates an equal contribution. </p> <p>The CAD model consists of 81 individual files in IPT or IAM format, which need to be loaded together into a AutoDesk Inventor to be viewed. We used AutoDesk Inventor 2021 to create and view this CAD model. </p>
Data from: Self-organizing nervous systems for robot swarms
Open the record for dataset details and reuse information.
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>
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>
Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot
<p>This video is a supporting material of the paper "Swarming Behavior Emerging from the Uptake–Kinetics Feedback Control in a Plant-Root-Inspired Robot". The paper presents a plant root behavior-based approach to defining the control architecture of a plant-root-inspired robot, which is composed of three root-agents for nutrient uptake and one shoot-agent for nutrient redistribution. By taking inspiration and extracting key principles from the uptake of nutrient, movements and communication strategies adopted by plant roots, we developed an uptake–kinetics feedback control for the robotic roots. Exploiting the proposed control, each root is able to regulate the growth direction, towards the nutrients that are most needed, and to adjust nutrient uptake, by decreasing the absorption rate of the most plentiful one. Results from computer simulations and implementation of the proposed control on the robotic platform, Plantoid, demonstrate an emergent swarming behavior aimed at optimizing the internal equilibrium among nutrients through the self-organization of the roots. Plant wellness is improved by dynamically adjusting nutrients priorities only according to local information without the need of a centralized unit delegated for wellness monitoring and task allocation among the agents. Thus, the root-agents can ideally and autonomously grow at the best speed, exploiting nutrient distribution and improving performance, in terms of exploration capabilities and exploitation of resources, with respect to the tropism-inspired control previously proposed by the same authors.</p> <p>The supplementary video (Supplementary Video S1) shows how each agent independently moves according to their internal state and local perception, and the immediate response of the uptake–kinetics mechanism that, as soon as the missing nutrient is inserted in the environment, leads to a decreasing of the imbalance of nutrients in the whole plant.</p>
Integrated automatic design process for robot swarms
<p>Demonstration of the Integrated automatic process for robot swarms in three missions: Aggregation, Foraging, and Migration.<br> The dataset contains the following:<br> 1. SML (Swarm Mission Language) related files: <br> - specification files used to create missions,<br> - generated files to be used by an optimization method <br> 2. AUTOMODE related files:<br> - the log files running AUTOMODE - an optimization method that generates control software for different missions<br> - generated control software <br> 3. Demonstration:<br> - snapshots and videos of running the missions on real robots</p>
Data from: Secure and secret cooperation in robot swarms
<p>The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a method to encapsulate cooperative robotic missions in an authenticated data structure known as Merkle tree. With this method, operators can provide the "blueprint" of the swarm's mission without disclosing its raw data. In other words, data verification can be separated from data itself. We propose a system where robots in a swarm, to cooperate towards mission completion, have to "prove'' their integrity to their peers by exchanging cryptographic proofs. We show the implications of this approach for two different swarm robotics missions: foraging and maze formation. In both missions, swarm robots were able to cooperate and carry out sequential tasks without having explicit knowledge about the mission's high-level objectives. The results presented in this work demonstrate the feasibility of using Merkle trees as a cooperation mechanism for swarm robotics systems in both simulation and real-robot experiments, which has implications for future decentralized robotics applications where security plays a crucial role. This dataset includes all experimental data generated for this paper. </p>
Robot swarms neutralize harmful Byzantine robots using a blockchain-based token economy
Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (e.g., a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed. In this paper, we show that security issues can be addressed by setting up a token economy between the robots. To create and maintain the token economy, we use blockchain technology, originally developed for the digital currency Bitcoin. The robots are given crypto tokens that allow them to participate in the swarm's security-critical activities. The token economy is regulated via a smart contract that decides how to distribute crypto tokens among the robots depending on their contributions. We design the smart contract so that Byzantine robots soon run out of crypto tokens and can therefore no longer influence the rest of the swarm. In experiments with up to 24 physical robots, we demonstrate that our smart contract approach indeed works: the robots can maintain blockchain networks and a blockchain-based token economy can be used to neutralize the destructive actions of Byzantine robots in a collective-sensing scenario. In experiments with more than 100 simulated robots, we study the scalability and long-term behavior of our approach. The obtained results demonstrate the feasibility and viability of blockchain-based swarm robotics.
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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.