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2,639 results for “Robotic”
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>
RSSI-based mobile robot localization datasets
<p>Experimental datasets used for an MSc project, titled "Outdoor Localization System for Mobile Robots Based on Radio-Frequency Signal Strength", for performing trajectory recovery of a mobile ground robot by data fusion of odometry, gyroscope and Received Signal Strength Indicator (RSSI), through Extended and Augmented-Extended Kalman Filter algorithms.</p> <p><br> "experiment_1" contains the robot's data in "rosbags" (i.e. ROS' compressed robot data), recorded in a parking lot with ground-truth from an RTK GPS. The measurements recorded were wheel odometry, IMU accelerations/angular velocities, RSSI from three receiver-transmitter pairs, regular GPS position and RTK GPS position.</p> <p>"experiment_2" contains the robot's data in "rosbags", recorded in the parking lot and in a second environment, a garden with tall trees and a nearby building. Both environments were recorded for later comparison, to see how the localization solution performed in a GPS-denied environment. All measurements were the same as in experiment 1, except for the exclusion of the common and RTK GPS.</p>
Data sheet for Robotic assisted milling for increased productivity
<p>FRF(tap test result) in EXCEL - FRF analysis - Fixed support vs mobile supportv4.xlsx<br> Form error in EXCEL - Form error.xlsx<br> Force data in EXCEL - Force data with and without support.xlsx<br> Surface roughness in EXCEL - Surface roughness-Alicona.xlsx</p>
Review of Recent Trends in Measuring the Computing Systems Intelligence-Figure 4. Intelligent robots (accessed 01.11.2017). 4.1. Erica, a humanoid robot (https://www.tech-review.com/erica-is-the-latest-japanese-robot-with-human-appearance.html). 4.2. Atlas, a bipedal humanoid robot developed by Boston Dynamics (https://en.wikipedia.org/wiki/Atlas_(robot))
<p>One of the most highly quoted and interesting definitions of machine intelligence was presented by Alan Turing (1950). Turing considered a computing system intelligent if a human assessor could not decide the nature of the system (being human or artificial) based on questions asked from a room hidden from a human assessor. Until recently there were performed different discussions and comments on the Turing test. Hernández-Orallo (2000) presents an interesting study related to the Turing Test. Dowe and Hajek, (1998) propose a computational extension of the Turing Test. The design and development of intelligent systems are historically very recent. But, even if the advance of hardware and software is very fast, it will take a longer time until the artificial computing systems will attain a similar intelligence with the humans. Based on this fact, we consider that is not appropriate to formulate the problem of the direct comparison at a general level of human intelligence with the machine intelligence. Different definitions were proposed for the intelligence of the agents (Russell, & Norvig, 2003; Iantovics, & Zamfirescu, 2013). Many authors (Russell, & Norvig, 2003; Iantovics, 2005) argue that the intelligence of the agents cannot be defined universally. The impossibility to give a universal definition to the human intelligence is based mostly on the enormous complexity of the human brain and complexity of the human thinking and decision making. Similarly, we may consider the impossibility of universal definition of intelligence of the agents based on the very large variety (by type and complexity) of intelligent agents. The machine intelligence frequently is defined based on different abilities such as (Iantovics, 2005; Sharkey, 2006): autonomous learning, self-adaptation, and evolution. These principles of considering the intelligence are inspired by biological life forms able to learn autonomously during their life cycle, to adapt to the environment and to evolve during more generations. We would like to outline that not all the designed agents are intelligent. There is not a required property of an agent to be intelligent.</p>
Artificial Intelligence and the Future of Smart Cities-Figure 10. Respondents' opinions about the use of robots in different activities (grouped by age)
<p>Question 14 was used to evaluate the respondents’ opinions about the use of robots in the following activities: performing medical surgeries, child care, supply of consumer goods, driving a car, assistance in performing tasks at work and cleaning (Figure 10). The respondents feel most confident and safe to use robots for cleaning (M=4.18, SD=1.07) and for assistance in performing tasks at work (M=4.12, SD=1.05) and less confident and safe to use robots for driving a car (M=3.87, SD=1.11), for supply of consumer goods (M=3.81, SD=.81) and performing medical surgeries (M=3.68, SD=.92) The child care obtained the lower score (M=2.00, SD=86).</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>
Robot navigation measurements driving in and out of the van
<p>This dataset is a rosbag which contains the laser scans and the pose of a Husky mobile robot, using the robot's odometry and two SICK S300 laser scans to navigate.</p> <p><strong>List of available topics:</strong></p> <ol> <li>/colrobot/hardware/scan_pointcloud</li> <li>/colrobot/navigation/perfect_match/debug/LaserScanfiltered</li> <li>/colrobot/navigation/perfect_match/LocalisationPoseResult</li> <li>/colrobot/navigation/map</li> <li>/tf</li> <li>/tf_static</li> </ol> <p><strong>Topics contents:</strong></p> <ol> <li>Pointcloud containing the merge of the two laser scans </li> <li>Pointcloud containing the laser scans which matched with the given map</li> <li>Robot's pose estimate</li> <li>Map of the van (robot's working area)</li> <li>6. Transforms of the system</li> </ol>
Human Grasp Dataset for Human Robot Handovers
<p>The dataset consists of 278.400 RGB images of size (299, 299).</p> <p>The images are sorted into the folders:</p> <ul> <li>Angles <ul> <li>30°, 45°, and 60°</li> </ul> </li> <li>Lights <ul> <li>From_Behind, From_Front, and Full_Lighting</li> </ul> </li> <li>Objects <ul> <li>Duplo_Block, Highlighter, Plastic_Pear, Table_Tennis_Racket, and Wood_Block</li> </ul> </li> <li>Persons <ul> <li>Person_1 to Person_10</li> </ul> </li> <li>Other <ul> <li>Default_Configuration, Clutter, and Other_Grasps_And_Interactions</li> </ul> </li> </ul> <p>Each folder contains 11.600 images that have a label in their file name. l=1 for grasp and l=0 for not grasp. </p>
Photomorphogenesis for Robot Self-assembly: Adaptivity, Collective Decision-making, and Self-repair
<p>Self-assembly in biological systems is an inspiration for engineered large-scale multi-modular systems with desirable characteristics, such as robustness, scalability, and adaptivity. Previous works have shown that simple mobile robots can be used to emulate and study self-assembly behaviors. However, many of these studies were restricted to rather static and inflexible aggregations in predefined shapes, and were limited in adaptivity compared to that observed in nature. We propose a photomorphogenesis approach for robots using our vascular morphogenesis model---a light-stimuli directed method for multi-robot self-assembly inspired by the tissue growth of trees. Robots in the role of `leaves' collect a virtual resource that is proportional to a real, sensed environmental feature. This resource is then shared throughout the whole robot aggregate and determines where it grows or shrinks as a reaction to the dynamic environment. In our approach the robots use supplemental bioinspired models to collectively select a seed robot to decide who starts to self-assemble (and where), or to assemble static aggregations. The robots then use our vascular morphogenesis model to aggregate in a directed way preferring bright areas, hence resembling natural phototropism (growth towards light). In this assembly, they are adaptive and able to react to a dynamic environment by collectively and autonomously rearranging the aggregate, discarding outdated parts and growing new ones. In representative experiments, the self-assembling robots collectively make rational decisions on where to grow. Cutting off parts of the aggregate triggers a self-organizing repair process in the robots, and the parts regrow. All these capabilities of adaptivity, collective decision-making, and self-repair in our robot self-assembly originate directly from self-organized behavior of the vascular morphogenesis model. Our approach opens up opportunities for self-assembly with reconfiguration on short time-scales with high adaptivity of dynamic forms and structures.</p>
Robot trajectory data for "Biohybrid Fly-Robot Interface system performs active collision avoidance"
<p>This dataset includes the videos of the trajectories of the biohybrid robot (Fly-Robot Interface), performing collision avoidance at the patterned wall corners (90 and 60 degrees). A python script for the manual tracking is also attached, as well as the processed coordinates of each individual robot trajectory, where two tracking markers were chosen on the front-left and front-right corners of the robot.</p> <p>Serial Number <20: the videos at 90-degree wall corner</p> <p>Serial Number >20: the videos at 60-degree wall corner</p>
Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment — IROS 2019
<p>This video accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
Robot view — Supplementary dataset of experiment videos, IROS 2019 — Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment
<p>This is a supplementary dataset of experiment videos of self-organized multi-robot fibre deployment. Each video is true speed and shows the full respective experiment. These videos show the <strong>robot view</strong> of each experiment.</p> <p><em>For a 2-minute summary video of these experiments, refer to:</em></p> <pre>https://doi.org/10.5281/zenodo.3357187</pre> <p>This supplementary dataset accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
Fibre view — Supplementary dataset of experiment videos, IROS 2019 — Self-organized adaptive paths in multi-robot manufacturing: reconfigurable and pattern-independent fibre deployment
<p>This is a supplementary dataset of experiment videos of self-organized multi-robot fibre deployment. Each video is true speed and shows the full respective experiment. These videos show the <strong>fibre view</strong> of each experiment.</p> <p><em>For a 2-minute summary video of these experiments, refer to:</em></p> <pre>https://doi.org/10.5281/zenodo.3357187</pre> <p>This supplementary dataset accompanies a conference paper prepared for IEEE IROS 2019.</p> <p>Using multi-robot systems for autonomous construction allows for parallelization and scalability. Swarm construction furthermore exploits robot interactions and collaboration, such that the robot swarm collectively constructs artifacts beyond what a single comparable robot could achieve. Here we present an alternative concept of swarm construction that is distinct because it uses continuous building material. Our approach is unique in its use of braiding techniques for construction. We deploy fibres that potentially allow for structures that are not possible with building blocks. To achieve maximal scalability we restrict ourselves to a decentralized approach. The main challenges are the local coordination of the robot teams, self-organized task allocation, and the dynamic reconfiguration of the braiding scheme at runtime. We successfully validate our approach in multi-robot experiments that show both braiding and branching of the braid. In addition, we show options for implementing an open system—that is robots can join and leave the braiding process on the fly.</p>
ScienceDex guides
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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.