Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
36
datasets available to search
ShareScore release 0.7.1
Dataset results
36 results for “Obstacle Avoidance.”
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>
Avoiding 3D obstacles in mixed reality: Does it differ from negotiating real obstacles?
<p>This dataset is used in "Avoiding 3D obstacles in mixed reality: Does it differ from negotiating real obstacles?". Mixed-reality technologies are evolving rapidly, allowing for gradually more realistic interaction with digital content while moving freely in real-world environments. In this study, we examined the suitability of the Microsoft HoloLens mixed-reality headset for creating locomotor interactions in real-world environments enriched with 3D holographic obstacles. In Experiment 1, we compared obstacle-avoidance maneuvers of 12 participants stepping over either real or holographic obstacles of different heights and depths. Participants’ avoidance maneuvers were recorded with three spatially and temporally integrated Kinect v2 sensors. Similar to real obstacles, holographic obstacles elicited obstacle-avoidance maneuvers that scaled with obstacle dimensions. However, some participants showed maladaptive obstacle-avoidance maneuvers to holographic obstacles by consistently failing to raise their trail foot or by crossing the obstacle with extreme margins. In Experiment 2, we examined the efficacy of mixed-reality video feedback in altering such maladaptive avoidance maneuvers. Participants quickly adjusted maladaptive maneuvers in the course of mixed-reality feedback trials and these improvements were largely retained in subsequent trials without feedback. Participant-specific differences in real and holographic obstacle avoidance notwithstanding, the present results suggest that 3D holographic obstacles, supplemented with mixed-reality feedback, may be used for studying and perhaps also training 3D obstacle avoidance.</p>
Passive Morphological Adaptation for Obstacle Avoidance in a Self-Growing Robot Produced by Additive Manufacturing
<p>Dataset acquired for the obstacle negotiation experiments. The dataset collects the forces obtained by the growing robot when facing obstacles at different inclinations.</p> <p>You an find the related publication on https://doi.org/10.1089/soro.2019.0025</p>
Avoiding obstacles while intercepting a moving target: A miniature fly's solution
<p>The miniature robber fly <i>Holcocephala fusca<i> </i></i>intercepts its targets using a system whose behaviour is approximated by the proportional navigation guidance law. During predatory trials, we challenged <em>Holcocephala</em>'s interception performance by placing a large object in its potential flight path. In response, <em>Holcocephala</em> deviated from the path predicted by pure-proportional navigation, but in many cases still eventually contacted the target. We show that such flight deviations can be explained as the output of two competing navigational systems; pure-proportional navigation and a simple obstacle avoidance algorithm. Obstacle avoidance by <em>Holcocephala</em> is here described by a simple feedback loop that uses the visual expansion of the approaching obstacle to mediate the magnitude of the turning-away response. We name the integration of this this steering law with pro-nav "Combined Guidance". The results demonstrate that predatory intent does not operate a monopoly on the fly's steering when attacking a target, and that simple guidance combinations can explain obstacle avoidance during interceptive tasks.</p>
Avoiding obstacles while intercepting a moving target: A miniature fly's solution
Open the record for dataset details and reuse information.
Data from 'Sensorimotor model of obstacle avoidance in echolocating bats'
<p>The entry contains all data reported in the paper.</p> <p>Data are provided as MATLAB mat files. These can be read using MATLAB and other (free) software, including:</p> <ul> <li>R: https://www.r-project.org/</li> <li>SciPy: http://wiki.scipy.org/Cookbook/Reading_mat_files</li> </ul> <p>Each .mat file contains the data for a separate simulation:</p> <ul> <li>Experiment1.mat: randomly distributed reflectors in horizontal plane</li> <li>Experiment2.mat: randomly distributed reflectors in vertical plane</li> <li>Experiment3.mat: randomly distributed reflectors, 3D</li> <li>Experiment4.mat: torus setting</li> <li>Experiment7.mat: patch of fir forest</li> <li>Experiment8.mat: forest road</li> <li>Experiment9.mat: vertical wires</li> <li>Experiment10.mat: horizontal wires</li> <li>Experiment11.mat: randomly distributed reflectors in horizontal plane, for FM bat</li> <li>Experiment12.mat: randomly distributed reflectors in vertical plane, for FM bat</li> </ul> <p>The files contain the same entries. The relevant entries are the following:</p> <p><strong>condition labels</strong>: the various conditions (i.e. controller variants) are encoded using the values in the arrays RD, EF, OA and CS. For example, for experiment1.mat these have following values below.</p> <ul> <li>RD = [0 0 1 2 0];</li> <li>EF = [0 1 0 0 0];</li> <li>OA = [0 0 0 0 0];</li> <li>RF = [1 1 1 1 1];</li> <li>CS = [0 0 0 0 1];</li> </ul> <p>RD: a value of 1 in RD indicates controller random A, 2 indicates controller random B.</p> <p>EF: a value of 1 indicates the the fixed ear controller.</p> <p>OA: indicates the controller with the ears fixed of axis</p> <p>RF: indicates the phases of the reflections were randomized</p> <p>CS: indicates the controller with constrained elevation</p> <p>Hence, the 5 conditions/controllers simulated for the randomly distributed reflectors in horizontal plane are the Default controller (no random, no fixed ears, no constraints), Fixed Ear, Random A, Random B and Constrained controller, respectively.</p> <p>The controller types in the other .mat files can be decoded similarly.</p> <ul> </ul> <p>Batpositions is 4D matrix [simulation steps x dimension (x, y, z) x condition x replication]. This matrix contains the 3D <strong>positions </strong>for the simulated bat for all replications and controllers. Similarly, velocities, distances, reflectors contain the <strong>speed of the bat, the distance to the nearest reflector, the number of reflectors returning an echo > 0 dB, respectively</strong>.</p> <p>Worlds contains the <strong>3D positions of the reflectors</strong> for each of the replications.</p> <p>Other variables are support variables used while running the simulations.</p>
Dataset from 'Kangur, K., Billino, J., & Hesse, C. (2017). Keeping safe: Intra-individual consistency in obstacle avoidance behaviour across grasping and locomotion task. i-perception, 8(1), 1-10. DOI: 10.1177/2041669517690412'
<p>Dataset associated with the following publication:</p> <p>Kangur, K., Billino, J., & Hesse, C. (2017). Keeping safe: Intra-individual consistency in obstacle avoidance behaviour<br> across grasping and locomotion task. i-perception, 8(1), 1-10. DOI: 10.1177/2041669517690412 <span><a></a></span></p> <p>The folder contains a data file and a description file providing column lables.</p> <p>For further questions, please contact:<br> c.hesse[at]abdn.ac.uk</p>
Data for: Effects of domestication and captive breeding on reaction to moving objects: Implications for avoidance behaviors of obstacles and predators by masu salmon Oncorhynchus masou
<p>Domestication and captive breeding can compromise obstacle- and predator-avoidance of animals in the wild. Whereas previous studies only examined these effects in combination, here we examine them individually by comparing the abilities of wild, F1 (offspring of wild parents), and captive-bred (approx. F15) masu salmon (<em>Oncorhynchus</em> <em>masou</em>) to avoid a falling object under experimental conditions. Rates of avoidance failure were low (wild, 12.5%; F1, 10.7%; captive-bred, 8%) under light conditions but increased under dark conditions (wild, 11.1%; F1, 32.1%; captive-bred, 60.0%). We attribute the elevated avoidance-failure rate among F1 fish to the lack of learning opportunities in hatchery environments, and the further elevation of avoidance-failure rate among captive-bred fish to the degradation of sensory organ function. These results imply reduced survival rates for F1 and captive-bred fish in the wild and are consistent with the low stocking efficiencies reported for captive-bred masu salmon.</p>
Data for: Effects of domestication and captive breeding on reaction to moving objects: Implications for avoidance behaviors of obstacles and predators by masu salmon Oncorhynchus masou
Open the record for dataset details and reuse information.
Locomotor patterns during obstacle avoidance in children with Cerebral Palsy
<p><strong>Supplementary material</strong></p> <p>Video S1: Video fragment of stepping over an obstacle in a TD child (TD16, 7.1 yrs).</p> <p>Video S2: Video fragment of stepping over an obstacle in a DI child (CP15, 4 yrs).</p> <p>Video S3: Video fragment of stumbling with the trailing limb in a DI child (CP7, 4.5 yrs).</p> <p>Video S4: Video fragment of stumbling with the leading limb in a DI child (CP13, 3.2 yrs).</p> <p>Video S5: Video fragment of step onto (with the leading limb, LA side) and stumbling with the trailing limb (MA side) in a HE child (CP29, 3 yrs).</p>
Obstacle avoidance function of the rolling and jumping robot
<p>Obstacle avoidance function of the rolling and jumping robot</p>
The Effect of Exercise Training Programs: Step and Stability Ball on Balance and Obstacle Avoidance in Older People
ClinicalTrials.gov study NCT01887236. IPD Sharing: Not stated. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
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.