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16 results for “Path Planning”
Data set: Industrial IoT-driven remote path planning
<p>This compressed file contains data from three different experiments during the IIoT-REPLAN experimentation phase (Industrial IoT-drive remote path planning). IIoT-REPLAN was funded by an open call from the H2020 Fed4FIRE+ project.</p> <p>Contents:</p> <p>A. astar.csv<br> This file contains the timestamp of each movement of the Robot and the uncertainty (d) at each specific time that Switch 1 was checked. The first two columns refer to seconds while the third one is a scalar value. The total duration of the experiment is 62.33 sec and the setup of this experiment is the real time application of the Astar Algorithm with the localization being based only on the sensor of the Robot</p> <p><br> B. Dijkstra.csv <br> In this experiment the full functionality of the switching system proposed in this work is highlighted. </p> <p>C. Cloud.csv<br> In this experiment the localization algorithm and the path planning algorithm are always executed on the cloud.</p> <p>In both B,C experiments the values of each column are explained inside the Dijkstra.csv </p> <p>Also, two pictures of singlie vision-based self localization are included. </p> <p>A more detailed exposition on all of the above can be found at <br> github link : https://github.com/maravger/alphabot-ppl</p>
Test shapes for ultrasonic testing coverage path planning
<p>This data set contains different geometric objects. The main intention of these it to test robotic coverage path planning with an ultrasound sensor.</p>
Figure 9. Path planning with algorithm of potential fields
<p>Since the motion trajectory of each robot is divided into several median points that the robot<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the robots through<br> the opponents to reach the destination. The routine used for this purpose is the potential field<br> method (also an alternative new method is in progress which models the robot motion through<br> opponents same as the flowing of a bulk of water through obstacles). In this method different<br> electrical charges are assigned to our robots, opponents and the ball. Then by calculating the<br> potential field of this system of charges a path will be suggested for the robot. At a higher level,<br> predictions can be used to anticipate the position of the opponents and make better decisions in<br> order to reach the desired vector.</p>
Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections? - Data Collected and Presented for the Experiments
<p>This dataset contains images collected (and processed) for the experiments of Beyond Coverage Path Planning: Can UAV Swarms Perfect Scattered Regions Inspections?" journal article, a work that defines a new path planning problem for UAVs - the Fast Inspection of Scattered Regions (FISR) - and introduces a novel method that deals with this problem - the multi-UAV Disjoint Areas Inspection (mUDAI) method. For the validation of the introduced methodology, two sets of real-world experiments were executed, one small-scale in Galatsi, Athens, were two mUDAI missions were depolyed, with two different optimization objectives for the data collection procedure (Mazimized Coverage Objective - MCO, and Balanced Coverage Objective - BCO), and one large scale in ZEP-Kissos, Thessaloniki, where a Coverage Path Planning (CPP) mission, and 2 mUDAI missions, one with a single and one with two UAVs, using both the MCO criterion for the data collection, were deployed. Regarding the CPP mission, both the collected images, and the processed results (to generate 2D, 3D, elevation, and plant health maps) are included.</p> <p>In this <a title="mUDAI - ChoosePath platform guide" href="https://sites.google.com/view/mudai-platform/" target="_blank" rel="noopener">page</a> you can find a guide for the on-line platform hosting demo instances of the algorithms used for the deployment of all experiments.</p> <p>In case you use this data, please cite the article:<br>(Article under review - more information to be included soon)</p>
Changing the reaction path of Al/Ni multilayers through planned growth defects
<p>To form line structures with 80 µm hills and valleys width into a Si <100> substrate, thermal oxidation, lithography, and wet chemical etching steps were necessary. During the procedure a inclination was formed in the transition zone between the hills and valleys, due to the KOH etching of the Si <100> substrate. A valley depth of 3.4 µm was measured after the KOH etching. To enable a self-propagating reaction on the structured Si surface, as seen in the video, a 1.2 µm thick layer of thermal SiO2 was produced. The 5 µm thick Al/Ni multilayers were then deposited with direct current magnetron sputtering in an atomic ratio of 1:1, while keeping a bilayer thickness of 50 nm. During the deposition, defects in the multilayers located at the inclined area between the hills and valleys were formed. As shown in the publication of Jaekel et al. (2022) the defects are gaps at the inclined transition zone [1]. During the ignition of the sample, these defects prevented a reaction of the multilayers deposited on the hills. Therefore, a new potential way to guide the reaction on a specific pathway could be established. The video displays an example of a self-propagating reaction, in which the bright reaction front propagates only in the valleys, with an average velocity of 7.4 m/s. The velocity is calculated with the pixels size of 34.4827 µm and the frame rate of 50000 per second. Highspeed-camera FASTCAM SA-X2 type 480K-M3 was used to obtain the video in a resolution of 512x408 pixels.</p>
Least‐cost path analysis for urban greenways planning: a test with moths and birds across two habitats and two cities
<p>1. One of the major planning tools to respond to urban landscape fragmentation is the development of ecological corridors, i.e. interconnected networks of urban green and blue spaces. Least-cost paths (LCP) appear to be an easy and appropriate resistance-based modeling method to respond to urban planners' needs. However, the ecological validation of urban corridors using LCP is rarely performed and needs to be generalized to different species, habitats and cities.</p> <p>2. We developed an experimental design to test the efficiency of LCP predictions to detect highly connecting landscape contexts that facilitate individual movements compared to movements in less connecting landscape contexts. We deliberately assigned LCP analysis parameters based on the scientific literature and expert knowledge to test a method potentially easy to use for urban stakeholders. To extend the validation, we applied our LCP model to two biological taxa with different habitat requirements: grassland-dwelling moths and forest-dwelling passerines, and to two medium-sized cities.</p> <p>3. We used mark-release-recapture (MRR) methods for moths and playback recall protocols for passerines to compare the patterns of individual movement between two contrasted connectivity contexts determined by the presence and absence of modelled LCPs. MRR protocol estimated movement rates between herbaceous patches and the two contrasted connectivity contexts. Playback recall protocol consisted in attracting individuals from wooded patches to the two contrasted connectivity contexts. A movement was considered facilitated, when displacement was rapidly engaged and individuals moved a long distance from their wooded patch.</p> <p>4. Moth and passerine movement patterns differed between the two connectivity contexts: moth recapture rates were higher in highly connecting contexts than in less connecting contexts. For passerine birds, responses to playback recalls were faster and movement distance longer in highly connecting contexts. All results support the hypothesis that both taxa were more prone to move in corridors modeled by LCP.</p> <p>5. The convergence of the results for different biological models and across cities strengthens the relevance of LCP analysis for planning urban greenways and provides guidelines for landscape planners in the development of these corridors to favor the movement and survival of multiple urban species.</p>
How biomechanics, path-planning and sensing enable gliding flight in a natural environment
<p>Gliding animals traverse cluttered aerial environments when performing ecologically relevant behaviours. However, it is unknown how gliders execute collision-free flight over varying distances to reach their intended target. We quantified complete glide trajectories amid obstacles in a naturally behaving population of gliding lizards inhabiting a rainforest reserve. In this cluttered habitat, the lizards used glide paths with fewer obstacles than alternatives of similar distance. Their takeoff direction oriented them away from obstacles in their path and they subsequently made mid-air turns with accelerations of up to 0.5 g to reorient towards the target tree. These manoeuvres agreed well with a vision-based steering model which maximized their bearing angle with the obstacle while minimizing it with the target tree. Nonetheless, negotiating obstacles reduced mid-glide shallowing rates, implying greater loss of altitude. Finally, the lizards initiated a pitch-up landing manoeuvre consistent with a visual trigger model, suggesting that the landing decision was based on the optical size and speed of the target. They subsequently followed a controlled-collision approach towards the target, ending with variable impact speeds. Overall, the visually guided path-planning strategy that enabled collision-free gliding required continuous changes in the gliding kinematics such that the lizards never attained theoretically ideal steady state glide dynamics.</p>
Accountability simulator path planning data
<p>Measurement data of developed path planning algorithms.</p> <p>Regards execution time, path length and differences of conducted modifications.</p>
Path Planning for Mobile Robot
<p>Implementation of Rapid exploring Random Tree Star (RRT*) Optimization based on Ellipsoid Equation. The simulation shows two different scenario of obstacles to test the implementation. The algorithm is running on MATLAB. The communication between Gazebo simulation and MATLAB is done through Robot Operating System (ROS) framework.</p>
Video of Automated path planning for 3D robotic filament winding of high-performance composite materials
<p>Video showing the results of the reasearch article "Automated path planning for 3D robotic filament<br> winding of high-performance composite materials"</p>
How biomechanics, path-planning and sensing enable gliding flight in a natural environment
Open the record for dataset details and reuse information.
Least‐cost path analysis for urban greenways planning: a test with moths and birds across two habitats and two cities
Open the record for dataset details and reuse information.
Path Planning for Autonomous Bus Driving in Urban Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Urban Environments".</pre>
Path Planning for Autonomous Bus Driving in Highly Constrained Environments - Simulation Results
<pre>This video contains simulation results for the scientific article "Path Planning for Autonomous Bus Driving in Highly Constrained Environments".</pre>
Virtual Path Planning for Image-guided Needle Interventions
ClinicalTrials.gov study NCT02021071. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Accuracy Evaluation of Artificial Intelligence Assisted Liver Tumor Ablation Path Planning
ClinicalTrials.gov study NCT05161624. IPD Sharing: NO. Countries: 1. Publications: 0.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.