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PercEvite SIGNED

PercEvite - Sense and avoid technology for small drones

Total Cost €

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EC-Contrib. €

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Partnership

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 PercEvite project word cloud

Explore the words cloud of the PercEvite project. It provides you a very rough idea of what is the project "PercEvite" about.

signals    learning    communication    tiny    exchange    expensive    energy    motion    angular    world    autonomous    successfully    waypoints    probability    avoiding    collision    station    sees    background    solution    vision    benefit    distances    performances    retrieve    active    speed    expertise    wireless    audio    adsb    scenario    suite    simulations    perception    hardware    obstacle    commercially    appearance    interoperable    extensive    breakthrough    obstacles    ranging    light    academic    sensors    payload    self    supervised    camera    tests    rely    platform    small    weight    sonar    instance    maximizes    cameras    performed    emits    collaborative    air    plusmn    detecting    capacity    stereo    fusion    flight    position    microphones    drone    detection    space    close    operation    efficient    estimate    multiple    objects    angle    lightweight    5g    vehicles    flying    industrial    estimations    autonomously    board    aircraft    wifi    avoidance    fused    ground    lasers    mentioned    drones    sensor    densely    realize    types    combining   

Project "PercEvite" data sheet

The following table provides information about the project.

Coordinator
TECHNISCHE UNIVERSITEIT DELFT 

Organization address
address: STEVINWEG 1
city: DELFT
postcode: 2628 CN
website: www.tudelft.nl

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Netherlands [NL]
 Project website http://www.percevite.org/
 Total cost 899˙007 €
 EC max contribution 899˙007 € (100%)
 Programme 1. H2020-EU.3.4.7. (SESAR JU)
 Code Call H2020-SESAR-2016-1
 Funding Scheme SESAR-RIA
 Starting year 2017
 Duration (year-month-day) from 2017-09-01   to  2020-08-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNISCHE UNIVERSITEIT DELFT NL (DELFT) coordinator 304˙888.00
2    KATHOLIEKE UNIVERSITEIT LEUVEN BE (LEUVEN) participant 246˙912.00
3    AEROVINCI BV NL (Delft) participant 175˙500.00
4    PARROT DRONES FR (PARIS) participant 171˙706.00

Map

 Project objective

We will develop a sensor, communication, and processing suite for small drones for autonomously detecting and avoiding “ground-based” obstacles and flying objects. To avoid ground-based obstacles, we aim for a lightweight, energy-efficient sensor and processing package that maximizes payload capacity. Self-supervised learning will allow for a breakthrough in perception range. This will enable effective fusion of stereo vision, motion, appearance, ranging and audio information. Our learning process will allow obstacle detection as far as the camera ‘sees’, rather than the current ± 30 m. For close distances, our solution does without energy expensive active sensors such as lasers or sonar. For collaborative avoidance between drones and other air vehicles, we achieve an interoperable solution by combining multiple communication hardware types (ADSB, 4/5G, WiFi) to exchange information on position, speed, and future waypoints. This will enable drones to successfully avoid other flying vehicles even in a very densely used air space. The probability for a collision in a collaborative scenario will be in the order of 10-9. For non-collaborative avoidance, we rely on sensors and even the communication hardware mentioned above. If a non-collaborative aircraft emits communication signals, for instance to a ground station, this hardware allows to retrieve angular measurements. These measurements can be fused with detection and angle estimations performed with multiple tiny microphones and cameras on board of the detecting drone. We estimate the collision probability in a non-collaborative scenario as 10-6. These performances will be assessed by simulations and extensive real-world tests. The consortium will benefit from the partners’ academic and industrial background with expertise in autonomous flight of very light-weight drones, robust wireless communication, drone design, production, and operation to realize a commercially viable platform.

 Deliverables

List of deliverables.
Report tests WP4.2-4.3 Documents, reports 2020-02-12 18:04:02
Project Management Plan Documents, reports 2019-04-15 03:11:29
Multi-technology communication Other 2019-03-20 17:27:39

Take a look to the deliverables list in detail:  detailed list of PercEvite deliverables.

 Publications

year authors and title journal last update
List of publications.
2018 Martins, D., Van Hecke, K., & De Croon, G.
Fusion of stereo and still monocular depth estimates in a self-supervised learning context
published pages: 849-856, ISSN: , DOI:
IEEE International Conference on Robotics and Automation (ICRA) 2019-10-29
2019 Evgenii Vinogradov, Sofie Pollin
Wireless Communications with Unmanned Aerial Vehicles
published pages: , ISSN: , DOI: 10.13140/rg.2.2.15185.38249
The 53rd IEEE International Conference on Communications (ICC) 2019-10-29
2018 Evgenii Vinogradov, Hazem Sallouha, Sibren De Bast, Mohammad Mahdi Azari, Sofie Pollin
Tutorial on UAVs: A Blue Sky View onWireless Communication
published pages: 395-468, ISSN: 1550-4646, DOI: 10.13052/jmm1550-4646.1443
Journal of Mobile Multimedia 14/4 2019-10-10

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The information about "PERCEVITE" are provided by the European Opendata Portal: CORDIS opendata.

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