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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.

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

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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