Explore the words cloud of the PercEvite project. It provides you a very rough idea of what is the project "PercEvite" about.
The following table provides information about the project.
TECHNISCHE UNIVERSITEIT DELFT
|Coordinator Country||Netherlands [NL]|
|Total cost||899˙007 €|
|EC max contribution||899˙007 € (100%)|
1. H2020-EU.3.4.7. (SESAR JU)
|Duration (year-month-day)||from 2017-09-01 to 2020-08-31|
Take a look of project's partnership.
|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|
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.
|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.
|year||authors and title||journal||last update|
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|
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|
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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