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PercEvite - Sense and avoid technology for small drones

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.

Field of science

  • /engineering and technology/electrical engineering, electronic engineering, information engineering/electronic engineering/robotics/autonomous robots
  • /natural sciences/computer and information sciences/data science/data processing
  • /natural sciences/computer and information sciences/artificial intelligence/machine learning

Call for proposal

H2020-SESAR-2016-1
See other projects for this call

Funding Scheme

SESAR-RIA - Research and Innovation action

Coordinator

TECHNISCHE UNIVERSITEIT DELFT
Address
Stevinweg 1
2628 CN Delft
Netherlands
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 304 888,75

Participants (3)

KATHOLIEKE UNIVERSITEIT LEUVEN
Belgium
EU contribution
€ 246 912,50
Address
Oude Markt 13
3000 Leuven
Activity type
Higher or Secondary Education Establishments
AEROVINCI BV

Participation ended

Netherlands
EU contribution
€ 175 500
Address
Mijnbouwstraat 120
2628 RX Delft
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)
PARROT DRONES
France
EU contribution
€ 171 706,25
Address
174-178 Quai De Jemmapes
75010 Paris
Activity type
Private for-profit entities (excluding Higher or Secondary Education Establishments)