The “Explo-Drone” project: toward a one-liter MAV for ... · The \Explo-Drone" project: toward...
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The “Explo-Drone” project: toward aone-liter MAV for exploring confined
environments
C. Chauffaut, D. GomezInstitut Superieur de l’Aeronautique et de l’Espace (ISAE-SUPAERO), Universite de Toulouse,
31055 TOULOUSE Cedex 4
Garden Workshop 2015 - July 2nd - Toulouse
Garden Workshop 2015
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Explo-Drone project
Joint research project between ISAE and ONERA
As part of the MAV Research Center
Objectif :Developing a compact micro air vehicle capable of autonomouslyflying through highly confined environments.
Garden Workshop 2015
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Work packages
Automatics
Work package 1 : Guidance and control through vision
Aerodynamics
Work package 2 : Innovative blade design for compact,long-endurance micro air vehicles
Work package 3 : Aerodynamic interaction of micro rotorsin confined environments
Work package 4 : The concept of “aerodynamic covertness”for indoor MAV exploration
Garden Workshop 2015
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Prototype
Project goal : Design of a compact (1L) ducted coaxial rotorMAV with a low aerodynamic footprint
VisionAir by ISAE Gimball by Flyability(EPFL Spin-off)
Garden Workshop 2015
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Work package 1
Project constraints:
Low power embedded system
Low sensor payload
Proposed approach:
Onboard reactive obstacle avoidance
Offboard localization at a low frequency
Garden Workshop 2015
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Work package 1
Project constraints:
Low power embedded system
Low sensor payload
Proposed approach:
Onboard reactive obstacle avoidance
Offboard localization at a low frequency
Garden Workshop 2015
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Development framework
Garden Workshop 2015
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First approach for obstacles avoidance
Extend an existing optical flow algorithm design for low computingpower embedded system:
State estimation and data fusion
Global motion estimation
Position and noise prediction
Exploration
Tracked points
Search rectangles
Local feature extraction
Extracted features
Feature matching and ranking
Matches
Active zones
Optical flow prediction
New points
Optical flow measure
Garden Workshop 2015
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Limitations of this approach onembedded processor
Following 60 features per frame of 320x240
Feature detector Harris Min-Eigen FAST
I7 PC (ms) 3.2 2.7 0.41
Gumstix (ms) 405 504 4.3
We need more features to detect obstacles.
Introduction of features descriptors for nearby features matching:
Feature descriptor SURF SIFT
I7 PC (ms) 12.4 7.2
⇒ new sensor needed
Garden Workshop 2015
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Limitations of this approach onembedded processor
Following 60 features per frame of 320x240
Feature detector Harris Min-Eigen FAST
I7 PC (ms) 3.2 2.7 0.41
Gumstix (ms) 405 504 4.3
We need more features to detect obstacles.
Introduction of features descriptors for nearby features matching:
Feature descriptor SURF SIFT
I7 PC (ms) 12.4 7.2
⇒ new sensor needed
Garden Workshop 2015
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Comparison of environment sensors
Laser
scanner
Asus Xtion
(structured light)
Kinect V2
(Time-of-Fligh)Stereo vision Mono vision
Range (m) 0.1-40 0.3-4 0.5-4 0.2-5Movement
needed
On Sensor
depth
computation
Yes Yes No No No
Weight High Medium High Medium Low
In the dark Yes Yes Yes Not directly Not directly
Textures needed No No No Yes Yes
Connectivity USB2 USB2 USB3 2 USB2 USB2
Field of viewVery
LargeMedium Medium Medium Medium
Planar Yes No No No No
Resolution Very High High High Low/High Low/High
Cost High Low Low Low Low
Garden Workshop 2015
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Comparison of environment sensors
Laser
scanner
Asus Xtion
(structured light)
Kinect V2
(Time-of-Fligh)Stereo vision Mono vision
Range (m) 0.1-40 0.3-4 0.5-4 0.2-5Movement
needed
On Sensor
depth
computation
Yes Yes No No No
Weight High Medium High Medium Low
In the dark Yes Yes Yes Not directly Not directly
Textures needed No No No Yes Yes
Connectivity USB2 USB2 USB3 2 USB2 USB2
Field of viewVery
LargeMedium Medium Medium Medium
Planar Yes No No No No
Resolution Very High High High Low/High Low/High
Cost High Low Low Low Low
Garden Workshop 2015
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New approach: Xtion depth sensor
Default results
Garden Workshop 2015
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New approach: Xtion depth sensor
with an IR filter
Garden Workshop 2015
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New approach: Xtion depth sensor
with fisheye lenses
Garden Workshop 2015
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New approach: Xtion depth sensor
with fisheye lenses
Garden Workshop 2015
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Future work
New approach with an Asus Xtion
Development of a reactive control law
Integrate the aerodynamic disturbances model into the controllaw
Garden Workshop 2015
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Aerodynamics
Compact and efficient Low aerodynamic footprint Robust to wall proximity
Architecture : counter-rotating rotors
Issue : commercial rotors not appropriate for hovering flight
Solution : optimize rotor blade twist and chord laws using aerodynamic theories, taking into account upper-to-lower rotor interactions
Preliminary results : 13 % gain in aerodynamic efficiency (Power Loading) on a single rotor with respect to the best commercial rotor for a 200 gr equivalent Thrust.
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Aerodynamics
Compact and efficient Low aerodynamic footprint Robust to wall proximity
Issue : wall proximity affects aerodynamics
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CONFINEMENT CASES (Multiple Wall)
Channel Effect (IChE)
Two-Wall Duct Effect (ITwDE)
Square Duct Effect (ISDE)
H/R = 2
H/R = 3
H/R = 4
H/R = 5 d/R = 3
d/R = 4
H/R = 2 – W/R = 4
H/R = 4 – W/R = 4
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Compact and efficient Low aerodynamic footprint Robust to wall proximity Issue : wall proximity affects aerodynamics
Results : Modelling of aerodynamic coefficients with wall distance Flying near ground enhances aerodynamic efficiency
Aerodynamics
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Results : Modelling of aerodynamic coefficients with wall distance Flying near ceiling enhances aerodynamic efficiency
Aerodynamics
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Issue : wall proximity affects aerodynamics
Results : Channel effects result from the linear combination of ground and ceiling effects
Aerodynamics
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Issue : wall proximity affects aerodynamics
Results : Channel effects result from the linear combination of ground and ceiling effects
Aerodynamics
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Issue : wall proximity causes « brown-out » phenomenon
Aerodynamics
• Archaeology
• Civil Rescue
• Confined environment
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Issue : wall proximity causes « brown-out » phenomenon
Solution : shrouded rotors
Aerodynamics
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Issue : wall proximity causes « brown-out » phenomenon
Solution : shrouded rotors
+ Inherent safety
Aerodynamics
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Solution : shrouded rotors
+ Inherent safety + Contributes to thrust
Aerodynamics
T rotor
T shroud T shroud
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Solution : shrouded rotors
+ Inherent safety + Contributes to thrust • Promote rotor jet
divergence
Aerodynamics
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Energy Dissipation
Aerodynamics
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Aerodynamics
Shroud Nozzle Geometry
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Compact and efficient Low aerodynamic footprint Robust to wall proximity
Aerodynamics
Shroud Nozzle Geometry Some Results
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Future Work
Experimental Numerical
Rotor-Shroud Ground effect measurements : • Force and Moments • Ground and Shroud Pressure
Measurements • LDV measurements of the wake
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Future Work
Experimental Numerical
Rotor-Shroud Ground effect Simulations • RANS (VBM) • URANS (Rotating blade)
Simulation of Vortex Generators in the Shroud Wall
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THANK YOU
ISAE: • Jean-Marc MOSCHETTA • Sébastien PROTHIN • Thierry JARDIN • Dominique BERNARD • François DEFAŸ ONERA: • Laurent BURLION • Henry de PLINVAL
Acknowledgments