Introduction
Evolutionary optimization
Mechanical design
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Questions
Flapping-wing flight in bird-sized UAVs for the Robur project: from an evolutionary optimization to a real flapping-wing mechanism E. de Margerie J.-B. Mouret S. Doncieux J.-A. Meyer T. Ravasi P. Martinelli C. Grand ISIR-Université Paris 6 CRIC (IUT Cachan)
MAV 2007 Jean-Baptiste Mouret
Robur project
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Birds and UAVs Birds are better than current UAVs extremely maneuverable (perching, slow flight, sharp turns) energetically efficient (gliding, fast forward flight)
Part of these capabilities originate from complex wing kinematics Ô closed-loop control of wings Ô no-sinusoidal kinematics Ô many degrees of freedom
Jean-Baptiste Mouret
Robur project
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Robur project Robur project : design and control a bird-sized flapping-wing UAV, from the point of view of bio-inspired artificial intelligence neural-network controllers evolutionary algorithms bio-inspired behaviors (e.g. soaring, optic flow, ...)
Bird-sized (versus insect-sized) : Soaring is possible High-payload (artificial intelligence onboard) Outdoor flight
Institute of Intelligent Systems and Robotics (ISIR, Univ. Paris 6) and IUT Cachan (CRIC)
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Robur artificial bird
Basic features/choices : position-controlled wing-beat mechanism Ô arbitrary movements rigid panel-based wings (easier to simulate and to build) articulated wings (wing folding and twisting) closed-loop control Ô different from most current designs (toys, slow-hawk, ...) In this talk : wing-beat mechanism Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Typical experiment Goal : Closed loop control of forward flight
Tethered flight on a whirling arm Aerodynamic measurements Learning experiments (evolution of neural network controllers) No free flight Ô no weight constraints Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Topic Problem : we want to explore complex flapping-wing kinematics with this experiment but ... How to choose the right motors for the wing-beat mechanism ? allow the right angular ranges ?... flapping frequency ? power ? angular ranges ? wing-span ?
Ô basic kinematics are required to design a wing-beat mechanism Ô a mechanism is required to test efficient kinematics Ô “chicken-and-egg” problem Jean-Baptiste Mouret
Robur project
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Approach 1. Evolutionary optimization in simulation simple kinematics parameters morphologies (wingspan, aspect ratio, ...)
Ô typical flight speed, mechanical power, angle ranges, ... Ô specifications 2. Mechanical design Ô classical engineering 3. (future work) whirling arm experiments Ô evolution of neuro-controllers
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Approach 1. Evolutionary optimization in simulation simple kinematics parameters morphologies (wingspan, aspect ratio, ...)
Ô typical flight speed, mechanical power, angle ranges, ... Ô specifications 2. Mechanical design Ô classical engineering 3. (future work) whirling arm experiments Ô evolution of neuro-controllers
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Approach 1. Evolutionary optimization in simulation simple kinematics parameters morphologies (wingspan, aspect ratio, ...)
Ô typical flight speed, mechanical power, angle ranges, ... Ô specifications 2. Mechanical design Ô classical engineering 3. (future work) whirling arm experiments Ô evolution of neuro-controllers
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Approach 1. Evolutionary optimization in simulation simple kinematics parameters morphologies (wingspan, aspect ratio, ...)
Ô typical flight speed, mechanical power, angle ranges, ... Ô specifications 2. Mechanical design Ô classical engineering 3. (future work) whirling arm experiments Ô evolution of neuro-controllers
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Evolutionary optimization
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Simulated UAV 0.5 kg 2 rigid panels by wing 4 degrees of freedom (DOFs) by wing : dihedral, shoulder twist, wrist twist, wing folding (sweep) simulator : semi-empirical (validation : polars and wind tunnel) airfoil : Selig 4083 control : sinusoidal curves
Jean-Baptiste Mouret
Robur project
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Parameters Optimized parameters wing area (0.1-0.4 m2 ) wing aspect ratio (4.5-10) flapping frequency (1-10 Hz) amplitude of rotation for each DOF offset for each DOF time offset with the dihedral Ô 12 real parameters Ranges chosen according to zoological data corresponding to birds of similar masses.
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Evolutionary algorithm Fitness, two objectives to be optimized simultaneously : flying along the most horizontal path given a target speed mechanical power used (to be minimized)
Multi-objective evolutionary algorithm (ε-MOEA, Deb 2005) This algorithm try to find the set of all optimal trade-offs between objectives at the same time (Pareto-optimal) Ô no weight between objectives 24 evolution runs, for target horizontal speed ranging from 6 to 20 m/s Jean-Baptiste Mouret
Robur project
Introduction
Evolutionary optimization
Mechanical design
Results : videos
6 m/s
12 m/s Jean-Baptiste Mouret
Robur project
Conclusion
Questions
Introduction
Evolutionary optimization
Mechanical design
Results : power
Jean-Baptiste Mouret
Robur project
Conclusion
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Results : wing folding
wing folding was used for all flying speeds medium speed : 25-44% of power decrease high speed : 7-17%
drawing by Karl Herzog
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Results : useful data Typical (most efficient) flying speed : 10-12 m/s Minimum power consumption : 20 W/kg Medium to high speed : 20-60 W/kg Wing folding decreases substantially power consumption Typical flapping frequencies : 3-5 Hz Angles : Speed (m/s) 6-8 10-12 16-20 chosen
Dihedral 15-50 25-45 30-65 ± 50
Should incid. 0-30 0-15 0-5 ± 30
Wrist incid. 10-50 8-15 1-10 -
Ô Specifications for a real flapping mechanism (dihedral and twist) Jean-Baptiste Mouret
Robur project
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Mechanical design
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Overview
To right wing
Conical gears
Minimum energetic consumption for a sinusoidal movement
Pulley-belt components
Other kinematics are possible Dihedral parallel mechanism
Shoulder incidence parallel mechanism To left wing
Symmetrical movements
Patent pending
Jean-Baptiste Mouret
Two rod-crank parallel mechanisms
Robur project
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Overview
To right wing
Conical gears
Pulley-belt components
4 brushless motors (30 W, 100g) 0-5 Hz Dihedral ± 50 deg. Dihedral parallel mechanism
Shoulder incidence parallel mechanism To left wing
Patent pending
Jean-Baptiste Mouret
Robur project
Twist ± 30 deg.
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Kinematic schema L Wing
Wing
ϑ
J1
ϑ
J2
a Motor 1
J4
J3
Motor 2
b
J5 J6
L
Jean-Baptiste Mouret
Robur project
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Simplified schema
L ϑ
A1 a
u1
ϑ
A2 A6
L
A5
γ
a u2 b
b α1
α2 λ
A3
A4
L ϑ
ϑ a
L
γ
b α2
α1 λ
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Variables L ϑ
ϑ a
L
γ
b α2
α1 λ
New input variables α (mean input angle) and ϕ (half-phase angle). α = 12 (α2 + α1 ) α1 = α − ϕ and α2 = α + ϕ ϕ = 12 (α2 − α1 ) L−λ ϑ = f (ϕ, α) = sin−1 2a q λ = L2 − 4b2 cos2 α sin2 ϕ + 2b sin α sin ϕ Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Quasi-sinusoidal motion Evolution of the flapping angle for different phase angles ϕ 0.3
0.2
θ (rad)
0.1
0
-0.1
-0.2 ϕ=.25 rad ϕ=.50 rad ϕ=.75 rad
-0.3 0
1
2
3
4
5
6
α (rad)
Motors at constant speed Ô minimum energy consumption
Ô quasi-sinusoidal motion Jean-Baptiste Mouret
Robur project
α˙ = 2π · fϑ ϕ = sin−1 ( ba sin(ϑmax ))
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Pseudo-periodical motion
To obtain a pseudo-periodical motion : Ô modification of the quasi-sinusoidal motion The motor velocities have to be adapted at each timestep ϕ˙ and α˙ can be computed using the differential kinematic model (cf paper) The more the motion differs from a quasi-sinusoid, the more the power consumption increases
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Pseudo-periodical motion
To obtain a pseudo-periodical motion : Ô modification of the quasi-sinusoidal motion The motor velocities have to be adapted at each timestep ϕ˙ and α˙ can be computed using the differential kinematic model (cf paper) The more the motion differs from a quasi-sinusoid, the more the power consumption increases
Jean-Baptiste Mouret
Robur project
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Jean-Baptiste Mouret
Robur project
Conclusion
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Conclusion
A multi-objective evolutionary algorithm has been used to determine, for a horizontal flight : typical flight speed (10-12 m/s) angle ranges power required to fly (20-50 W/kg)
Simulations show that wing folding substantially decreases the required power (25-44%) These data have been used to design an efficient innovative parallel wing-beat mechanism any kinematic is possible minimum energy consumption = sinusoidal movement
Jean-Baptiste Mouret
Robur project
Introduction
Evolutionary optimization
Mechanical design
Future work
This is only a preliminary work Basic aerodynamic measurements Whirling arm experiments : design of control laws comparison of wing designs evolution of open-loop controllers evolution of neural-network controllers ...
Folding wings
Jean-Baptiste Mouret
Robur project
Conclusion
Questions
Introduction
Evolutionary optimization
Mechanical design
Conclusion
Questions
Contact :
[email protected] This study was funded by a grant from Parinov
Jean-Baptiste Mouret
Robur project
Questions
