Introduction Evolutionary optimization Mechanical design Conclusion 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 ➔ closed-loop control of wings ➔ no-sinusoidal kinematics ➔ many degrees of freedom Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions 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
Introduction Evolutionary optimization Mechanical design Conclusion Questions Robur artificial bird Basic features/choices : position-controlled wing-beat mechanism ➔ arbitrary movements rigid panel-based wings (easier to simulate and to build) articulated wings (wing folding and twisting) closed-loop control ➔ different from most current designs (toys, slow-hawk, ...) In this talk : wing-beat mechanism Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions 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 ➔ no weight constraints Jean-Baptiste Mouret Robur project
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 ? ➔ basic kinematics are required to design a wing-beat mechanism ➔ a mechanism is required to test efficient kinematics ➔ “chicken-and-egg” problem Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Approach 1. Evolutionary optimization in simulation simple kinematics parameters morphologies (wingspan, aspect ratio, ...) ➔ typical flight speed, mechanical power, angle ranges, ... ➔ specifications 2. Mechanical design ➔ classical engineering 3. (future work) whirling arm experiments ➔ evolution of neuro-controllers Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Approach 1. Evolutionary optimization in simulation simple kinematics parameters morphologies (wingspan, aspect ratio, ...) ➔ typical flight speed, mechanical power, angle ranges, ... ➔ specifications 2. Mechanical design ➔ classical engineering 3. (future work) whirling arm experiments ➔ evolution of neuro-controllers Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Approach 1. Evolutionary optimization in simulation simple kinematics parameters morphologies (wingspan, aspect ratio, ...) ➔ typical flight speed, mechanical power, angle ranges, ... ➔ specifications 2. Mechanical design ➔ classical engineering 3. (future work) whirling arm experiments ➔ evolution of neuro-controllers Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Approach 1. Evolutionary optimization in simulation simple kinematics parameters morphologies (wingspan, aspect ratio, ...) ➔ typical flight speed, mechanical power, angle ranges, ... ➔ specifications 2. Mechanical design ➔ classical engineering 3. (future work) whirling arm experiments ➔ evolution of neuro-controllers Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Evolutionary optimization Jean-Baptiste Mouret Robur project
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 Questions Parameters Optimized parameters wing area (0.1-0.4 m 2 ) 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 ➔ 12 real parameters Ranges chosen according to zoological data corresponding to birds of similar masses. Jean-Baptiste Mouret Robur project
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) ➔ 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 Conclusion Questions Results : videos 6 m/s 12 m/s Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Results : power Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions 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
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) Dihedral Should incid. Wrist incid. 6-8 15-50 0-30 10-50 10-12 25-45 0-15 8-15 16-20 30-65 0-5 1-10 chosen ± 50 ± 30 - ➔ Specifications for a real flapping mechanism (dihedral and twist) Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Mechanical design Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Overview Minimum energetic Pulley-belt components consumption for a Conical gears To right wing sinusoidal movement Other kinematics are possible Two rod-crank Shoulder incidence parallel mechanisms Dihedral parallel parallel mechanism mechanism To left wing Symmetrical movements Patent pending Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Overview Pulley-belt components Conical gears To right wing 4 brushless motors (30 W, 100g) 0-5 Hz Dihedral ± 50 deg. Twist ± 30 deg. Shoulder incidence Dihedral parallel parallel mechanism mechanism To left wing Patent pending Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Kinematic schema L Wing Wing ϑ ϑ J 1 J 2 a Motor 1 J 4 J 3 Motor 2 J 5 b J 6 L Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Simplified schema L ϑ ϑ A 1 A 2 A 6 a L A 5 γ u 1 a u 2 b b α 2 α 1 λ A 3 A 4 L ϑ ϑ L γ a b α 2 α 1 λ Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Variables L ϑ ϑ L γ a b α 1 α 2 λ New input variables α (mean input angle) and ϕ (half-phase angle). � α = 1 � α 1 = α − ϕ 2 ( α 2 + α 1 ) and ϕ = 1 2 ( α 2 − α 1 ) α 2 = α + ϕ f ( ϕ, α ) = sin − 1 L − λ = ϑ 2 a � L 2 − 4 b 2 cos 2 α sin 2 ϕ + 2 b sin α sin ϕ = λ Jean-Baptiste Mouret Robur project
Introduction Evolutionary optimization Mechanical design Conclusion Questions Quasi-sinusoidal motion Evolution of the flapping angle for different phase angles ϕ 0.3 0.2 0.1 θ (rad) 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 � ˙ α = 2 π · f ϑ ➔ minimum energy consumption ϕ = sin − 1 ( a b sin ( ϑ max )) ➔ quasi-sinusoidal motion Jean-Baptiste Mouret Robur project
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