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Lakehopper 2 Design

Tilt-rotor active vertical takeoff flying wing

This document is very much still a scratchpad.

In the meantime, here's what Lakehopper 2 will probably look like:

Lakehopper 2 design preview

Goals

Literature

Lakehopper 1

  • Chose conventional monoplane design over flying wing because:
    • Generally higher efficiency than flying wing
      • Counter: not significant enough
    • No tilt rotor necessary
      • Counter: weight and complexity of floats

Design

  • Bended foam board + foamboard ribs
  • Spar: wood
  • Winglets: no
  • Power system:
    • Motor: Propdrive V2 4258 500KV brushless outrunner
    • Prop: TGS Precision Sport 17x10, non-folding
    • ESC: YEP 80A
    • Battery: 6S 4000 mAh LiPo 35C
    • Thurst:

Design of a Passive Vertical Takeoff and Landing Aquatic UAV

2017 Richard-Alexandre Peloquin, Dominik Thibault, Alexis Lussier Desbiens

  • Quebeq, Canada
  • Sherbrooke University Water-Air VEhicle (SUWAVE)
  • Passive tilting
  • 2D model of wing and center body during takeoff to determine angle of thrust with respect to center body along with various other parameters
  • Righting after capsize (center body turns 180 degrees)
  • Impact modelling: nosedive OK

Design

  • Solid foam
  • Carbon spar
  • Winglets: 75deg??, 8cm??
  • Center body: PLA
  • Wings:
    • Airfoil: NACA M3
    • Wingspan: 1000mm
    • Front sweep: 33deg
    • Back sweep: 17deg??
    • Root chord: 320mm (.26)
    • Tip chord: 160mm (.13)
    • Taper ratio: 0.5
  • Elevons:
    • 90% of wing width (up to end of wing)
    • Remaining space (in center): fully filled in with non-moving wing
    • GS-9018 servos
  • Rudder: no
  • Power system:
    • Motor: EMax CF2822 1200KV brushless inrunner
    • Prop: 11x6, non-folding
    • ESC: Turnigy Plush 18A
    • Battery: 3S 1000 mAh LiPo
    • Thrust: 911g, TTW: 1.56
  • Total weight: 584g
  • Learnings:
    • Latch mechanism unreliable in windy conditions

Active Vertical Takeoff of an Aquatic UAV

2020 Étienne Tétreault, David Rancourt, and Alexis Lussier Desbiens

  • Follow-up of SUWAVE
  • Active tilting
  • 3D model
  • XFLR5 analysis

Design

  • Solid foam + fiberglass
  • No carbon spar
  • No winglets
  • No center body (avionics in wing)
  • Wings:
    • Wingspan: 1240mm
    • Front sweep: 15deg??
    • Back sweep: 0deg
    • Mean chord: 250mm
    • Root chord: 280mm?? (.23)
    • Tip chord: 130mm?? (.10)
    • Taper ratio: 0.5
  • Elevons:
    • 50% of wing width (up to end of wing)
    • Remaining space (in center): fully filled in with non-moving wing
    • Servos: ??
  • Rudder:
    • Area: 130 cm^2
    • Length: 13cm??
    • Height: 10cm??
  • Power system:
    • Motor: 300W, other details??
    • Prop: 12x6, folding
    • ESC: 30A
    • Battery: 3C 1000 mAh LiPo
    • Thrust: ??
  • Total weight: 865g
  • Learnings:
    • Gyroscopic effect from propeller causes undesired yaw during takeoff

Development of an aquatic UAV capable of vertical takeoff from water

2019 Leonard Waldau

  • KTH

Model 1: Zagi HP-based

  • See: https://zagi.com/product/hp/
  • Solid foam (EFP) + tape??
  • Wooden spar
  • Winglets: 45 degree, 8cm max??
  • Center body: separate foam block
  • Wings:
    • Airfoil: Zagi 101.4
    • Wingspan: 1500mm (original: 1220mm)
    • Front sweep: 28deg??
    • Back sweep: 10deg??
    • Root chord: 380mm?? (.25)
    • Tip chord: 160mm?? (.11)
    • Taper ratio: 0.42??
  • Elevons:
    • 80% of wing width (up to end of wing)
    • Remaining space (in center): partly filled in with non-moving wing
    • Servos: Hitec HS-5086WP
  • Rudder: no
  • Power system:
    • Motor: Turnigy Aerodrive SK3 3548 1050kv
    • Prop: Mayatech 12x6.5, folding
    • ESC: AlfroESC 30A
    • Battery: 3C 2200 mAh mAh LiPo 30C
    • Thrust: 1.85kg, TTW: 1.42
  • Total weight: 1283g
  • Learnings:
    • Elevons should be in motor wake for control during takeoff
    • Elevons should be larger

Model 2: X8 Skywalker-based

Fury Slope Wing

https://www.rc-factory.eu/letadla/fun-series/rc-factory-fury-slope-wing

  • Very nimble
  • Slope soaring

Design

  • Solid foam?? + tape with contact glue (3M77)
  • Spar: carbon strips (thin beam), follows front sweep (80% of wingspan), with horizontal strip in center (30% of wingspan)
  • Winglets: 30deg, 5cm
  • Center body: no
  • Wings:
    • Wingspan: 1200mm
    • Front sweep: 25deg??
    • Back sweep: 20deg??
    • Root chord: ~336mm (.28)
    • Tip chord: 204mm (.17)
  • Elevons:
    • 65% of wing width (up to end of wing)
    • Remaining space (in center): fully filled in with non-moving wing
    • Servos: MG92B
  • Rudder: no
  • Power system: N/A
  • Total weight: 400-600g

Design & Build of a Flying Wing (With Balsa Wood)

https://www.youtube.com/watch?v=2YOK1p4iCDo

Design

  • Balsa ribs + covering foil
  • Wings:
    • Wingspan: 723mm
    • Airfoil MH60
  • Elevons:
    • 80% of wing width (up to end of wing)
    • Center space: pusher prop
    • Servos: FrSky Xact 5700
  • Power system:
    • Motor: 1506 4000KV
    • Prop: ??
    • ESC: 30A
    • Battery: 3S 500mAh LiPo
  • Total weight: 235g
  • Learnings:
    • CG needed to be more forward than CL
    • Add reflex camber
    • Reverse taper elevons (wider near tips) => reduces wingtip AoA on up-elevon (climb) => reduces tip stalling
    • Use CA flue instead of UHU
    • Build on parchment paper

General

  • EPP foam in front for better crash resistance

Lakehopper 2

Design 1

  • Construction
    • Wings
      • One piece each
      • Hot wire cut
    • Fuselage
      • Hot wire cut
      • PLA printed inserts for motor mount
    • Front spar
      • 500mm
    • Back spar
      • 1000mm
  • Wings
    • Center section: 60mm
    • Wing length: 720mm
    • Wingspan: 1500mm
    • Front sweep: 25deg
    • Back sweep: 16deg
    • Root cord: .24 => 360
    • Tip cord: .12 => 180
    • Calculated:
      • Wing area (simple): (60 * 360) + ((700 * 2) * ((360 + 180) / 2)) = ~225000mm^2 = 39.96dm^2
      • http://rcwingcog.a0001.net/V3_testing/
      • Wing area (a0001 calc V3): 41.04dm^2
      • Chassis CG (50% area): ~285mm
      • Desired CG (10% area): ~195mm

  • Elevons:

  • Power system:
    • Prop:
      • Diameter: 13-17inch
      • Pitch to diameter ratio (d/p): .5 - .75
      • For example:
        • 15.5inch diameter => 7.75 to 11.63inch pitch
    • Battery: 3S 3000mAh LiPo 70C
  • Weight:
    • Front spar: 18g
    • Back spar: 36g
    • Nuts & bolts: 10g
    • Servos & linkages: 84g
    • Foam:
      • Volume: 270 * 1500 * 2 = 810000mm^3
      • Density: 0.5g/cm^3
      • Weight: 810cm^3 * 0.5g/cm^3 = 405g
    • ESC: undecided - 100g
    • FPV: 30g
    • RC Receiver: 5g
    • Telemetry transceiver: 10g
    • Battery: 280g
    • Prop: undecided - 70g
    • Raspberry Pi 4: 50g
    • Camera: 10g
    • Total:
      • 18+36+10+84+405+100+30+5+10+280+70+50+10
      • Without motor: 1108g
      • With 400g motor: 1508g
  • Thrust to weight ratio:
    • Goal: 1.50 with motor
    • Required thrust: 1962g (2kg)

Lithium polymer is by far the most common battery chemistry with RC enthusiasts. LiPo batteries are more power dense than those with an older nickel metal hydride (NiMH) or nickel–cadmium (NiCad) chemistry. Even more power dense than LiPo batteries are lithium-ion batteries. However, LiPo batteries are generally more leak-resistant and rigid than their lithium-ion counterpart. These two properties make them safe for RC aircraft, which need to endure hard landings and intensive use. Given these considerations, and the precedent set by the previously considered models, Lakehopper 2 will use a LiPo battery.

TODO: Cell count

Electric motors for RC aircraft are usually specified in terms of the motor constant Kv as well as the maximum safe current. The motor constant Kv specifies the rotational speed of the motor in revolutions per second per volt when not under load. Given equal voltage, the higher the Kv rating, the faster the motor will spin.

The maximum current specification needs to be respected in order to prevent the motor from overheating. Larger motors with thicker winding wires have a higher maximum current because they do not heat up as much as smaller motors with thin wires. Putting a large propeller on such a small motor and driving it with a high voltage could cause the wires to melt or the ESC to fail. A less severe consequence of over-driving a motor is that its efficiency decreases.

Because the load on the motor will be highest during vertical takeoff, the propeller should perform at a high efficiency during this phase. If the plane were not required to be able to take off vertically it would be more important to optimize efficiency during level flight. As with all design considerations though, a balance should be struck.

At low airspeeds, the propellers actual pitch (a.k.a. effective pitch) is lower than its geometric pitch. The plane would need to be moving at a speed of GeometricPitch x RPM for the effective pitch to equal the geometric pitch. This difference is referred to as pitch 'slip'. A high pitch slip lowers the propeller's efficiency.

As discussed before, efficiency during takeoff (low airspeed) is important. Therefore the propellers pitch to diameter ratio should be relatively low (nearing 1/2 vs 1/1).

Design 2 - Power system A

  • Prop: 15.5x9.5 folding https://hobbyking.com/en_us/folding-propeller-w-hub-55mm-5mm-shaft-15-5x9-5-1pc.html
  • Battery: 4S 3300mAh LiPo
  • Motor:
    • To achieve a pitch speed of around ~70km/h => 330..420rpm/V
    • => min. 250W motor (min. 17A at nominal 14.8V of 4S battery)
    • => min. 25A ESC (17A + buffer of ~1.5)
    • Candidate:
      • PROPDRIVE v2 5060
      • 380KV
      • max 90A
      • 438g
  • Weight:
    • 1108g + 438g = 1546g
  • Thrust
    • Required for 1.5 T/W: 2319g
    • Max: 2364g / 1.5 T/W (87.8% efficient)
    • ~80% throttle: 1580g / 1.0 T/W (85.8% efficient)

Design 2 - Power system B

  • Prop: 12x7 folding https://hobbyking.com/en_us/folding-propeller-w-alloy-hub-45mm-4mm-shaft-12x7-1pc.html
  • Battery: 4S 3300mAh LiPo
  • Motor:
    • To achieve a pitch speed of around ~70km/h => 640..940rpm/V
    • => min. 350W motor (min. 24A at nominal 14.8V of 4S battery)
    • => min. 35A ESC (24A + buffer of ~1.5)
    • Candidate:
      • SK3 4240
      • 740KV
      • max 59A
      • 195g
  • Weight:
    • 1108g + 195g = 1303g
  • Thrust
    • Required for 1.5 T/W: 1955g
    • Max: 2770g / 2.1 T/W (90.1% efficient)
    • ~80% throttle: 1930g / 1.5 T/W (89.5% efficient)

Design 2 - Power system C

  • Prop: 12x7 folding https://hobbyking.com/en_us/folding-propeller-w-alloy-hub-45mm-4mm-shaft-12x7-1pc.html
  • Battery: 4S 3300mAh LiPo
  • Motor:
    • To achieve a pitch speed of around ~70km/h => 640..940rpm/V
    • => min. 350W motor (min. 24A at nominal 14.8V of 4S battery)
    • => min. 35A ESC (24A + buffer of ~1.5)
    • Candidate:
      • PROPDRIVE v2 4238
      • 750KV
      • max 55A
      • 156g
  • Weight:
    • 1108g + 156g = 1264g
  • Thrust
    • Required for 1.5 T/W: 1896g
    • Max: 2618g / 2.0 T/W (84.1% efficient)
    • ~80% throttle: 1830g / 1.4 T/W (83.9% efficient)

Design 2 - Power system D

  • Prop: 12x7 folding https://hobbyking.com/en_us/folding-propeller-w-alloy-hub-45mm-4mm-shaft-12x7-1pc.html
  • Battery: 4S 3300mAh LiPo
  • Motor:
    • To achieve a pitch speed of around ~70km/h => 640..940rpm/V
    • => min. 350W motor (min. 24A at nominal 14.8V of 4S battery)
    • => min. 35A ESC (24A + buffer of ~1.5)
    • Candidate:
      • PROPDRIVE v2 3536
      • 910KV
      • max 38A
      • 88g
  • Weight:
    • 1108g + 88g = 1196g
  • Thrust
    • Required for 1.5 T/W: 1794g
    • Max: 2902g / 2.4 T/W (77.6% efficient)
    • ~80% throttle: 2123g / 1.8 T/W (79.5% efficient)

Wing shape:

Washout: tip angle of incidence lower to slow tip stall Wingtips:

  • reduce wingtip vorteces (increases efficiency, slows tip stall)
  • increases yaw stability

Reflex: increases pitch stability (acts as tail in traditional plane) https://www.youtube.com/watch?v=gkb11eKXM14

Swept wing:

  • increase pitch stability
  • increase elevon authority
  • move chassis CG and CP further apart (increases stability)

Airfoil:

MH60

vs

S5010

Choice: S5010
Build-in reflex => need less elevon reflex

Washout:

3 degrees

Wingtips:

Create vortexes

Semseg

Video processing

Shot boundary detection

Use average of chi-square distance between 16x16 grids histograms. 'Keyframe' is middle frame of shot (between two boundaries).

Only keyframes are used in semseg dataset.

Used by:

  • S. et al., 2019
    • Average distance threshold: 0.2 (experimentally)

Labeling tools

LabelMe

Used by:

  • S. et al., 2019
  • Wu et al., 2019

GOFAI

Random forest

Used by:

  • Bhatnagar et al., 2020
    • number of trees: 100 (1000 samples with repetition)
    • total number of splits: 5853
    • Also tried with textural properties (contrast, correlation, energy, homogeneity, mean, variance, entropy, range, skewness, kurtosis)
    • Pixel accuracy (a.k.a. OA/Overall accuracy): 83.3% (w/o textural properties), 85.1% (w/ textural properties)

CNN

FCN

Fully Convolutional Neural Net

Used by:

  • Long et al., 2015 (original)
  • S. et al., 2019
    • FCN32
    • Backbone: VGG16
    • Directly from dense representation frame to output layer (no stepwise decoder)
    • 80, 10, 10 split
    • Transfer learning: no
    • Augmentation: no
    • Batch size: 10
    • Epochs: 100
    • Pixel accuracy: 89.7%
  • Wu et al., 2019
  • Bhatnagar et al., 2020 (bog Ireland)
    • Backbone: ResNet-50
    • Transfer learning: yes (imagenet)
    • Augmentation: no
    • Batch size: 64
    • Epochs: 100 (saturated after 35)
    • Pixel accuracy: 89.9%

FPN

Feature Pyramid Network

U-Net

Todo:

  • First U-Net??: Ronneberger et al., 2015: U-net: Convolutional networks for biomedical image segmentation
  • Daudt et al., 2018: Fully convolutional siamese networks for change detection
  • Daudt et al., 2018: Urban change detection for multispectral earth observation using convolutional neural networks
  • Celik, 2009: Unsupervised change detection in satellite images using principal component analysis and k-means clustering

Used by:

  • S. et al., 2019: Semantic segmentation of UAV aerial videos using convolutional neural networks
    • RGB
    • Original resolution: 1280x720
    • Scaled to 256x256
    • 80, 10, 10 split
    • Transfer learning: no
    • Augmentation: no
    • Batch size: 10
    • Epochs: 100
    • Pixel accuracy: 87.31%
    • Has trouble with dark/light areas (maybe use brightness augmentations to fix?)
    • Architecture
      • Encoder:
        • 4 Stages
          • Conv + ReLu activation, doubling features
          • Conv + ReLu activation
          • Transition:
            • Max pooling 2x2 kernel downscale (relevant feature selection)
      • Encoded frame (dense representation):
        • Features: 64*(2**4) = 1024
        • Resolution: 256/(2**4) = 16
      • Decoder:
        • 5 Stages
          • Conv + ReLu activation, halving features
          • Conv + ReLu activation
          • Transition:
            • Upsampling + conv
            • Combine with corresponding encoder frame
  • Wu et al., 2019
  • Bhatnagar et al., 2020
    • As opposed to GOFAI ML: does not require color correction or the addition of extra textural features
    • Backbone: ResNet-50
    • Transfer learning: yes (imagenet)
    • Augmentation: no
    • Batch size: 64
    • Epochs: 100 (saturated after 35)
    • Pixel accuracy: 91.5%

Evaluation

  • MIoU
    • Mean Intersection over Union
    • Used by
      • S. et al., 2019
  • PA
    • Pixel Accuracy
    • Used by
      • S. et al., 2019
  • F1-score
    • Mean Intersection over Union
    • Used by
      • S. et al., 2019

Datasets

Todo:

  • Bonetto et al.: Privacy in mini-drone based video surveillance
  • Lyu et al.: UAVid: A semantic segmentation dataset for UAV imagery

  • Robicquet et al.: Learning Social Etiquette: Human Trajectory Understanding in Crowded Scenes
    • Campus crossings and paths (social interactions)
    • Pictures from quadcopter
    • ~30m 15deg
    • Frames: 100+ unique (a lot more per location)
    • Resolution: 1400x1904
    • Classes: 6: road, roundabout, sidewalk, grass, building, bike rack
    • Background class: no
  • S. et al., 2019: Semantic segmentation of UAV aerial videos using convolutional neural networks
    • Suburb/campus asphalt roads with parking spaces (Manipal, India)
    • Videos from DJI quadcopter
    • ~25m 25deg
    • Frames: 2494
    • Resolution: 1280x720
    • Classes: 2: greenery, road
    • Background class: no
  • Wu et al., 2019
    • Polsar (not RGB) => not usable
  • Bhatnagar et al., 2020
    • Of bog in Ireland => not applicable
  • UAVID:
    • Oblique => not applicable
  • Drone deploy
  • UVid-Net:

    • Should cite: S. Girisha, M. M. M. Pai, U. Verma and R. M. Pai, "Performance Analysis of Semantic Segmentation Algorithms for Finely Annotated New UAV Aerial Video Dataset (ManipalUAVid)," in IEEE Access, vol. 7, pp. 136239-136253, 2019. doi: 10.1109/ACCESS.2019.2941026

      Girisha S, U. Verma, M. Pai and R. M. Pai, "UVid-Net: Enhanced Semantic Segmentation of UAV Aerial Videos by Embedding Temporal Information," in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, doi: 10.1109/JSTARS.2021.3069909.

    • Verdict: email sent, didn't answer

Aggregated dataset:

  • Dronedeploy
    • chips: 10325
  • iv-ortho-mid
    • 233n
      • chips: 512
    • 218z
      • chips: 1600
  • Aggregated
    • chips: 12.437

TODO: Areas of improvement:

  • Make semi-supervised system (show 10*10 patch => ask "water or ground" => change mask transparency)