Studio III booklet

2019/2020

research stages Jun Woo Lee Andreea Bunica Abdullah Sheikh Abdelrahman Koura

index

1 2 3 4 5

GRIPPERS TABLE SET-UPS ALUMINIUM & SPRINGBACK SYSTEM CONSTRAINTS 4.1 PHASE 1 4.2 PHASE 2

COMPUTER VISION

5.1 EDGE DETECTION 5.2 GH CURVE FITTING 5.3 SKELETONIZATION

6 7

SENSING

6.1 PRESSURE SENSOR 6.2 HAND TRACKING 6.3 VISION COMPARING

AUTO ROBOT SIMULATION 7.1 CURVE OPTIMIZATION

1

grippers

END EFFECTOR OLD

old end effector tool

Bending tool V2

Pneumatic piston Pressure sensor

Pneumatic gripper (Fixed on the table)

Aluminium strip 5XXX series Aluminium

Bending tool V2

30

Aluminium STRIP

0

70

X mm

TOP VIEW

90 mm

30

END EFFECTOR 60

Slotted rack

1.

5

Aluminium 700 x 30 x 1.5 mm

CURRENT

m 0m

30

400

Gripper 15 x 10 mm

mm

m

HORIZONTAL SETUP

Xm

60

90 mm

700

mm

Guide O 20 mm

1.2 mm ~ 1.5mm Empty = 1.2 ~1.5 x 30 mm

FRONT VIEW

RIGHT VIEW

AL HEIGHT 30 mm displacement

60

30

TOP VIEW

Aluminium bending process

a new 3D printed end effector tool400

300

700 mm

FRONTAL VIEW

2

table set-ups

TABLE SET-UP OLD

old table set-up

TABLE SET-UP CURRENT

current table set-up

3

aluminium and springback

Aluminium 1050 Aluminium 1050 is a popular grade of aluminium for general sheet metal work where moderate strength is required. Alloy 1050 is known for its excellent corrosion resistance, high ductility and highly reflective finish.

Aluminium: 99.5% min; Copper: 0.05 % max; Iron: 0.4% max; Magnesium: 0.05%max; Manganese: 0.05%: Silicon: 0.25% max; Titanium: 0.03% max; Vanadium: 0.05%max; Zinc: 0.05% max.

types of bends

4

system constraints

4.1

phase 1

Incremental angle: The constraints of the kinematics of the rotation limits of the six axis are considered at each control point.

The achievement of a best fit curve is now rationalised through compound curvature.

Control Spacing: Resolution of geometry control points to effectively replicate the craft geometry while incorporating efficient kinematics for production.

This is also affected by single or double end effector configurations.

Directional bending (single end effector) /Tension: Directionality and constant and reliable material feed into the end effector is key and requires a measured level of tension that would be improved with force feedback.

4.2

phase 2

5

computer vision

On our side, we take the video from the craftsman and we process it in order to extract robot paths.

First, we use mask rcnn to only segment the metal strip out of the video, then we convert all masked pixels to white and the background to black.t

edge detection processed real time

5.1

edge detection

we convert these images to curves and then we optimize the angles to fit our current hardware system constraints.

Point to curve

5.2

gh curve-fiting

Getting bitmaps out that we then input into the skeletonization script. In grasshopper, we convert these images to curves and then we optimize the angles to fit our current hardware system constraints.

processed data skeleton

5.3

skeletonization

6

sensing

For sensing, we’re using the aforementioned pressure sensor, connected via firefly and splitting the incoming values both from the craftsman and from the robot into low, medium and high ranges. The point of this is to be able to calibrate the robotic path by remapping the amount of angle exaggerations needed to achieve accurate results depending on how much pressure was detected in the original video.

Workflow from the hand to reach and categorize the data in grasshopper

6.1

pressure sensor

Hand tracking, in the form of following the very small finger wrappers on the screen, with the goal of incorporating, at a later stage of the project better accuracy in translating the movement from humans to rigid end effectors.

traking the small finger wrappers on the screen

6.2

hand traking

Side by side: craftsman unique piece with 6 axis fabricated piece with the same parameters and values

6.3

vision comparing

To wrap up the technological part, our three main components- conversion from input video, pressure sensing and hand tracking all collaborate towards the same goal- that of mapping the human gesture and sensibility to the fingers of our end effector. The current hardware prototype we are using does have 4 fingers--2 pneumatically activated, with a very rough on off function and two rounded, for bending. Our goal at this stage is to use the pressure sensor for remapping intensity and the colour tracking to better control which one of the two sets of fingers on the robot is being used.

7

auto robot simulation

.

°

16

.75

.10°

162

°

65

0.

166.50°

162 .10 ° 16 6

17

162.05°

160 °

2,010 mm deviation

5°

6.7

161.43°

16

.5 0

0° 8 .5

160°

0°

162.16°

16

°

2 16

5 CONTROL POINT

°

.50

157

° 00

14

3,724 mm deviation

4 CONTROL POINT

0°

°

14

00

5,813 mm deviation

1,098 mm deviation

0,988 mm deviation

a

b

5,420 mm deviation

3,264 mm deviation

1,7633 mm deviation

1,256 mm deviation

3,702 mm deviation

1,450 mm deviation

0,976 mm deviation

100.64°

7.624 mm deviation

.3 8° 90

ORTHOGONAL SHAPE

° .50

5.

13

ROUNDED IRREGULAR SHAPE

a

90°

7.1

3 CONTROL POINT

8

a

IRREGULAR SHAPE

The system, in its current form, does come with a bunch of geometrical constraints that we took to catalogue via using evolutionary iterations, in order to convert and approximate the input curves extracted from the video to rebuilt poly lines that abide by our systems constraints

2 CONTROL POINT

157

90° ROUNDED SHAPE

1 CONTROL POINT

3.833 mm deviation

curve cataloge

curve optimization

4,560 mm deviation

80° 6°

.5

82

° 00

.

80

3.165mm deviation

70° °

.50

70

60°

°

0 0.0

7

2.692 mm deviation

° .00

60

0 mm deviation

50° 3°

52.

° .00

50

4,692 mm deviation

1,135 mm deviation

40° 5°

50.2

0°

40.0

91,8044 mm deviation

2019/2020 @IAAC, Barcelona