tuhh im nächsten jahrtausend · pdf filerobotic and medicine robot-, hand-eye- relation...

Prof. Dr.-Ing. habil. Hermann Lödding

Prof. Dr.-Ing. Wolfgang Hintze

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PD Dr.-Ing. habil. Jörg Wollnack

MMC.1 04.05.2015

Lecture Topics

1. Introduction

2. Sensor Guides Robots / Machines

3. Motivation Model Calibration

4. 3D Video Metric (Geometrical Camera Model)

5. Grey Level Picture Processing for Position Measurement

6. Light and Perception as well as Black-and-White- and Colour

Pictures

7. Model Calibration

8. Video Metric Sensor Calibration (Geometrical Camera Model)

9. Video Metric Camera Model (Fourier 2D)



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MMC.2 04.05.2015

Motivation

Model Calibration



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MMC.3 04.05.2015

Video der Firma Volvo



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MMC.4 04.05.2015

Applications

Transportation- and Flight Systems

R ö n tg e n q u e lle

R ö n tg e n g e rä t

sc h e m a tisc h

S tra h le n k e g e l

B ild p la t te

Z e n tra ls tra h l

Measuring Techniques,

Robotic and Medicine

Robot-, Hand-Eye-

Relation und

Work Piece Frame

Kuka Ahlers

Tool Machine

Work Piece Frame

SHW



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MMC.5 04.05.2015

Accuracy Behavior of the Robot before and

after the Model Calibration

Calibrated Model

Non calibrated

Model

/ 100 µm

/ 1 mm

RMS x / m y / m

x / m y / m



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MMC.6 04.05.2015

Positionsfehler

-5,00E-04

-4,00E-04

-3,00E-04

-2,00E-04

-1,00E-04

0,00E+00

1,00E-04

2,00E-04

3,00E-04

4,00E-04

5,00E-04

1 3 5 7 9

11

13

15

17

19

21

23

25

27

29

31

33

35

37

39

Pose-Nr.

[m]

y

Accuracy behavior of the Sensor and

Robot Model Calibration in x/y Direction

Position Error



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MMC.7 04.05.2015

Research Center CFK-Nord (Stade)



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MMC.8 04.05.2015

Load change

Aging and Attrition

as well as

Changed Environmental Condition

Operating

point change

Time Variance of the System Properties

Kuka

Boeing



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MMC.9 04.05.2015

Adaptive Production Technology

Pose Error

Assembly

(5 – 10 mm)

Pose and Shape

Error Linear Axis

at TCP

(5 – 20 mm)

Pose Error

Robot

(1 – 5 mm)

Process Pose

Error at TCP

(0,5 – 1 mm)

Process Force

influenced

Pose Error

(0,5 – 1 mm)

Accuracy

(0,1mm auf

10 to 20 m

-> 5µm/m)



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MMC.10 04.05.2015

Assembly Shape and Pose Correction I

• Flexible controlled Shape

• Non expensive Gripper

• 6D Controlled Forces

• Multi dimensional elasticity model

• Shape and Pose accuracy converge after

2 – 4 Iterations to machine repeatability

(50 to 100 µm)



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MMC.11 04.05.2015

Assembly Shape and Pose Correction II

Multi-Frames with separate and

cooperating Movements

(Adaptive hierarchical control process)



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MMC.12 04.05.2015

Absolute PositionierAbsolute Positionier--

fehlerfehler der Großder Groß--

bauteilebauteile

0,05 bis 0,1 mm0,05 bis 0,1 mm

(nach typisch 2-4 Iterationen)

Preiswerte Greifer,Preiswerte Greifer,

StandardStandard--IndustrieIndustrie--

roboterroboter und preisund preis--

werte Hallenwerte Hallen--

fundamentefundamente

Fotos:

Roman

Jupitz

Experimental Field for Wide Body Shape and

Pose Automatic Control Loop

Inexpensive

gripper,

robots and

hall foundations

Absolute

positioning errors

0,05 – 0,1 mm (after typically 2-4

iterations)



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MMC.13 04.05.2015

Absolute PositionierfehlerAbsolute Positionierfehler

der Großbauteile der Großbauteile

0,1 bis 0,2 mm0,1 bis 0,2 mm

(nach typisch 2-4 Iterationen)

Preiswerte Greifer, StandardPreiswerte Greifer, Standard--

Industrieroboter und preiswerte Industrieroboter und preiswerte

HallenfundamenteHallenfundamente

Fotos: Roman Jupitz Symposium 2004Symposium 2004

Wide Body handling with Cooperating Robots

Inexpensive gripper,

robots and

hall foundations

Absolute positioning

errors 0,1 – 0,2 mm (after typically 2-4 iterations)



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MMC.14 04.05.2015

Laser Scanner Remote Welding

Laser-Scanner

Laser-Scanner Mirror Movements

are to be derived from Constructional Data (No Teaching!)

TRUMPF



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MMC.15 04.05.2015

Sensor guided Remote Laser Welding

Emmelmann



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MMC.16 04.05.2015

Fixateur Extern, Robotic und Picture Processing

R ö n tg e n q u e lle

R ö n tg e n g e rä t

sc h e m a tisc h

S tra h le n k e g e l

B ild p la t te

Z e n tra ls tra h l

a b

Medicinal 3D Picture Processing

Fixateur Extern /

Robotic

3D x-Ray Technology

K u g e lg e le n k

S te l lsc h ra u b e

D is tr a k to r I l is a ro w -R in g

Video Metric to Measure Pose of

x-Ray Source and Picture Plane



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MMC.17 04.05.2015

Machine Off Line Programming

Separation from plant enterprise and programming

B lu m e

Absolute pose errors require time consuming teaching



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MMC.18 04.05.2015

System Approach in Mechanical Engineering

Computer

Aided Manufacturing

Computer

Aided Design

Off-Line-

Programmig

Sensor-TCP Calibration Body

Palette System

with Index

Tool

Gripper

Option

Machines x

yz

w

Sensor



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MMC.19 04.05.2015

Examples

System Identification



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MMC.20 04.05.2015

System Identification at the Ohm's Resistance

u

i

Structure

Identification ?

Parameter Identification ?

u R i

Ru

i

Measure Voltage u and Current i ,

than Calculate Parameter R !

x

p

yy = f (x ,p )

P a ra m e te r

S te u e r-

w e rte

A u s g a n g s -

w e rte

concentrated

Element

Input

Values

Parameter

Output

Values



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MMC.21 04.05.2015

, {1, , }i i

i I y A p B x

1 1i I i I y y y A p B x x x

Y A p B X

A p Y B X

1

, R an k D im

p A Y B X A p

System Identification of Linear Vector Maps

x

p

yy = f (x ,p )

P a ra m e te r

S te u e r-

w e rte

A u s g a n g s -

w e rte

Input

Values

Parameter

Output

Values



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MMC.22 04.05.2015

System Identification with Linearization I

( , ) , {1, , }i i

i I y f p x

0 0( , ) ,

i i

py J p x p R 0 R 0

0 0 0 0, ( , ) ,

i i i i i y y y y f p x p p p with

Linearization

Vector Map

, {1, , }i i

i I y A p

, {1, , }i i

i Ix y

Measured Data



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MMC.23 04.05.2015

0 1

0

0

( , )

( , )

( , )

i

I

p

p

p

J p x

Α J p x

J p x

1

' , R an k D im ,

p A y A p with

t

t t t

1'

i Iy y y y

and

System Identification with Linearization II



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MMC.24 04.05.2015

The system identification appears

to be very simple.

What do you mean, are this is in

principle like that?

Why not?

System Identification I

x

yz

w

Kuka



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MMC.25 04.05.2015

• Structure of the model admits not from the beginning

• Spatially distributed non linear systems

• Spatially concentrated non linear systems

• Spatially distributed or concentrated features of the

systems not sufficiently known

System Identification II

• Input and output values must be measurable.

Therefore principle sensors are needed.



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MMC.26 04.05.2015

Potentials of the System Identification

• Static and dynamic parametric models

Control, Regulation as well as Sensor Models

• Accuracy increase of the stationary and dynamic behavior by

model calibration

(Positioning and Path error, Automatic Controller Optimization,

Adaptive Automatic Controllers etc..)

• Calibration, Quality Assurance, Error-, Aging- as well as Attrition-

Recognition (In the manufacturing process and at the customer in the

service)

• Linear Independence Parameters makes Hierarchical as well as

Sub Set Parameter Calibrations possible

(Pre- and Re-Calibration at the manufacturer and customer)



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MMC.27 04.05.2015

Linear approximation in one and

multidimensional case

Base for Iterative

Optimization Procedures

(Regula falsi

Newton Method)

Base for Computation

of the

inverse Mapping

Base for Iterative

Identification

Algorithms

Analysis

Analysis

Base for Error

Propagation Analyses



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MMC.28 04.05.2015

Learning

by

Use and Repetition



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MMC.29 04.05.2015

Hand Eye Coordination



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MMC.30 04.05.2015

W a h rn e h m u n g

M o d e llb ild u n g

P la n u n g

A k tio n

M o to r is c h e

S te u e ru n g

Long Time Memory and

neuronal Catenations needs

Repetitions

Lerning and Remembering

Perception

Modeling

Scheduling

Action

Neuromuscular

Movement



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MMC.31 04.05.2015

Memory Quota

2 0 %

3 0 %

5 0 %

7 0 %

9 0 %

H ö re n

S e h e n

H ö re n u n d S e h e n

H ö re n , S e h e n u n d D is k u tie re n

H ö re n , S e h e n , D is k u tie re n u n d Tu n

H a n d b u c h H o c h s c h u l le h re

Hearing

Seeing

Hearing and seeing

Hearing, seeing and discuss

Hearing, seeing, discuss and doing



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MMC.32 04.05.2015

Matlab-Class-Room-License to Practicing



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MMC.33 04.05.2015

Hand Eye Calibration

Movement

Measure

Iterative Model Calibration



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MMC.34 04.05.2015

System Analysis

and

Calibration



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MMC.35 04.05.2015

Annahmen zurVereinfachung

VereinfachungAufstellen derGleichungen

TheoretischesModell

- Struktur- Parameter

Vereinfachtes theoretisches

Modell

A-priori-Kenntnisüber das System

Struktur unbe-bekannt kannt

Experiment

Identifikation

parame-tr isch

nicht parame-trisch

parame-trisch

nicht parame-trisch

Experim. ModellVergleich

TheoretischeAnalyse

ExperimentelleAnalyse

Qualität

neinja

Ende

Methodology Systems Analysis

Theoretical

Analysis

Experimental

Analysis

Assumption

Simplification

Set up the

Equations

Theoretical

Model Structure/Param.

Simplification

Experiment

A priori Knowledge

Structure known / unknown

Identification

Experiment

Model

compare

Q

Simplified Theoretical

Model

no yes Q := Quality

see robotic I



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MMC.36 04.05.2015

Model Quality and Process Knowledge

M := Model Quality

K := Process Knowledge Isermann

ME := Mechanical Electrical

Systems

ET := Energy-Technical

Systems

VT := Process Engineering

systems

Theoretical

Model Design Identification

Attainable

Model

Quality

Knowledge of

Internal Processes

see robotic I



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MMC.37 04.05.2015

Isermann

Model Quality and Investment

Expenditure

(Costs)

Expenditure

(Costs)

Attainable

Model

Quality

see robotic I



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MMC.38 04.05.2015

Problem Definition Robot I

Decrease of the manufacturing tolerances of the

construction components technically and economically

limited.

Repetition Accuracy

50 - 100 µm

Absolute PositioningAaccuracy

2 - 10 mm

Robot frame is indicated approximately or not

Angle error of 0.1° of 1 m length results in a arc length error of 1.7 mm

zero errors of the sensors and

arc errors between world and robot frame

=> problem:

see robotic I



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MMC.39 04.05.2015

x

z

x

z

o h n e e rw e ite r te R e g e lu n g

S o llb a h n

Is tb a h n

0 ,5 m0

,5m

0 ,10 ,8 0 ,9

t/s

0 ,5 m /s

x

z

e rw e ite r te r R e g le r

Weber

Problem Definition Robot II (Path Error)

Without extended regulation Extended regulation

Destination Path

Actual Path

see robotic I



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MMC.41 04.05.2015

0 5 5 4 3 2 1 1 2 3 4 5 5, 6

I I I I I I i I I I I I II

r D D R R R R r t t t t t t

Kinematics Transformation II

3 3 3 3

1 1 1 1 1, w ith , ,

x

i i i i i i i r R r t R t r

Coordinates Transformation (classical)

Si

Si+ 1

i- te s G e le n k

SR

Ti

SG

S0

W K S

R o b o te r -

K S

T C P

( + 1 )- te s

G e le n k

i

i- te s

G e le n k

ti

x i+ 1

y i+ 1z

i+ 1

x i

y i z

i

p := Parameter Vector

x := Ideal Machine Coordinates

i-th Joint

i-th

Joint

(i+1)-th

Joint World-FR

Robot-

FR

see robotic I



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MMC.42 04.05.2015

( , ) ( , )j j j

j

x T T p f p x

Transformation T with an open Kinematics Chain

Products of the homogeneous

Matrices Tj

Kinematics Transformation IV

Advantages of

Homogeneous Coordinates

see robotic I



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MMC.43 04.05.2015

Kinematics Transformation V

p := constant joint parameter of the cinematic model

x := ideal variable drive coordinates also

ideal machine coordinates called

xS := variable sensor coordinates of the internal

position measuring systems and/or real variable

machine coordinates

see robotic I



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MMC.45 04.05.2015

T f p , x m xk k kS

'

S

'

S0d i d ie j

Kinematics Transformation with Sensor Coordinates

Sensor zero position errors and sensor

gradient errors can be interpreted as

parameters of the model.

f m x p x f p x' ' '( , , , ) ( , )

S

'

S

'

S S0

FH

IKf p x p p

' ' '( , ) ,

S S

't

S

't

t

t

m xk kd i d i0

see robotic I



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MMC.46 04.05.2015

ST C P

TT C P

A

SA

T C P

rT i

di( )p

T C P

A

A

rB i

Wikipedia

A T C P T C P

B A TE

( )i i i

d r T p r

D 0i i i id d d D

{1, .. . , 6}i

Parallel Kinematic I

Analytic Inverse Kinematic Transform

see robotic I



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MMC.47 04.05.2015

T C P

A 0

D

0

:= N u ll P o se

:= - th D r iv e D is ta n c e

:= - th C a rd a n Jo in t D is ta n c e

:= - th O f f se t in N u ll P o se (S e n so r O f f se t)

:= - th D is ta n c e P a r t

i

i

i

i

d i

d i

d i

D i

p

Parallel Kinematic II

see robotic I



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MMC.48 04.05.2015

Causes for Absolute Positioning Errors

x

yz

w

1. Kinematics Parameters: (zero position, alignment and length)

2. Relation from Machine FR

to World FR

3. Variations

Temperature

4. Joint

Elasticity

5. Element

Elasticity 6. Transmission Error (Linearity, Backlash … Error)

7. Bearing

Backlash

8. Position

Sensor

9. Friction

10. Controller: (rounding and numeric

procedural errors)

Kuka

FR := Frame

= coordinate system

see robotic I



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MMC.49 04.05.2015

J u d d ‘8 7 , B e rg ‘9 0 ,

M e y e r ‘9 1 , T ra d t ‘9 1

T h e o d o lite n -

M e ß v e r fa h r e n T ra d t ‘9 1 , B e y e r ‘9 5 ,

G e u e n s ‘9 7

V id e o m e tr is c h e

V e r fa h re n

T C P - F la n s c h

L E D -

P a n e l

C C D -S e n s o r-

s y s te m

W a ts o n ‘8 5 , L a u ‘8 5 ,

M a y e r ‘8 6 , P re n n in g e r ‘9 2

In te r fe ro m e tr is c h e

L a s e r -T ra c k in g -V e r fa h re n

M e ß k o p f-V e r fa h re n

P o s e - u n d P o s it io n s -

s e n s o re n

in d u k tiv , ta s te n d :

u ltra s c h a ll:

v id e o m e tr is c h :

W a rn e c k e ‘8 6 , S c h ie le ‘8 7 ,

G o s s e l ‘9 6

S to n e ‘8 6 , B e rg ‘9 0

D a in is ‘8 5 , Z h u a n g ‘9 6 ,

R o o s ‘9 6 , W o lln a c k ‘9 6

K a lib r ie rk ö rp e r

Theodolites

Laser-Tracking

Measuring Head

(TCP-Sensor)

Calibration body

TCP Flange CCD/CMOS 3D Sensor Videometric

Inductively, tactile:

Ultrasonic:

Videometric:

External 3D Position Sensors

see robotic I



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MMC.50 04.05.2015

SM

S

TM

T

T

MesswerteMesswerte ModellwerteModellwerte

Measured Values Model Values

Homogeneous Error Transformation I

1 MΔT T T

see robotic I



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MMC.51 04.05.2015

2 t

E E EM in = M in ,p p p

M 1

E( ) ( ) ( )

T T p T p T p I

M

E p p p

Principal ambiguities of the pose and periodicity

ambiguities are not present here (See inverse RPY-Transform).

The minimization of the pose instead of the

homogeneous matrix T generates the orthogonal

rotation matrix correctly.

Homogeneous Error Transformation II

These approach is very important for pose calibration.

with

see robotic I

tuhh im nächsten jahrtausend · pdf filerobotic and medicine robot-, hand-eye- relation...

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