64-424 intelligent robotics - uni-hamburg.de

University of Hamburg Department of Informatics

64-424 Intelligent Robotics

64-424Intelligent Robotics

http://tams.informatik.uni-hamburg.de/

lectures/2014ws/vorlesung/ir

Jianwei Zhang / Eugen Richter

University of HamburgFaculty of Mathematics, Informatics and Natural SciencesDepartment of InformaticsTechnical Aspects of Multimodal Systems

Winter 2014/2015

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http://tams.informatik.uni-hamburg.de/lectures/2014ws/vorlesung/ir




64-424 Lecture

Lecture Monday 14:15 - 15:45Room F-334Web http://tams.informatik.uni-hamburg.de/lectures/

2014ws/vorlesung/ir

Organizers Prof. Dr. Jianwei Zhang / Eugen RichterO�ce F-308 / F-225Phone +49 40 42883-2430 / -2356E-mail {zhang, erichter}@informatik.uni-hamburg.deSecretariat Tatjana TetsisO�ce F-311Phone +49 40 42883-2430E-mail [email protected]

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64-425 Seminar

Seminar Monday 16:15 - 17:45 UhrRoom F-334Web http://tams.informatik.uni-hamburg.de/lectures/

2014ws/seminar/ir

Organizer Benjamin AdlerO�ce F-307Phone +49 40 42883-2565E-mail [email protected]

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http://tams.informatik.uni-hamburg.de/lectures/2014ws/seminar/ir

http://tams.informatik.uni-hamburg.de/lectures/2014ws/seminar/ir



Outline1. Introduction2. Fundamentals

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1 Introduction 64-424 Intelligent Robotics

Outline1. Introduction

Motivation2. Fundamentals

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1.1 Introduction - Motivation 64-424 Intelligent Robotics

Intelligent robots?

A general definition is hard to establishI There is no general answerI Intelligence often equaled to autonomy

Depends on the tasks executed by the robotI Sensor data acquisition and processingI Fusion and interpretation of sensor dataI LocalizationI Path planningI Motion / ManipulationI Human-machine interactionI . . .

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An interdisciplinary field

The field of robotics combines many disciplinesI MechatronicsI Architecture- and system designI Control theoryI Image processingI Speech processingI Neural networksI Artificial intelligenceI . . .

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Example: Personal Robot 2 (PR2)

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Hardware platformI Mobile base

I 4-wheel omnidirectional driveI Telescoping spineI Fixed laser range findersI ⇡ 2 hours runtime

I Two compliant armsI 4 DOF armsI 3 DOF wristsI 1.8 kg payloadI Passive counterbalanceI Gripper camera

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Sensor headI 2 DOF (pan & tilt)I 5 MP RGB cameraI Kinect RGB-D camera (not shown)I Environment stereo cameraI Manipulation stereo cameraI LED texture projectorI Inertial measurement unitI Tilting laser range finder

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Two grippersI 1 degree of freedomI 90mm range of motionI 3-axis accelerometersI Fingertip pressure sensor arraysI Gripper cameras in forearm

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Two on-board computersI Dual Quad-Core i7 XeonI 24 GB main memoryI 1.5 TB removable hard drivesI Multi-gigabit interconnectionsI Synchronized network camerasI EtherCAT communication busI 1 kHz motor control loop

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Other robot examples

Very di�erent platformsI Service robots (TASER)I Humanoid robots (HOAP-2)I Mobile platforms (Pioneer)I Modular robots (GZ-1)I Cleaning robots (SkyCleaner)I Educational robots (NXT,...)) System architectures) Mechanics) Sensors, Actuators) . . .

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What is the purpose of this lecture?

This lecture will cover topics such asI Fundamental sensor/actuator technology

I e.g. Rotation, motion, grasping, . . .I Established data processing/fusion algorithms

I e.g. State estimation, image processing, control, . . .I Application examples

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2 Fundamentals 64-424 Intelligent Robotics

Outline1. Introduction2. Fundamentals

IntroductionSensor data acquisitionSensor characteristicsLiterature

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2.1 Fundamentals - Introduction 64-424 Intelligent Robotics

Sensor applications in robotics

I Integration of sensors continues to gain importance in thedevelopment of autonomous and intelligent robotic systems

I Throughout the development process theperception-action-cycle is of primary importance

I The perception-action-cycle governs the sensing of theenvironment through the sensors and the transition to theadaptive manipulation of the environment as a result of anaction

I In case of interactive use of robotic systems a situation-basedalteration of the sequence of actions is particularly important

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Perception-Action-Cycle

Environment

Sensor data preprocessing

Sensor data fusion

Feature extraction

Pattern recognition

Environment modeling

Perception-Action-Cycle

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Perception-Action-Cycle (cont.)

1. Data acquisition: The sensors convert the stimuli and outputan analog or digital signal

2. Data preprocessing: Acquired data is filtered, normalizedand/or scaled, etc.

3. Data fusion: Redundant and multi-dimensional sensor data iscombined/fused in order to obtain more robust measurementresults

4. Feature extraction: Extraction of features representing amathematical model of the sensed environment in order toapproximate the natural human perception

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Perception-Action-Cycle (cont.)

5. Pattern recognition: Extracted features are searched forpatterns in order to classify the data

6. Environment modeling: Successfully classified patterns areused to model the environment of the robotic system

. . . . . .n. Manipulation: Based on the model of the environment sets of

goal-oriented actions are executed manipulating theenvironment (using robotic arms, grippers, wheels, etc.)

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Sensor examples

I Intrinsic sensors:Incremental encoder, angle encoder, tachometer, gyroscope, . . .

I Extrinsic sensors (force/pressure):Strain gauge, force-torque sensor, piezoelectric sensor (crystal andceramic), . . .

I Extrinsic sensors (distance):Sonar sensor, infrared sensor, laser range finder, . . .

I Visual sensors:Linear camera, CCD-/CMOS-camera, stereo vision system,omnidirectional vision system, . . .

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A simple example

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What is a sensor?

A sensor consists of two parts:I The water level indicatorI The human eye

) Perception of the level indicatorresults in a signal to the brain

DefinitionA sensor is a unit, whichI receives a signal or stimulusI and reacts to it

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2.1.1 Fundamentals - Introduction - Natural and physical sensors 64-424 Intelligent Robotics

Natural and physical sensors

Natural sensors:I A reaction is an electrochemical signal on neural pathwaysI Examples: Auditory sense, visual sense, tactile sense, . . .

Physical sensors:

DefinitionA physical sensor is a unit, whichI receives a signal or stimulusI and reacts to it with an electrical signal

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Input signal

I A sensor converts a (generally) non-electrical signal into anelectrical one

I This signal is referred to as a stimulus

DefinitionA stimulus is aI quantity,I characteristic orI state,

which is perceived and converted into an electrical signal

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Output signal

I The output signal can beI a voltage,I a current orI a charge

I Furthermore, there’s the option to distinguish byI amplitude,I frequency orI phase

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2.1.2 Fundamentals - Introduction - Sensor types 64-424 Intelligent Robotics

Sensor types

I Intrinsic:Information about the internal system state

I Extrinsic:Information about the system environment

I Active:Modify applied electrical signal on alterationof the stimulus

I Passive:Create an electrical signal directly on alterationof the stimulus

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2.1.2 Fundamentals - Introduction - Sensor types 64-424 Intelligent Robotics

Sensor types:1.: extrinsic, passive 2. und 3.: intrinsic, passive4.: intrinsic, active 5.: intrinsic (in data acquisition), passive

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2.1.3 Fundamentals - Introduction - Sensor classification 64-424 Intelligent Robotics

Sensor classification

Classification of sensors by:I Type of stimulusI Characteristics, specification and parametersI Type of stimulus detectionI Conversion of stimulus to output signalI Sensor materialI Field of application

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2.1.3 Fundamentals - Introduction - Sensor classification 64-424 Intelligent Robotics

Classification example

SENSORS

INTRINSIC EXTRINSIC

Encoder

Tachometer

Gyroscope CONTACT NON-CONTACT

BumperForce-Torque

Microphone

Laser range finder Camera

Infrared

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2.2 Fundamentals - Sensor data acquisition 64-424 Intelligent Robotics

Outline2. Fundamentals

IntroductionSensor data acquisitionSensor characteristicsLiterature

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2.2 Fundamentals - Sensor data acquisition 64-424 Intelligent Robotics

Measurement with sensors

I Important scientific criterion: ReproducibilityI Scientific statements have to be comparableI Statements must be quantitative and based on measurementsI Measurement result consists of:

I Numerical valueI Measuring unit

I Additionally: Declaration of measurement accuracy

Measuring errorsNo measuring process yields a flawless and entirely accurate result!

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2.2.1 Fundamentals - Sensor data acquisition - Measurement deviation 64-424 Intelligent Robotics

Measurement deviation (Measuring error)

Systematic deviation ("systematic error"):I Deviation is caused by the sensorI For example: wrong calibration, persistent sources of

interference like friction, etc.I Elimination only possible through elaborate examination of the

error source

Random deviation ("random or statistical error"):I Deviation is caused by inevitable, disorderly interferenceI Repeated measurements yield di�erent resultsI Individual results fluctuate around a mean value

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2.2.2 Fundamentals - Sensor data acquisition - Error declaration 64-424 Intelligent Robotics

Error declaration

I Measurement is always a�icted with uncertainty

I Example: Distance measurementI Distance to an object is measured repeatedly

Individual measurement results:4, 40 m 4, 40 m 4, 38 m 4, 41 m 4, 42 m4, 39 m 4, 40 m 4, 39 m 4, 40 m 4, 41 m

I Individual measurement results vary

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Histogram

Measurements can be illustrated in a histogram:

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Mean value

The mean value x̄ of the individual measurements xi

is determinedas follows:

x̄ =1N

NX

i=1x

i

The mean value is also called arithmetic average or best estimatefor the true value µ

Note: µ is the mean or expected value of the population (the set of all possible

measurement values), frequently called "true" value x

w

of the measured

parameter X : E(X) = µ = x

w

. We assume that the measured parameter X is a

(normally distributed) stochastic variable.

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Absolute and relative measurement error

Deviation is specified in two di�erent fashions:I Absolute measurement deviation ("Absolute error"):

The absolute error �xi

of a single measurement xi

equals thedeviation from the mean value x̄ of all N measurements{x

n

|n 2 {1 . . .N}}I Uses the same unit as the measured valueI �x

i

= |xi

� x̄ |

I Relative measurement deviation ("Relative error"):The relative error �x

rel

is the ratio between absolute error andmeasured value �x

i

x

i

I Has no dimension, usually specified as a percentage (%)I �x

i

rel= �x

i

x

i

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Variance of a measurement series

The distribution of the single measurement values xi

around thearithmetical mean x̄ may also be represented by the variance(variance of a measurement series):

s2 = (�x)2 =1

N � 1

NX

i=1(�x

i

)2

=1

N � 1

NX

i=1(x

i

� x̄)2

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Standard deviation of a measurement series

The positive square root of the variance is the called the standarddeviation (standard deviation of a measurement series):

s =

vuut 1N � 1

NX

i=1(x

i

� x̄)2

The standard deviation is also known as the average or mean errorof a single measurement

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Standard deviation of the mean

I For N ! 1 a discrete distribution of a measurement seriestransitions into a continuous distribution

I The measurements of a physical/technical quantity X areusually assumed to be normally distributed

I N ! 1: x̄ ! µ and s ! �

DefinitionNormalized density function (Gaussian distribution)

f (x) = 1�p

2⇡e�

(x�µ)22�2

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Standard deviation of the mean (cont.)

201510x

50

0.4

0.3

0.2

0.1

0.0

f(x)

= 4σ

= 10µ

= 10µ

= 10

=2σ

= 1σ

µ

f (x) = 1�p

2⇡e�

(x�µ)22�2

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Standard deviation of the mean (cont.)

The standard deviation of the mean value (also known as error ofthe mean value) is calculated as follows:

sx̄

=

vuut 1N(N � 1)

NX

i=1(x

i

� x̄)2

=�xp

N=

spN

sx̄

is the distribution of the mean values from individualmeasurement series x̄ around the "true" mean value µ

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Result of a measurement

DefinitionAs the result of a measurement we get:

x = (x̄ ± sx̄

) [Unit]

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2.2.3 Fundamentals - Sensor data acquisition - Confidence limit 64-424 Intelligent Robotics

Confidence interval

I Interval around a determined mean value of a measurementseries that is said to contain the true mean value with a givenprobability

I A confidence interval of ± sx̄

is said to contain the true meanvalue with a probability of 68 %

I For a certainty of 95 % the interval must be extended to ± 2 · sx̄

I A certainty of 99 % requires an interval of ± 3 · sx̄

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2.2.4 Fundamentals - Sensor data acquisition - Error propagation 64-424 Intelligent Robotics

Error propagation

I A measurement uncertainty must be specified for ameasurement value calculated from several measurementparameters

I If the measurement value z is defined as

z = f (x1, . . . , xn

)

and �x̄i

the measurement uncertainty (maximum error) ofeach individual measurement parameter, the measurementuncertainty �z̄ of the calculated value is

�z̄ =

��@f@x1

�� ·�x̄1 + . . . +

��@f@x

n

�� ·�x̄n

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Error propagation (cont.)

I The partial derivatives are weight factors for the propagation ofindividual errors

I Weight factors should generally be calculated prior to themeasurement

I This is the only way to detect the errors with the largestimpact on the combined result

I Corresponding measurement values have to be determinedparticularly accurate

I The measurement result of an indirectly determinedmeasurement parameter reads as follows:

z = z̄ ±�z̄

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Error propagation (cont.)

Two rules of thumb:I Summation und subtraction accumulate the absolute errorsI Multiplication and division accumulate the relative errors

I Squaring doubles, extracting the square root halves the relativeerror

I The di�erence of two parameters with nearly equal valuesresults in a big relative error ) better: measuring di�erencedirectly

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64-424 intelligent robotics - uni-hamburg.de

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