robotics & ai control lab kunsan national university control & instrumentation eng. present...

Robotics & AI Control LABKunsan National University Control & Instrumentation Eng.

Present Status and Future Trend

of

Autonomous Mobile Robot

http://mouse.kunsan.ac.kr/~RAIC

Presented byYoung Hoon Joo


Robot 의 개요 및 정의

ROBOT(manipulator)

다양한 작업을 수행하기 위해 여러 가지 프로그램된 동작으로 재료 , 부품 등을이동 및 조립하기 위해 설계된 다기능 조작기 단순히 , 인간 팔의 작업 기능을 모방한 로봇으로 고정된 장소에서 작업을 수행

MOBILE ROBOT

인간의 개입 없이 실시간으로 이동하여 유용한 작업을 수행할 수 있는 자동화 장치 즉 , 로봇에 더욱 다양한 기능을 부여하기 위해 기존의 로봇에 인간과 같은 이동 기능을 부여한 로봇

Legged Mobile Robot : 여러 개의 관절로 이루어진 다리를 이동기구로 한다 .

Wheeled Mobile Robot : 로봇에 장착된 바퀴를 지표면에 접촉하여 구동시킴으로써 지표면을 단독으로 이동가능 ( 산업용으로 AGV 가 실용화 )


Description of Position and Orientation

X

Y

ZY

Y

Y

Z

Z

Z

X

X

X

-. 로봇 공학에서는 3 차원 공간상의 물체를 다룸 - 링크 , 부품 , 공구 등

-. 물체를 position 과 orientation 으로 표시하고 이것을 수학적으로 다루는 방법을 공부

-. Position 과 Orientation 을 표시하기 위해 물체에 좌표계 (frame) 를 부착

-. 한 계에서 다른 계로의 변환을 공부


-. Kinematics : 운동을 야기시키는 힘을 고려하지 않고 운동을 취급

-. Dynamics : 운동을 일으키는데 필요한 힘을 공부

-. Forward Kinematics : 한 조의 관절각이 주어졌을 때 base frame 에 대한 tool frame 의 position 과 orientation 을 계산하는 것

Y

1

Z

BASE

TOOL

X

Y Z

2

3

X

1

BASE

TOOL

Y

Z

Z

X

Y

2

3

-. Inverse Kinematics : 말단효과 장치의 position 과 orientation 이 주어졌을 때 그 주어진 position 과 orientation 을 얻기 위해 사용될 수 있는 모든 가능한 관절각을 구하는 것


Types

Features

Mobility

Applications

1st Generation( 1960 ~1970 )

2nd Generation( 1980’ )

3rd Generation( 1990’ )

Playback robot

Link and camteaching/playback

ConventionalMobile systems

Paint sprayingSpot welding

Pick and place

PerceptionProblem-Solving

Feedback sensors(visual, tactile

untrasound)

Omni-directionalmobile

Arc weldingAssemblingMedical/NursingRemote operation

LearningInterface

LearningDecision making

Walking

Automated assemblingIndependent operationAdvanced InspectionDomestic


ROBOT

-. 1-Axis Manipulator






-. Redundant Manipulator

Robot Manipulator

In recent years the area of robotics applicationhas been expanded due to the addition of mobility

Mobile Robot

Legged Mobile Robot

Wheeled Mobile Robot

-. Hopping type

-. Human type

-. Horse type

-. Insect type

-. Steering & Driving type

-. Driving & Driving type

-. Hybrid type

-. Stanford Wheel type

-. Mechanam Wheel type

-. Ball Wheel type


Robot Manipulator

2-Axis

5-Axis 6-Axis

4-Axis


Legged Mobile Robot

Hopping Type(1-Legged) Human Type(2-Legged)

Insect Type(6-Legged)Horse Type(4-Legged)


-. Steering & Driving Type

-. Hybrid Type I -. Hybrid Type II

-. Driving & Driving Type

Wheeled Mobile Robot


Application Example of Mobile Robot


RESEARCH CHALLENGES OF MOBILE ROBOT

Hardware Aspect

Software Aspect

Autonomous Navigation

Position Estimation

Vehicle body

Controller

Actuator

Sensors

Map & Path-Planning

Operating System


MechanicalPart

ApplicationPart

ControlPart

SensingPart

ManipulationPart

MOBILE ROBOT SYSTEM

Block Diagram of Mobile Robot System

Application Area of Mobile Robot System

-. Automated Guided Vehicle(AGV)

-. Document Delivery Robot in Office Building

-. Nursing Robot in Hospital

-. Guide Robot for Handicapped people

-. Serving Robot

-. Wafer Handling Robot in Semiconductor Factory


DESIGN EXAMPLE

Construction of Wheels

World CoordinateSystem

X

Y

+

( x , y , )k

HeadingAngle

k

k k

YL

XL

Reference Point

Local CoordinateSystem

Arrangement of Sonar Sensors

S[0]S[1]

S[2]S[3] S[4]S[5]

S[6]S[7]

S[8]

S[17]S[16]

S[15]S[14]S[13]

S[12]S[11]

S[10]S[9]


Hardware Block Diagram

MIAN CONTROLLER(IBM PC)

VehicleControlModule

ManipulatorControlModule

SensorAcquisition

Module

ImageProcessing

Module

RFModemModule

VoiceSynthesize

Module

Standard BusStandard Bus

EDC

Motor EDC

Motor

Sonar Sensors

Infrared Sensors

Servo Controller

CCDCamera

Speaker Servo Controller

Antenna


Assembly of Mobile Robot


Specification of Mobile Robot

The Specifications Of CCD Camera

Item Specification

Pixel

Cell Size

Item Specification

Resolu.

Size

756 X 493

11 X 13 m

570 X 485 lines

44X29X107mm3

[SONY XC-77]

Item Specification

Weight

Size

Power

Item Specification

Velocity

Accele.

Motor

150 Kg

70X70X140Cm

24VDC Battery

0-1000 mm/sec2

0-1000 mm/sec

DC Servo Motor

The Specifications of Vehicle

The Specifications of Sonar Sensor

Item Specification

Beam

Period

Item Specification

Distance

Resolu.56 pulses/msec 1 cm/m

0.15 - 10.7 m

[Polaroid Co.]

15 [Width]

Appearance of Mobile Robot


MAP & PATH-PLANNING

Mapping Method

-. Graph method : delicate but long time

-. Grid Method : not delicate but short time

Using the sensor data fusion techniqueof the data obtained by sonar sensors,generate the grid map

Having the generated grid map, plan thenavigation path by the movable window method

Path-Planning Method

-. Voronoi method : delicate but long time

-. Potential method : delicate but vibrated moving

-. 4 or 8 Connection method : correct but long time

-. Movable Window Method


Block Diagram for Navigation System of Mobile Robot

Velocity

GraphicSimulator

(IBM PC/AT)

ControlObjective

FuzzyInference

Rule

LearnedNeural

Network

StateEvaluation

Inference Engine

MOBILEROBOT

PositionCorrector(Vision)

Land Mark

ProgrammedMapping(Manual)

Path Planning(NavigationData File)

EnvironmentMapping

(Auto)

EnvironmentIdentifier

Sensor Inputs

PATH PLANNER

ENVIRONMENTDECETOR

NAVIGATOR OBSTACLE AVOIDER

Distance Difference

X1

Steering Angle

OrientationX2

y

v

POSITION MONITOR

Position&

Orientation

(Output)


Mapping Technique

Footprint of Beam and Rearranged Cells Modeling of the Sonar Snesor

r

x

y maxRminR

P(r)1

1P(

)

)()(

)()(

2/2/2/

1)(

1)(

2

maxmin

2

max

prpCPCP

prpP

p

RrRR

rrp

DetFal

Det

dataRange1 2

3

mj

kj

j

i

1 2 i j kj mj

Non occupied region occupied region


Path-Planning Technique

Environmental Coordinate The Movable Window by Grid

d 0

al

(xr , yr)

ROBOT

ar

OBSTACLE

(x’e , y’

e)

Oe Xe

Ye

OwROBOT

r r

r

R

k

Xw

Yw


Block Diagram for Map & Path-Planning

Position & Orientationof Mobile Robot

Modeling forUltrasonic Sensor

Mapper

Thresholding Path Planner

Grid Map ModifiedGrid Map

PathFollowing


Experimental Results in Mapping Method

-. Simulation Example -. Experiment Example


Experimental Results in Map & Path-Planning Method

-. Simulation Example -. Experiment Example


Fuzzy Control Fuzzy Control

AUTONOMOUS NAVIGATION

Guided Method

-. Dead-Reckoning Navigation Method : Blind Running

-. Sonar Guided Method

-. Wire Guided Method : AGV(Automated Guided Vehicle)

-. Landmark Guided Method

-. Laser Guided Method

-. Vision Guided Method

AI technique to model the control action of an expertAI technique to model the control action of an expert

It is difficult to model the navigation of a mobile robot mathematically

since we have to consider various environmental conditions such as

slippage, tear of wheel and oscillation by irregular floor.

Feature of Mobile Robot

Neural NetworkNeural Network+


DistanceDifference

X1

Reference Line

HeadingAngle

X2

( x , y , )k k k

Yn

Xn

The Geometry for Navigation

-. Controller Inputs : Distance Difference X1 and Heading Angle X2

-. Controller output : Steering Angle y


Control Strategy for Autonomous Navigation

Obstacle

Information

WallInformation

Differencex1

x2Orientation

Fuzzy Reasoning

for Navigation

Neural Networkfor ObstacleAvoidance

Steering Angley [deg/sec]

Turning Angle [deg]

Difference Control Objective

SENSOR INPUTS OUTPUTS

Sensor Data


Control Block Diagram for Autonomous Navigation

Key Point : Using the input-output data obtained by the control action of an expert

-. Identify the fuzzy control rules using FCM, GA and nonlinear programming techniques.

-. Train the neural network for obstacle avoidance.

Position &

Orientation

Control Objective

Fuzzy ControlRules

NeuralNetwork

StateEvaluation

InferenceEngine

MobileRobot

DifferenceX1

X2

Orientation

Steering Angle

y

Turning Angle


Block Diagram for Fuzzy Modeling

Pattern Data

Generation ofFuzzy Inference Rules

Identification ofParameters of FuzzyMembership Function

Genetic Algorithm+

Complex MethodGradient Method

(2) Identification of membership function

Decision ofOptimal Number ofInference Rules

Hard ClusteringFCM Clustering

(1) Decision of the number of fuzzy rules


Identification Technique of Fuzzy Control Rules

output yr

Input( ),...x,x T2

T1

2r* )y(yN

1E:Error

Optimized Result

Minimize

Genetic Algorithm+

Complex MethodGradient Method

Simplified Fuzzy Reasoning Method

Rule 1:

Rule n:

Small(A11) Big(A12)

Y

Y

yn=wn

y1=w1

w1

wn

Big(An1) Medium(An2)

x1 x2

x2x1

X10 X2

0

a11

b11

a12

b12

an1

bn1 bn2

an2

Output

21

2211*

ww

ywywy

Pattern data


Learning Technique of Neural Network

output yr

Input( ),...x,x T2

T1

X1 X2 X3 X4 X6 X7X5

y*

Multi-Layered Neural Network

Output y*2r* )y(y

N

1E:Error

Learned Network

Minimize

Back-PropagationAlgorithm


Experimental Result in Obstacle Avoidance

-. Autonomous Navigation Experiment 1 -. Autonomous Navigation Experiment 2


POSITION ESTIMATION

Sensors for Position Estimation

Absolute Sensor(External Sensor) : Measuring the external position.

-. Vision Sensor( CCD Camera) -. Laser Scanning Sensor -. Optical Sensor -. Sonar Sensor

Relative Sensor(Internal Sensor) : Measuring the relative position

-. Encoder -. Accelerometer -. Gyroscope

Estimate the position using Sensor Data Fusion Technique between

absolute sensor and relative sensor by Kalman Filter


Yw

Xw

XR

YR

R

d

300

300

200

150

[Side View] [Landmark]m

Z2

Z1xL

Reference Point( xk ,yk , k )

yL k

Y

X+Reference Coordinate system

[Top View]

Position Estimation Techniques

-. Position Estimation for Encoder -. Position Estimation for CCD camera


f YC

XC

YS

ZC

XS

P1

P2P0

P3

Landmark

Q1Q2

Q3 Q0

screen

Centerof lens

ZW

YW

XW

YL

XL

ZL

XR

YR

ZR

WTL

RTC

WTR

CTL

YC

XC

ZC

WTL=WTR RTC

CTL WTR=WTL C TL

-1 RTC-1

Absolute Coordinate in a Vision SystemThe Coordinate of Camera-Landmark


Position Estimation Algorithm by Vision System

Calculate the vertices of the landmarkin the screen coordinate : ( SXi , SYi )

Calculate the position of the robot, WTR

Calculate the vertices of the landmarkin the camera coordinate : ( CXi , CYi , CZi)

Calculate the robot-camera transformation matrix, RTC

Obtain the Image

Calculate the threshold valuesfor binarization

Calculate the vertices of the landmarkin the frame coordinate : ( FXi , FYi )

Calculate the camera-landmark transformation matrix, CTLDetect Landmark ?

Yes

No


Experimental Result in Position Estimation

-. Experimental 1 -. Experimental 2


FUTURE TREND OF MOBILE ROBOT

The development of the autonomous mobile robot system

-. Sensor data fusion technique to recognize the environment

-. Autonomous navigation method using the AI techniques

-. Man-Machine interface techniques

• Development and standardization of basic and fundamental technologies

• Integration of component technologies into a complete system

• Development of a real-time perception capabilities

-. Real-time position estimation using vision

• Development of key component hardware technologies

-. Sensors and actuators, controller H/W, etc

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