Design And Analysis Of Mechanical Hand Using Computer Aided Mechanism Design (CAMD) Technique.

‘I/We* hereby declare that this composition has been read and on my/our* opinion this composition is acceptable from the scope and the quality for being awarded Bachelor

Degree of Mechanical Engineering (Design and Innovation)’

Signature :………

1st Supervisor’s Name :………

Date :………

Signature :………

2nd _{Supervisor’s Name :………}

Date :………

DESIGN AND ANALYSIS OF MECHANICAL HAND USING COMPUTER AIDED MECHANISM DESIGN

(CAMD) TECHNIQUE

MUHAMAD KHALIS BIN RAMLI

This thesis submitted in partial fulfillment of the requirements for the degree of Mechanical Engineering

(Design & Innovation)

Faculty of Mechanical Engineering Universiti Teknikal Malaysia Melaka

ii

“I confess that this work is the result of my own investigations. All sections of the text and results, which have been obtained from other

workers/sources, are fully referenced”

Signature :………….……… Name : Muhamad Khalis bin Ramli Date : 18th May 2009

iii

iv

ACKNOWLEDGEMENT

“In The Name of Allah, The Merciful, The Beneficent”

Glory to Allah S.W.T, The Most Gracious, The Most Merciful, and peace is upon His messenger Holy Prophet Muhammad S.A.W. All the worship belongs to only Allah. We seek refuge with Allah from the wickedness within and from the evil deeds. We also praised to Allah S.W.T for giving us courage, time, and knowledge in completing this thesis for my bachelor project.

I would like to take this opportunity to thank my project supervisor, Mr. Faiz bin Redza for his invaluable guidance, assistance, and support throughout this work. Under his supervision, many aspects regarding on this project has been explored, and with knowledge, idea and support received from him, this thesis can be presented in the given time.

I’d also like to especially thank my family for their patience, support, and words of encouragement that have been crucial to this thesis completion. My parents, Mr. Ramli bin Jantan, and Mrs. Khatijah binti Johar, and also to my sister, Ms. Rosilawati binti Ramli.

Finally, I would like to dedicate my gratitude to all my lecturers involved in teaching my course, thanks for the lesson that been delivered. Not forget to all my friends, course mate, especially Mr. Abu Hanzalah bin Omar and Mr. Azrul bin Omar for being my teacher for SolidWorks, and anyone that has provided whether an idea or support, directly, or indirectly, that played a role towards in completing this work.

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ABSTRACT

Robotic hands are still long way from matching the grasping and manipulation capability of their human counterparts. One way to push the research further along is to use computer modeling and simulation to learn more about human and robotic grasping. This project have used LEGO MINDSTORMS model to construct the robotic hand for replace robotics that present nowadays. This project also analyzed by using three software to design, simulating something and analysis for this model. Among them is, COSMOSMotion, Lego Digital Designer, and SolidWorks 2007. All software that has been used provides the convenience type of design hand from the various aspects. It can accommodate a wide variety of hand designs, and it can evaluate grasps formed by these hands, as well as perform full dynamic simulation of the grasping process. In this research, it presents the various components of the systems, and describes the projects which use it as an integral part of larger grasp planning systems. This project also discusses the development of a human hand model that uses accurate geometry and kinematics derived from experimental measurements. This is part of current project to create a biomechanically realistic human hand model to better understand what features are most important to mimic in the designs of robotic hands by using this LEGO MINDSTORMS model.

vi

ABSTRAK

Keupayaan tangan robotik masih lagi jauh untuk dibandingkan dengan keupayaan genggaman dan manipulasi daripada tangan manusia. Salah satu cara untuk membawa kajian ini lebih jauh ialah dengan menggunakan pembentukan dan simulasi komputer untuk mempelajari dengan lebih lanjut berkenaan dengan genggaman manusia dan robotik. Projek ini telah menggunakan model “LEGO MINDSTORMS” untuk membina model tangan berkenaan bagi menggantikan robotik yang sedia ada. Projek ini juga dianalisis dengan menggunakan tiga perisian untuk mereka bentuk, membuat sesuatu simulasi dan analisis terhadap model berkenaan. Antara perisian yang dimaksudkan adalah “COSMOSMotion”, “Lego Digital Designer”, dan “SolidWorks 2007”. Semua perisian yang digunakan ini memberikan kemudahan dari segi pelbagai jenis reka bentuk tangan, serta menjalankan simulasi dinamik penuh tentang proses menggenggam. Kajian ini juga memperkenalkan pelbagai komponen untuk sistem ini dan menerangkan segala projek yang menggunakan komponen tersebut sebagai bahagian penting untuk sistem genggaman yang lebih besar. Projek ini juga membincangkan tentang pembangunan sebuah model tangan manusia yang menggunakan geometri yang tepat dan kinematik yang diterbitkan daripada pengukuran eksperimen. Ini adalah sebahagian daripada projek terkini untuk menghasilkan sebuah model tangan manusia realistik biomekanikal untuk memahami dengan lebih baik tentang apakah ciri-ciri yang penting untuk ditiru dalam rekabentuk tangan robotik dengan menggunakan model “LEGO MINDSTORMS” ini.

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TABLE OF CONTENT

CHAPTER TOPIC PAGE

CONFESSION ii

DEDICATION iii

ACKNOWLEDGEMENT iv

ABSTRACT v

ABSTRAK vi

TABLE OF CONTENT vii

LIST OF TABLES xi

LIST OF FIGURES xii

LIST OF APPENDIX xvii

CHAPTER I INTRODUCTION 1

1.1 Objective 1

1.2 Scope 2

1.3 Problem Statement 2

CHAPTER II LITERATURE REVIEW 4

2.1 Related Work 4

2.2 High Level Grasp Synthesis 4

viii

CHAPTER TOPIC PAGE

2.3.1 Theoretical Bounds on the Necessary and Sufficient Number of Contacts Required for Form- and Force-Closure

8

2.3.2 Quality Measures 9

2.3.3 Contact-level Synthesis Algorithms 10 2.3.4 Hand-level Grasp Synthesis Systems 11 2.3.5 Optimal Force Distribution 13

2.4 LEGO MINDSTORMS NXT 14

2.4.1 Introduction 14

2.4.2 The NXT 15

2.4.3 Output (motor) ports and the USB port

16

2.4.4 The input (sensor) ports 17

2.4.5 The servo motor 18

2.4.6 The Sensors 19

2.4.6.1 Passive Sensors 19

2.4.6.2 Digital sensor 23

2.5 LEGO MINDSTORMS NXT pieces 25

2.5.1 Classifying the pieces 26

2.6 Software Description 29

2.6.1 SolidWorks 2007 29

2.6.2 COSMOSMotion 30

2.6.2.1 User Interface 31

2.6.2.2 Constraints 32

2.6.2.3 Forces 33

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CHAPTER TOPIC PAGE

CHAPTER III METHODOLOGY 35

3.1 Methodology Flow Chart 35

3.1.1 Literature Review 37

3.1.2 Designing 37

3.1.3 Analysis 37

3.1.4 Result and Discussion 38

3.1.5 Conclusion 38

CHAPTER IV BUILDING LEGO MINDSTORMS NXT PIECES

4.1 The Beams 39

4.1.1 The Straight Beam 41

4.1.2 The Angled Beam 42

4.2 The Connectors 43

4.2.1 The Axles 46

4.2.2 The Pegs 48

4.3 The Gears 48

4.3.1 Spur Gears 50

4.3.2 Turntable Gears 50

CHAPTER V DETAILED DESIGN 52

5.1 Introduction 52

5.2 Part Design 52

5.2.1 Mechanical Base 54

5.2.2 Mechanical Arm 55

5.2.3 Mechanical Hand 56

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CHAPTER TOPIC PAGE

CHAPTER VI ANALYSIS DESIGN 60

6.1 Introduction 60

6.2 Analysis with COSMOSMotion 2007 60

6.2.1 Procedure of analysis using COSMOSMotion

61

6.2.2 Motion after simulation 66

CHAPTER VII RESULT AND DISCUSSION 69

7.1 Results with COSMOSMotion 2007 69

7.1.1 Velocity Analysis 69

7.1.2 Acceleration 73

CHAPTER VIII CONCLUSION & RECOMMENDATION 78

8.1 Conclusion 78

8.2 Recommendation 79

REFERENCES 80

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LIST OF TABLES

NO. TITLE PAGE

2.1 The Default Sensor Ports On The NXT 18

4.1 The NXT Beams 40

4.2 The Connectors In The NXT Set 43

xii

LIST OF FIGURES

NO. TITLE PAGE

2.1 Taxonomy Of Human Grasps By Cutkosky And Wright’s (1986)

5

2.2 NXT Programming With Four Types Of Sensor And Three Servo Motor

14

2.3 The NXT, A LEGO MINDSTORMS Microcomputer 15

2.4 Output Ports A, B, C; And The USB Port 16

2.5 The Input Ports On The NXT 17

2.6 The MINDSTORMS NXT Servo Motor 18

2.7 The Built-In Rotation Sensor Or Tachometer 19

2.8 The MINDSTORMS NXT Touch Sensor 20

2.9 Functions Of The Touch Sensor 21

xiii

NO. TITLE PAGE

2.11 The Comparison Between When Human Eyes See The Light and When Robot Sees Using The Light Sensor

22

2.12 The MINDSTORMS NXT Sound Sensor 22

2.13 The MINDSTORMS NXT Ultrasonic Sensor 24

2.14 The Ultrasonic Sensor Sends and Then Receives A Sound Wave to Determine The Distance Between Itself and an Object

24

2.15 The Types and Quantities Of Pieces In The NXT Set 26

2.16 The Beams In The NXT Set 27

2.17 The Connectors In The NXT Set 27

2.18 The Gears In The NXT Set 28

2.19 The Miscellaneous Elements In The NXT Set 28

2.20 SolidWorks Official Logo 29

2.21 COSMOSMotion Logo 30

3.1 Methodology Flow Chart 36

xiv

NO. TITLE PAGE

4.2 A Module (M) is about 8 Mm, the Distance from The Centre Of One Hourglass-Shaped Depression to The Center Of One Adjacent Depression

42

4.3 The 7M Perpendicular Angled Beam 43

4.4 The 7M Axle 47

4.5 The Bushing And Half-Bushing are Assistants to The Axles

47

4.6 3M Friction Peg 48

4.7 The 8t Gear 50

4.8 The Turntable Gear 51

5.1 Mechanical Base Assembly 54

5.2 The Servo Motor Was Located In The Center of The Mechanical Base Assembly

55

5.3 The Assembly of Mechanical Arm 56

5.4 The Assembly of Mechanical Hand 57

5.5 Full Assemblies That Joint From Three Small Parts Assemblies

xv

NO. TITLE PAGE

5.6 The Mechanical Hand Want to Grasp The Red Ball 59

6.1 Add-Ins Tab 61

6.2 “Motion” Menu 62

6.3 Intellimotion Builder 62

6.4 Mechanical Hand 64

6.5 Orange Motor and Motor is the Revolute Between These Parts

65

6.6(a) First Description about Simulation when The Mechanical Hand to Grasp The Red Ball

66

6.6(b) Second Description about Simulation when The Mechanical Hand to Grasp The Red Ball

67

6.6(c) Third Description about Simulation when The Mechanical Hand to Grasp The Red Ball

67

6.6(d) Fourth Description about Simulation when The Mechanical Hand to Grasp The Red Ball

68

6.6(e) Fifth Description about Simulation when The Mechanical Hand to Grasp The Red Ball

xvi

NO. TITLE PAGE

7.1 CM Velocity Beam Angled Below 70

7.2 CM Velocity Beam Angled Upper 71

7.3 CM Velocity 8t Gear 71

7.4 CM Velocity 24t Gear 72

7.5 CM Velocity Orange Motor 73

7.6 CM Acceleration Beam Angled Below 74

7.7 CM Acceleration Beam Angled Upper 74

7.8 CM Acceleration 8t Gear 75

7.9 CM Acceleration 24t Gear 76

xvii

LIST OF APPENDIX

NO. TITLE PAGE

A PROJECT PLANNING 84

B ENGINEERING DRAWING 86

1

CHAPTER I

INTRODUCTION

Grasping is one of the primary ways for a robot to interact with objects in its environment. Assembly line robots use simple end-effectors such as suction cups or parallel jaw grippers to accomplish pick and place tasks with a high degree of reliability. However, the factory environment is highly structured, and each robot usually only grasps one type of object until it is reprogrammed to handle another object type. This reprogramming is done simply using operator knowledge or more recently using software to automatically pick the contact points (Smith et al., 1999). As robots are being required to work in more unstructured environments, such as service robots in the home, bomb disposal robots, and robots used in space missions, more general purpose hands are being designed that are capable of grasping and manipulating a much wider range of objects. Hence, in these projects, one model of mechanical hands will be constructing by using LEGO MINDSTORMS model to study the movement mechanism by grasp any objects. This project also can put something more clearly about the mechanical hand in diurnal life.

1.1 Objective

The objective of this project is to design and analyze a mechanical hand that can grasp object using CAMD (Computer Aided Mechanism Design) Technique, which is building the mechanical hand by using LEGO MINDSTORMS NXT. With

2

using the touch sensor to trace object, the touch sensor will work to take the object and move it from one place to another place.

1.2 Scope

Following are the scope of the projects to make sure this project is successful:

i. Design mechanical hand by using 3D CAD software, such as SolidWorks 2007 and Lego Digital Designer.

ii. Study the mechanism of mechanical hand by using CAMD techniques, likes COSMOSMotion.

iii. Then, use the COSMOSMotion to do the analysis about the acceleration and the velocity. To do the simulation by using the same software to investigate the mechanical hand when grasp object.

iv. Also, use COSMOSMotion to make the animation how it works. v. Analyze and suggest improvement of mechanical hand mechanism so

that the model must be in the best condition and able to function in a long period of time.

1.3 Problem Statement

Nowadays, LEGO MINDSTORMS NXT becomes smartest, strongest, and most advanced in many ways. Even the LEGO MINDSTORMS NXT becomes the next generation of robots, in Malaysia doesn’t have any platforms for the society to develop any idea to make the new robot. The best design of the mechanical hand model to be developed using LEGO MINDSTORMS. The designs are limited to the components which are provided with the set of the LEGO MINDSTORMS, or better known as bricks. In other words, the model needs to be designed with taking into account on the limited amount of bricks provided.

3

Apart from that is to determine which software can give the accurate value when do the analysis. For example, between SolidWorks and ADAMS, which software can give the actual value with the origin condition?

Other than that is how to identify the limitation factors of the model, such as the types of objects that can be grasped, the object’s minimum and maximum weight, and other factors. For example, the simulation data taken from COSMOSWorks Designer and COSMOSMotion, which can give the accurate data with the real condition when grasp the object.

Lastly, the software which is files that saved can it moves from the SolidWorks to ADAMS? Which software that can use to draw the bricks (small) part of LEGO MINDSTORMS to make the mechanical hand? Which software that can use to do the animation? All this are the problem statement.

4

CHAPTER II

LITERATURE REVIEW

2.1 Related Work

Because of the versatility of the human hand, there has been a great deal of research devoted to studying the hand and to building robotic hands that can mimic some of its functionality. This thesis focuses on grasp analysis as well as simulation, and this chapter reviews the significant body of work in these areas. A clear division of approach in grasping was illustrated by Mishra and Silver, (1989) who separated the previous work into higher level of physiological studies of the human hand and lower level studies of the mechanics of contact. While this thesis is primarily concerned with simulating and analyzing the grasping mechanics, the synthesis techniques of the higher level approach cannot be ignored. This chapter begins by reviewing these techniques, and then it presents the large body of previous work concerned with the mechanics of grasping which is further divided into several sub-areas. Finally, the chapter concludes by reviewing research in the area of general robotic simulation.

2.2 High Level Grasp Synthesis

One difficulty in synthesizing grasps in the large number of degrees of freedom possessed by typical robotic hands. This flexibility gives rise to an

5

enormous set of possible hand configurations. However, in choosing their own grasps, humans unconsciously simplify the task to selecting one of the only a few different prehensile postures appropriate for the object and for the task to be performed. Medical literature has attempted to classify these postures into grasp taxonomies as seen in McBride (1942), Griffiths (1943), and Slocum and Pratt (1946), but the most well known grasp taxonomy was proposed by Napier (1956). His work establishes the fundamental differences between power grips, which envelop the object and offer maximum manipulability. Cutkosky and Wright (1986) extended this classification to the types of grips needed a manufacturing environment and examined how the task and object geometry affect the choice of grasp. Their tree-like classification can be seen in Figure 2.1.

Figure 2.1: Taxonomy of human grasps by Cutkosky and Wright’s, (1986)

Iberall (1987) saw this classification as too rigid, since in practice, the human hand often uses a combination of grasps to accomplish a task. She defined grasps

6

with respect to two virtual fingers which apply opposing forces on the objects, and only later maps these virtual fingers onto physical fingers based on object characteristics. The basic oppositions of the virtual fingers are divided into pad opposition, which is between the thumb and finger pads and used for precision type grasps, palm opposition, which is between the palm and the digits and used for power type of grasps, and side opposition, which is between the thumb and the side of the index finger and is compromise between the flexibility of the pad opposition grasps and the stability of the palm opposition grasps. Lyons (1985) uses the concept of the virtual fingers in his development of a grasp index that that selects a grasp on the basis of two object characteristics, shape, and size, whether the grasp should be firm or not and whether the grasp should be precise or not. Unfortunately, his categories are quite broad and make it difficult to create a grasp tailored to specific objects.

Stansfield (1991) built these classifications into a rule based system that, when given a simplified object description from a vision subsystem, will provide a set of possible hand preshapes and reach directions for the pre-contact stage of grasping. However, many problems are left unsolved. She only examines five possible approach directions, she does not try to choose the best grasp from this set of possibilities, and for any grasp that is chosen, the hand simply closes its fingers; no attempt is made to optimize the grasp for stability.

Pao and Speeter (1989) developed a method of solving for a transform that maps human hand poses to poses of the robotic hand, and using a Data Glove to measure the joint angles of a human hand, they were able to recreate a variety of poses with the robotic hand. Speeter (1991) later created HPL, Hand Programming Language that simplifies the problem of coding robotic grasps and dexterous manipulation tasks. The language consists of a number of motion primitives that are related to common human grasps and manipulation motions, providing a high-level abstraction of the grasping process.

CHAPTER I

INTRODUCTION

Grasping is one of the primary ways for a robot to interact with objects in its environment. Assembly line robots use simple end-effectors such as suction cups or parallel jaw grippers to accomplish pick and place tasks with a high degree of reliability. However, the factory environment is highly structured, and each robot usually only grasps one type of object until it is reprogrammed to handle another object type. This reprogramming is done simply using operator knowledge or more recently using software to automatically pick the contact points (Smith et al., 1999). As robots are being required to work in more unstructured environments, such as service robots in the home, bomb disposal robots, and robots used in space missions, more general purpose hands are being designed that are capable of grasping and manipulating a much wider range of objects. Hence, in these projects, one model of mechanical hands will be constructing by using LEGO MINDSTORMS model to study the movement mechanism by grasp any objects. This project also can put something more clearly about the mechanical hand in diurnal life.

1.1 Objective

The objective of this project is to design and analyze a mechanical hand that can grasp object using CAMD (Computer Aided Mechanism Design) Technique, which is building the mechanical hand by using LEGO MINDSTORMS NXT. With

using the touch sensor to trace object, the touch sensor will work to take the object and move it from one place to another place.

1.2 Scope

Following are the scope of the projects to make sure this project is successful:

i. Design mechanical hand by using 3D CAD software, such as

SolidWorks 2007 and Lego Digital Designer.

ii. Study the mechanism of mechanical hand by using CAMD

techniques, likes COSMOSMotion.

iii. Then, use the COSMOSMotion to do the analysis about the

acceleration and the velocity. To do the simulation by using the same software to investigate the mechanical hand when grasp object.

iv. Also, use COSMOSMotion to make the animation how it works.

v. Analyze and suggest improvement of mechanical hand mechanism so

that the model must be in the best condition and able to function in a long period of time.

1.3 Problem Statement

Nowadays, LEGO MINDSTORMS NXT becomes smartest, strongest, and most advanced in many ways. Even the LEGO MINDSTORMS NXT becomes the next generation of robots, in Malaysia doesn’t have any platforms for the society to develop any idea to make the new robot. The best design of the mechanical hand model to be developed using LEGO MINDSTORMS. The designs are limited to the components which are provided with the set of the LEGO MINDSTORMS, or better known as bricks. In other words, the model needs to be designed with taking into account on the limited amount of bricks provided.

Apart from that is to determine which software can give the accurate value when do the analysis. For example, between SolidWorks and ADAMS, which software can give the actual value with the origin condition?

Other than that is how to identify the limitation factors of the model, such as the types of objects that can be grasped, the object’s minimum and maximum weight, and other factors. For example, the simulation data taken from COSMOSWorks Designer and COSMOSMotion, which can give the accurate data with the real condition when grasp the object.

Lastly, the software which is files that saved can it moves from the SolidWorks to ADAMS? Which software that can use to draw the bricks (small) part of LEGO MINDSTORMS to make the mechanical hand? Which software that can use to do the animation? All this are the problem statement.

CHAPTER II

LITERATURE REVIEW

2.1 Related Work

Because of the versatility of the human hand, there has been a great deal of research devoted to studying the hand and to building robotic hands that can mimic some of its functionality. This thesis focuses on grasp analysis as well as simulation, and this chapter reviews the significant body of work in these areas. A clear division of approach in grasping was illustrated by Mishra and Silver, (1989) who separated the previous work into higher level of physiological studies of the human hand and lower level studies of the mechanics of contact. While this thesis is primarily concerned with simulating and analyzing the grasping mechanics, the synthesis techniques of the higher level approach cannot be ignored. This chapter begins by reviewing these techniques, and then it presents the large body of previous work concerned with the mechanics of grasping which is further divided into several sub-areas. Finally, the chapter concludes by reviewing research in the area of general robotic simulation.

2.2 High Level Grasp Synthesis

One difficulty in synthesizing grasps in the large number of degrees of freedom possessed by typical robotic hands. This flexibility gives rise to an

enormous set of possible hand configurations. However, in choosing their own grasps, humans unconsciously simplify the task to selecting one of the only a few different prehensile postures appropriate for the object and for the task to be performed. Medical literature has attempted to classify these postures into grasp taxonomies as seen in McBride (1942), Griffiths (1943), and Slocum and Pratt (1946), but the most well known grasp taxonomy was proposed by Napier (1956). His work establishes the fundamental differences between power grips, which envelop the object and offer maximum manipulability. Cutkosky and Wright (1986) extended this classification to the types of grips needed a manufacturing environment and examined how the task and object geometry affect the choice of grasp. Their tree-like classification can be seen in Figure 2.1.

Figure 2.1: Taxonomy of human grasps by Cutkosky and Wright’s, (1986)

Iberall (1987) saw this classification as too rigid, since in practice, the human hand often uses a combination of grasps to accomplish a task. She defined grasps

with respect to two virtual fingers which apply opposing forces on the objects, and only later maps these virtual fingers onto physical fingers based on object characteristics. The basic oppositions of the virtual fingers are divided into pad opposition, which is between the thumb and finger pads and used for precision type grasps, palm opposition, which is between the palm and the digits and used for power type of grasps, and side opposition, which is between the thumb and the side of the index finger and is compromise between the flexibility of the pad opposition grasps and the stability of the palm opposition grasps. Lyons (1985) uses the concept of the virtual fingers in his development of a grasp index that that selects a grasp on the basis of two object characteristics, shape, and size, whether the grasp should be firm or not and whether the grasp should be precise or not. Unfortunately, his categories are quite broad and make it difficult to create a grasp tailored to specific objects.

Stansfield (1991) built these classifications into a rule based system that, when given a simplified object description from a vision subsystem, will provide a set of possible hand preshapes and reach directions for the pre-contact stage of grasping. However, many problems are left unsolved. She only examines five possible approach directions, she does not try to choose the best grasp from this set of possibilities, and for any grasp that is chosen, the hand simply closes its fingers; no attempt is made to optimize the grasp for stability.

Pao and Speeter (1989) developed a method of solving for a transform that maps human hand poses to poses of the robotic hand, and using a Data Glove to measure the joint angles of a human hand, they were able to recreate a variety of poses with the robotic hand. Speeter (1991) later created HPL, Hand Programming Language that simplifies the problem of coding robotic grasps and dexterous manipulation tasks. The language consists of a number of motion primitives that are related to common human grasps and manipulation motions, providing a high-level abstraction of the grasping process.