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DOCTORAL THESIS

Virtual Holonomic Constraintsfrom academic to industrial applications

DANIEL ORTÍZ MORALES

UNIVERSITETSSERVICE

Profil & CopyshopÖppettider:Måndag - fredag 10-16

Tel. 786 52 00 alt 070-640 52 01Universumhuset

Robotics and Control Lab

Department of Applied Physics and Electronics

UMEÅ UNIVERSITY, SWEDEN

Umeå, 2015

Daniel Ortíz Morales

Department of Applied Physics and Electronics

Umeå University, Sweden

SE-90748 UMEÅ

Author e-mail:[email protected]

Thesis submitted for the degree of Doctor of Philosophy (Ph.D.)

in Applied Physics and Electronics with specialization in Control Engineering.

Akademisk avhandling för avläggande av teknologie doktorsexamen (Tekn. Dr.)

i tillämpad elektronik med inriktning mot reglerteknik.

Typeset in LATEX by Daniel Ortíz Morales

Copyright c© Daniel Ortíz Morales, 2015.

ISSN:1654-5419:7

ISBN:978-91-7601-196-6

E-version available at http://umu.diva-portal.org

Printed by Print & Media, Umeå University, Umeå, January 2015

Virtual Holonomic Constraintsfrom academic to industrial applications


Robotics and Control Lab, Umeå University, Sweden

ABSTRACT

Whether it is a car, a mobile phone, or a computer, we are noticing how automation

and production with robots plays an important role in the industry of our modern world.

We find it in factories, manufacturing products, automotivecruise control, construction

equipment, autopilot on airplanes, and countless other industrial applications.

Automation technology can vary greatly depending on the field of application. On

one end, we have systems that are operated by the user and relyfully on human ability.

Examples of these are heavy-mobile equipment, remote controlled systems, helicopters,

and many more. On the other end, we have autonomous systems that are able to make

algorithmic decisions independently of the user.

Society has always envisioned robots with the full capabilities of humans. However,

we should envision applications that will help us increase productivity and improve our

quality of life through human-robot collaboration. The questions we should be asking are:

“What tasks should be automated?”, and “How can we combine thebest of both humans

and automation?”. This thinking leads to the idea of developing systems with some level

of autonomy, where the intelligence is shared between the user and the system. Reason-

ably, the computerized intelligence and decision making would be designed according to

mathematical algorithms and control rules.

This thesis considers these topics and shows the importanceof fundamental mathe-

matics and control design to develop automated systems thatcan execute desired tasks.

All of this work is based on some of the most modern concepts inthe subjects of robotics

and control, which are synthesized by a method known as the Virtual Holonomic Con-

straints Approach. This method has been useful to tackle some of the most complex prob-

lems of nonlinear control, and has enabled the possibility to approach challenging aca-

demic and industrial problems. This thesis shows concepts of system modeling, control

design, motion analysis, motion planning, and many other interesting subjects, which can

be treated effectively through analytical methods. The useof mathematical approaches al-

lows performing computer simulations that also lead to direct practical implementations.

Sammanfattning

Oavsett om det är en bil, en mobiltelefon eller en dator, märker vi hur produktionen med

robotar påverkar industrin i vår moderna värld. Det förekommer i fabriker, i anläggnings-

maskiner, som farthållare för våra fordon och som autopiloti kommersiell flygtrafik, för

att nämna några exempel.

Automation kan variera mycket beroende på användningsområdet. I ena änden har vi

standardsystem som kontrolleras av användaren och förlitar sig helt på människans för-

måga. Exempel på dessa är anläggningsmaskiner, R/C-system, helikoptrar och mycket

annat. I andra änden har vi autonoma system som kan fatta beslut oberoende av använ-

daren.

Samhället har alltid föreställt sig robotar med den fulla kapaciteten hos människor.

Vi bör dock istället föreställa oss tillämpningar som hjälper oss att öka produktiviteten

och förbättra vår livskvalitet genom människa-robot-samarbete. De frågor vi bör ställa

är: “Vilka uppgifter bör automatiseras?”, och “Hur kan vi kombinera det bästa från både

människor och automation?”. Detta leder till tanken att skapa maskiner med viss grad av

autonomi, där intelligens definieras av en uppsättning matematiska algoritmer och kon-

trollregler.

Denna avhandling behandlar användandet av matematiska modeller och reglerdesign,

så att ett automatiserat system kan utföra förutbestämda arbetsuppgifter. Allt detta arbete

har baserats på ett flertal moderna koncept i ämnena roboteknik och reglerteknik som syn-

tetiserats genom metoden ”virtuella holonoma bivillkor” (virtual holonomic constraints).

Denna metod har varit användbar för att ta itu med några av de mest komplexa problemen

inom olinjär reglering, och har gjort det möjligt att närma sig industriella problem som

annars är svåra att angripa. Genom avhandlingen visas begreppen systemmodellering,

reglerdesign, rörelseanalys, rörelseplanering, etc., som kan behandlas effektivt genom

analysmetoder. Detta tillåter utförandet av datorsimuleringar som också leder till prak-

tiska tillämpningar.

iii

v

To my parents Raquel and Daniel and my sister Raquel, with gratitude, appreciation, and

love. They have given me their love, unequivocal support throughout.

In memory of Joshua David Flanders

October 10, 1980 – April 14, 2011

Preface

“A new scientific truth is usually not

propagated in such a way that

opponents become convinced and

discard their previous views. No, the

adversaries eventually die off, and the

upcoming generation is familiarized

anew with the truth.”

Max Planck

Parts of the contributions presented in this thesis have previously been accepted to

conferences or submitted to journals. A list of these publications is given below.

⊲ Daniel Ortíz Morales, P. La Hera and S. Ur Rehman. “Generating Periodic

Motions for the Butterfly Robot,” inProc. IEEE International Conference on

Intelligent Robots and Systems, pp. 2527–2532, Tokyo, Japan, 3–7 Novem-

ber 2013. c© 2013 IEEE.

⊲ Daniel Ortíz Morales and Pedro X. La Hera, “Design of energy efficient

walking gaits for a three-link planar biped walker with two unactuated de-

grees of freedom,” inProc. IEEE International Conference on Robotics and

Automation, pp. 148–153, Saint Paul, Minnesota, USA, 14–18 May 2012.

c© 2012 IEEE.

⊲ Pedro X. La Hera, andDaniel Ortíz Morales “Non-linear dynamics mod-

elling description for simulating the behavior of forestrycranes,”Interna-

tional Journal of Modelling, Identification and Control, Vol. 21 No. 2

pp.125–138, 2014, DOI: 10.1504/IJMIC.2014.060006,c© 2014 Inderscience

Publishers.

http://inderscience.metapress.com/content/XX51264172M1U31W

vii

viii Preface

⊲ Pedro X. La Hera, andDaniel Ortíz Morales “Designing and testing control

systems for forestry cranes,”(Submitted Manuscript).

⊲ Daniel Ortíz Morales, Simon Westerberg, Pedro X. La Hera, Uwe Mettin,

Leonid B. Freidovich, and Anton S. Shiriaev “Increasing thelevel of au-

tomation in the forestry logging process with crane trajectory planning and

control,” in Journal of Field Robotics, Vol. 31 Issue 3, pp. 343–363, DOI:

10.1002/rob.21496,c© 2014 Wiley Blackwell.

⊲ Daniel Ortíz Morales, Pedro X. La Hera, Simon Westerberg, Leonid B. Frei-

dovich, and Anton S. Shiriaev “Path-constrained motion analysis. An al-

gorithm to understand human performance on hydraulic manipulators,”Ac-

cepted as a regular paper in the IEEE Transactions on Human-Machine Sys-

tems.

List of Publications

A list of all contributions published by the author is given below.

1. U. Mettin, P. La Hera, D. Ortíz Morales, A. Shiriaev, L. Freidovich, and S. Wester-

berg.Trajectory planning and time-independent motion control for a kinemat-

ically redundant hydraulic manipulator . In Proc. 14th International Conference

on Advanced Robotics, pp. 1–6, Munich, Germany, June 2009.

2. D. Ortíz Morales, P. La Hera, U. Mettin, L. Freidovich, A. Shiriaev, and S. West-

erberg. Steps in trajectory planning and controller design for a hydraulically

driven crane with limited sensing. In Proc. International Conference on Intelli-

gent Robots and Systems, pp. 3836–3841, Taipei, Taiwan, October 2010.

3. D. Ortíz Morales, S. Westerberg, P. La Hera, U. Mettin, L. Freidovich, and A. Shiri-

aev. Open-loop control experiments on driver assistance for crane forestry

machines. In Proc. International Conference on Robotics and Automation, pp.

1797–1802, Shanghai, China, May 2011.

4. D. Ortíz Morales and P. La Hera.Design of energy efficient walking gaits for

a three-link planar biped walker with two unactuated degrees of freedom. In

Proc. International Conference on Robotics and Automation, pp. 148–153, Saint

Paul, Minnesota, USA, May 2012.

ix

5. D. Ortíz Morales and P. La Hera.Design of stable walking gaits for biped robots

with several underactuated degrees of freedom.. In Proc. Dynamic Walking

Conference, pp. 1–2, Pensacola, Florida, USA, May 2012.

6. P. La Hera and D. Ortíz Morales.Modeling dynamics of an electro-hydraulic

servo actuated manipulator: A case study of a forestry forwarder crane. In

Proc. World Automation Congress, pp. 1–6, Puerto. Vallarta, Mexico, June 2012.

7. P. La Hera, B. Ur Rehman and D. Ortíz Morales.Electro-hydraulically actuated

forestry manipulator: Modeling and Identification . In Proc. International Con-

ference on Intelligent Robots and Systems, pp. 3399–3404, Vilamoura, Algarve,

Portugal, October 2012.

8. D. Ortíz Morales, P. La Hera and S. Ur Rehman.Generating periodic motions

for the butterfly robot . In Proc. International Conference on Intelligent Robots

and Systems, pp. 2527–2532, Tokyo, Japan, November 2013.

9. P. La Hera and D. Ortíz Morales.Non-linear dynamics modelling descrip-

tion for simulating the behavior of forestry cranes. International Journal of

Modelling, Identification and Control., Vol. 21 No. 2 pp.125–138, 2014, DOI:

10.1504/IJMIC.2014.060006.

10. D. Ortíz Morales, S. Westerberg, P. La Hera, U. Mettin, L.Freidovich, and A. Shiri-

aev. Increasing the level of automation in the forestry logging process with

crane trajectory planning and control. Journal of Field Robotics, Vol. 31, Issue

3, pp. 343–363, 2014, DOI: 10.1002/rob.21496.

11. P. La Hera and D. Ortíz Morales.Designing and testing control systems for

forestry cranes. Preliminary reviewed:IEEE Transactions Journal on Control

Systems Technology.

12. D. Ortíz Morales, P. La Hera, S. Westerberg, L. Freidovich, and A. Shiriaev.Path-

constrained motion analysis. An algorithm to understand human performance

on hydraulic manipulators. Accepted as a regular paper:IEEE Transactions

Journal on Human–Machine Systems.

x Preface

Acknowledgments

“You have to give yourself credit, not

too much because that would be

bragging.”

Frank McCourt

First and foremost, I would like to thank God for providing meguidance and wisdom

during my time in earth. I thank my parents for all their love and support.

There is a large amount of people without whom this thesis would have not been

as successful, and to whom I am greatly indebted. I would liketo express my special

appreciation and gratitude to my supervisor Professor Anton Shiriaev for encouraging me

during my research time and for allowing me to grow as a researcher. I would also like

to thank my co-supervisor Dr. Leonid Freidovich for all the patience, support and time

he spent helping me understanding even the most simple concepts. Your personal and

professional advice have been priceless.

I am very grateful with all the staff of TFE, and with all the people from the Robotics

Control lab who became my colleagues and friends, and contributed greatly in my devel-

opment. My special thanks and admiration to Drs. Pedro La Hera, Uwe Mettin and Simon

Westerberg with whom I had uncountable discussions, received several corrections, and

developed several projects. I would also like to thank my good friend and office mate Mr.

Szabolcs Fodor, it was really enjoyable to share office with you.

And last but not least, I would like to thank to my country Mexico, to Sweden that

has become my new home, my sister, all my friends, relatives,teachers, partner, and all

others who externally influenced my work in many ways. I am really sorry that I cannot

mention you all.


xi

xii Acknowledgments

Contents

Abstract i

Preface vii

Acknowledgments xi

I Comprehensive Summary of Contributions 1

1 Introduction 1

1.1 Outline . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2 Summary of contributions 5

2.1 Butterfly Robot (Paper I) . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Three-link Planar Biped Walker (Paper II) . . . . . . . . . . . .. . . 9

2.3 Hydraulic manipulators (Papers III-VI) . . . . . . . . . . . . .. . . . 16

3 Concluding remarks 25

II Papers 29

I Generating Periodic Motions for the Butterfly Robot. 33

I.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

I.2 Model of the Butterfly Robot . . . . . . . . . . . . . . . . . . . . . . 35

I.2.1 Reaction Force . . . . . . . . . . . . . . . . . . . . . . . . 37

I.3 Trajectory Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . 39

I.4 Examples of constraint functions . . . . . . . . . . . . . . . . . . .. 40

xiii

xiv Contents

I.5 Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

I.5.1 Dynamics along the target orbit and transverse coordinates . 43

I.5.2 Partial feedback linearization . . . . . . . . . . . . . . . . . 44

I.5.3 Integral of the reduced dynamics . . . . . . . . . . . . . . . 44

I.5.4 Coordinates measuring the distance . . . . . . . . . . . . . . 45

I.5.5 Transverse Linearization . . . . . . . . . . . . . . . . . . . 45

I.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

I.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

I.7.1 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . 48

II Design of energy efficient walking gaits for a three-link planar biped walker

with two unactuated degrees of freedom. 53

II.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

II.2 Model of the biped walker . . . . . . . . . . . . . . . . . . . . . . . 55

II.2.1 Swing Phase Model . . . . . . . . . . . . . . . . . . . . . . 55

II.2.2 Impact Model . . . . . . . . . . . . . . . . . . . . . . . . . 56

II.3 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 57

II.4 Gait Synthesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58

II.4.1 Re-parametrization of the trajectories . . . . . . . . . .. . . 58

II.4.2 Equations for constraint functions . . . . . . . . . . . . . .59

II.4.3 Defining a function forφ2 . . . . . . . . . . . . . . . . . . . 61

II.4.4 Optimization procedure to find periodic gaits . . . . . .. . 62

II.4.5 Example of a Gait . . . . . . . . . . . . . . . . . . . . . . . 63

II.5 Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

II.5.1 Dynamics along the target orbit and transverse coordinates . 64

II.5.2 Partial feedback linearization . . . . . . . . . . . . . . . . .65

II.5.3 Integral of the reduced dynamics . . . . . . . . . . . . . . . 66

II.5.4 Coordinates measuring the distance . . . . . . . . . . . . . .66

II.5.5 Hybrid Transverse Linearization . . . . . . . . . . . . . . . 67

II.5.6 Feedback controller design . . . . . . . . . . . . . . . . . . 67

II.5.7 Control Design Simulations . . . . . . . . . . . . . . . . . . 68

II.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

III Non-linear dynamics modelling description for simulat ing the behavior of

forestry cranes. 73

III.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Contents xv

III.2 Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

III.3 Modelling the system dynamics . . . . . . . . . . . . . . . . . . . .76

III.3.1 Kinematic modeling . . . . . . . . . . . . . . . . . . . . . . 76

III.3.2 Modelling rigid-body dynamics . . . . . . . . . . . . . . . . 78

III.3.3 Modelling friction forces . . . . . . . . . . . . . . . . . . . 79

III.3.4 Combining the models of dynamics and friction forces . . . 80

III.3.5 Mapping cylinder forces to joint torques . . . . . . . . .. . 81

III.3.6 Modelling dynamics of the hydraulic system . . . . . . .. . 84

III.4 Calibration and estimation of unknown model parameters . . . . . . . 88

III.4.1 Parameter estimation procedure for the model (III.13) . . . . 88

III.4.2 Parameter estimation procedure for the hydraulic model (III.38) 91

III.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94

IV Designing and testing control systems for forestry cranes. 99

IV.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100

IV.1.1 Overview of forestry machines . . . . . . . . . . . . . . . . 100

IV.1.2 Overview of control of forestry cranes . . . . . . . . . . . .101

IV.1.3 Overview of Model-Based Design as a tool to go from re-

search to product development. . . . . . . . . . . . . . . . . 102

IV.1.4 Paper organization . . . . . . . . . . . . . . . . . . . . . . . 103

IV.2 System modeling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

IV.2.1 Mechanics . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

IV.2.2 Friction forces . . . . . . . . . . . . . . . . . . . . . . . . . 106

IV.2.3 Relation between hydraulic force and mechanical torque . . 106

IV.2.4 Hydraulic cylinder dynamics . . . . . . . . . . . . . . . . . 107

IV.2.5 Electro-hydraulic valve . . . . . . . . . . . . . . . . . . . . 108

IV.2.6 Hydraulic force as a function of the control input . . .. . . 110

IV.2.7 Mechanical motion as a function of the control input .. . . 111

IV.3 Nonlinear control design . . . . . . . . . . . . . . . . . . . . . . . . 111

IV.3.1 First-order sliding mode control . . . . . . . . . . . . . . . 112

IV.3.2 Second-order sliding mode control . . . . . . . . . . . . . . 113

IV.4 Controller implementation . . . . . . . . . . . . . . . . . . . . . . .114

IV.4.1 Model calibration . . . . . . . . . . . . . . . . . . . . . . . 115

IV.4.2 Reducing the sliding-mode chattering . . . . . . . . . . . .122

IV.4.3 Mathematical considerations . . . . . . . . . . . . . . . . . 123

IV.5 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 123

xvi Contents

IV.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128

V Increasing the level of automation in the forestry loggingprocess with crane

trajectory planning and control. 133

V.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134

V.1.1 Literature Review . . . . . . . . . . . . . . . . . . . . . . . 136

V.1.2 General assumptions . . . . . . . . . . . . . . . . . . . . . 137

V.2 Model of the manipulator . . . . . . . . . . . . . . . . . . . . . . . . 139

V.3 Motion planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

V.3.1 Path Planning . . . . . . . . . . . . . . . . . . . . . . . . . 142

V.3.2 Path-Constrained Trajectory Planning . . . . . . . . . . . .144

V.3.3 Time-Efficient Trajectory . . . . . . . . . . . . . . . . . . . 145

V.4 Control Design . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147

V.4.1 Dead-band compensation . . . . . . . . . . . . . . . . . . . 147

V.4.2 Velocity estimation . . . . . . . . . . . . . . . . . . . . . . 148

V.4.3 Individual joint PID controller . . . . . . . . . . . . . . . . 149

V.4.4 Hydraulic control . . . . . . . . . . . . . . . . . . . . . . . 150

V.4.5 Open-loop control . . . . . . . . . . . . . . . . . . . . . . . 150

V.5 Human-machine interface . . . . . . . . . . . . . . . . . . . . . . . . 152

V.6 Experimental Results . . . . . . . . . . . . . . . . . . . . . . . . . . 153

V.6.1 Laboratory Crane . . . . . . . . . . . . . . . . . . . . . . . 154

V.6.2 Commercial Crane . . . . . . . . . . . . . . . . . . . . . . 155

V.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157

V.8 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 159

V.8.1 Installation of Encoders . . . . . . . . . . . . . . . . . . . . 159

V.8.2 Pressure Sensors . . . . . . . . . . . . . . . . . . . . . . . . 159

V.8.3 Chair Signals . . . . . . . . . . . . . . . . . . . . . . . . . 159

V.8.4 Installing Prototyping Hardware into the Cabin . . . . .. . 160

VI Path-constrained motion analysis. An algorithm to understand human per-

formance on hydraulic manipulators. 167

VI.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168

VI.1.1 Literature review related to methods applied for analyzing

performance of forestry operators. . . . . . . . . . . . . . . 170

VI.2 Experimental platform. . . . . . . . . . . . . . . . . . . . . . . . . . 171

VI.3 Problem formulation . . . . . . . . . . . . . . . . . . . . . . . . . . 173

Contents xvii

VI.4 Finding a nominal motion. . . . . . . . . . . . . . . . . . . . . . . . 176

VI.5 Analyzing crane patterns generated by human commands .. . . . . . 182

VI.5.1 Path-constrained motion analysis algorithm . . . . . .. . . 183

VI.5.2 Analysis based on recorded data . . . . . . . . . . . . . . . 185

VI.6 Defining machine settings and reassigning velocity profiles . . . . . . 186

VI.6.1 Suggesting a procedure for customizing machine settings . . 187

VI.6.2 Reassigning the velocity profile. . . . . . . . . . . . . . . . 189

VI.7 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190

VI.8 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192

VI.9 Kinematics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193

VI.9.1 Forward Kinematics . . . . . . . . . . . . . . . . . . . . . . 193

VI.9.2 Computation of inverse kinematics . . . . . . . . . . . . . . 194

VI.10 Data processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

VI.10.1 Extracting the time intervals of trajectories . . . .. . . . . . 195

VI.10.2 Removing offsets in the vectors of time . . . . . . . . . . .195

VI.10.3 Computing the boom-tip Cartesian path . . . . . . . . . . .195

VI.10.4 Spline approximation . . . . . . . . . . . . . . . . . . . . . 195

VI.10.5 Estimating derivatives . . . . . . . . . . . . . . . . . . . . . 196

VI.10.6 Resizing the data set . . . . . . . . . . . . . . . . . . . . . . 196

Bibliography 197

xviii Contents

Part I

Comprehensive Summary of

Contributions

1 Introduction

“Most of the fundamental ideas of

science are essentially simple, and

may, as a rule, be expressed in a

language comprehensible to everyone.”

Albert Einstein

Humans have evolved through history from using rudimentarytools to the use of

highly sophisticated robots. Systems of these kind are making a considerable impact on

many aspects of modern life. From industrial manufacturing, healthcare, entertainment,

human assistance, domestic help, exploration of other planets, and more, we can see how

robots occupy an important place in our modern world (Fig. 1.1).

Although robotics and automation technologies are quite advanced today, they are

still very limited. In most cases robots are designed to accomplish a very specific set of

monotonous tasks in constrained environments to avoid accidents with human workers.

In other cases, robots are designed with some level of autonomy to operate in outdoor

environments through a mixture of human commands and semi-autonomous functions.

However, it is difficult to find robotic systems with full autonomy, resembling the decision

making ability of biological systems found in nature. Therefore, the subject of robotics is

still at its infancy, but we are starting to see all the possible benefits that it can bring to a

variety of industries.

The development of any type of robotic system involves threeimportant aspects:1)

the design of the robot itself, which consists of the mechanics, actuators, electronic hard-

ware, etc.,2) the control algorithms that will ensure that the performance of the robot

goes according to what is expected, and3) planning the desired tasks. Because these are

important subjects in this thesis, some preliminary concepts are provided below.

Thanks to the improvement of processing ability of today’s computers, there are var-

ious aspects of design that engineers are able to manage through computer–aided tools

1

2 1. Introduction

(a) Industrial robot, from ABB (b) PARO, from AIST (c) Groundbot, from Rotundus

(d) HAL-5 exoskeleton, from Cy-berdyne

(e) HRP-4, robot from AIST (f) Robonaut, from NASA

Fig. 1.1: Examples of different types of robots

and simulation technology. This allows to design cost effective solutions and verify their

functionality without the need of having to build a system first. Designing systems in

this form often leads to the need for formulating dynamical models based on physics, ob-

served data, or both, to generate mathematical models that can be conveniently solved by

computer–based numerical methods. Today, there are a lot ofways in which we use this

form of numerical solutions. For example, to design simulators that help to train people,

and also to study the design of different systems such as shuttles, cars, or machines, to

mention some.

In the particular field of control systems, model–based computer–aided design tools

are used for two reasons: to design feedback control algorithms and study their perfor-

mance through simulation tests. Applying this approach in robotics is useful, because this

allows to test different control algorithms and easily transition to real-time implementa-

tion. Understanding the performance of these algorithms isimportant for understanding

1.1. Outline 3

the system’s behavior when executing desired processes, tasks, or motions.

Motion is a behavior that can be planned through a variety of formal methods. To

prescribe the way in which a system will move, be it a vehicle,airplane, legged robot,

manipulator, etc., three important details are needed:

1. the possible path to take,

2. the internal and external constraints such as velocity limitations, obstacles, etc.,

3. the desired requirement; for example, the interest of performing movement in the

least amount of time or with the minimum energy, etc.

To effectively manage the problem of planning some motion, we need to consider the

knowledge of what is really required, so that we can select a particular design that falls

into some specified requirements.

The following thesis presents work that covers many of theseconcepts. In essence,

it presents analytical concepts that can be applied in the subjects of motion/trajectory

planning, motion/trajectory analysis, and control design. Much of this literature is based

on mathematical models that are derived from first principlelaws of physics, as well

as topics that are studied in nonlinear control. Examples ofthese are Euler-Lagrange,

Newtons’ laws, and many more.

One of the characteristics of this work is that it does not concentrate on one example,

but instead, it covers a variety of applications. They fall in the categories of under-actuated

mechanical systems, legged locomotion, industrial machines, and analysis of human abil-

ities. What is peculiar about this work is the method that is used to accomplish all of these

studies. Today, it is known as the Virtual Holonomic Constraints Approach (VHC) [1–3]

and is a procedure that my research group has been developingfor a long time. As it will

be seen, this method can be applied in a variety of challenging research disciplines, as

well as industrial applications.

1.1 Outline

The thesis is organized as follows: The first part is formed bythree chapters containing

an introduction, a summary of contributions, and concluding remarks. The second part is

composed by six papers organized in the following way:

4 1. Introduction

Papers I and II

These two papers study the problems of trajectory planning and control design for under-

actuated systems. In both cases, mathematical modeling, trajectory generation and control

design are presented. The system in paper I is a case of under-actuation degree one, while

the system in paper II is a more challenging example of under-actuation degree two.

Papers III – VI

These papers study an industrial application targeting hydraulic forestry cranes. In paper

III a mathematical model of such machines is derived. Paper IV suggests a framework for

designing non-linear motion controllers that have been verified and tested experimentally.

Paper V presents some insights into the automation problemsof these systems. Finally,

paper VI develops a procedure to analyze the performance of machine operators through

concepts of motion planning techniques.

2 Summary of contributions

“If someday they say of me that in my

work I have contributed something to

the welfare and happiness of my fellow

man, I shall be satisfied.”

George Westinghouse

This chapter contains a brief summary of the contributions presented in each of the

papers from this thesis

2.1 Paper I. Generating Periodic Motions for the Butter-

fly Robot

Many systems in nature are inherently under-actuated, because they have fewer actuators

than the number of degrees of freedom (DOF). Yet, they are able to produce complex

motions by smartly coordinating their movements (see e.g. Fig. 2.1). This characteristic

has inspired the design of different systems used in a variety of applications. Airplanes,

vehicles, vessels, submarines, and satellites, are a few ofthese examples.

In robotics, under-actuation is interesting because it allows designing systems with

fewer actuators than needed. When the mechanical design is planned carefully, this helps

to reduce the weight produced by heavy motors and gears. However, reducing the amount

of actuators also increases the difficulty of controlling the system. This poses different

scientific challenges to the problems of designing controllers for motion control and plan-

ning desired motion.

To study problems of this kind, several researchers in the subject of nonlinear control

have suggested to use simplified versions of complex under-actuated mechanical struc-

tures. Classical benchmark examples are based on pendulum systems among which, the

5

6 2. Summary of contributions

(a) Jugglers (b) Seagull

(c) Jumping diver (d) Seller

Fig. 2.1: Examples of under-actuation in daily life1.

Furuta pendulum [4, 5], the inertia wheel pendulum [6, 7], and the Acrobot [8, 9] are

quite popular. These systems have been studied to analyze challenging control problems

among which, stabilizing the system at an unstable equilibrium or performing a swinging

up maneuver are the most well known [5, 8, 10]. Such studies have helped to elaborate

many fundamental concepts to approach more complex problems, as for example, those

encounter in biologically inspired legged and flying machines.

In recent years, there has been an increasing interest to understand the problem of

dynamic object manipulation, also known as non-prehensilemanipulation. This is some-

thing we experience quite often when we see, for example, a juggler on the streets per-

forming contact juggling, or when we see a waitress carryingglasses on a tray with a

single hand. In fact, maneuvers of this kind require great coordination, a lot of practice,

and they are difficult even for the most skilled.

To study the fundamental concepts of this problem, a group ofresearchers from North-

western University proposed some simplified under-actuated systems. In particular, Prof.

1Sources:jugglers,http://www.scottbot.net/HIAL/?page_id=177seagull,http://andrewchen.co/2013/01/14/confessions-of-a-startup-seagull/diver,http://www.fmn.org.mx/w/ESP/galerias/clavados/clavados/2627seller, http://scotteagan.blogspot.se/2013/09/how-your-conflict-is-solved-will-make.html

2.1. Butterfly Robot (Paper I) 7

Lynch introduced the butterfly robot [11], which is a system with two DOF and one ac-

tuator. It is formed by coupling two plates that form a track where a ball moves freely.

These plates have the shape of a butterfly, hence the name (seeFig. 2.2). This setup is

under-actuated because the coupling is actuated by an electric motor, but the ball has free

motion as a consequence of the movement of the plates and gravity.

Fig. 2.2: Butterfly robot.

Some of the specific scientific challenges that can be analyzed with the butterfly robot

are:

1. Stabilizing the ball around different equilibrium points according to the butterfly

shape.

2. Swinging the ball from its resting position to one of theseequilibrium.

3. Making the ball follow a predefined periodic or finite-timemotion while preserving

its contact with the plates.

4. Catching the ball thrown to the plates.

5. Throwing the ball in planned ways.

6. Catching and throwing the ball.

7. Making the ball bounce ata) a point of the butterfly shape,b) in between different

points, or more radicallyc) among different butterfly systems.


8. Doing all of the above with more than one ball.

Although this list is not exhaustive, most of these challenges still remain, because

the problem of having an under-actuated link and non-holonomic mechanics makes them

quite challenging in both theory and practice. Most of thesechallenges can be treated as

the essential problem of controlling motion under constrained contact forces, and limited

control inputs.

To the best of our knowledge, two approaches have been proposed for this particular

setup:a) an optimization-based motion planning under proportionalderivative (PD) feed-

back control [11], andb) passivity based control [12, 13]. The former shows a peculiar

solution done to rotate the plates180 degrees and roll a disk to the other side without los-

ing contact (a disk is used instead of a ball). The latter considers the swinging-up problem

with a subsequent stabilization of the ball.

The article I am presenting for this example intends to propose a solution to the third

problem, which consists of designing periodic movement that resembles a permanent

juggling motion. However, readers will realize that a variation of similar method can

also be used to perform any desired finite movement. To understand the problem of

controlling motion, it can be imagined that to move the ball in some desired way we have

to smartly maneuver the plates to produce such a movement. This involves a combination

of planning how the butterfly is going to move to produce a natural, graceful motion of

the ball and the way such behavior is kept permanently stablethrough feedback control.

Although the material in this article is new, it comes from theory and concepts that

have been an ongoing development within my research group [1, 14] and have shown to

be quite successful in both theory and practice [15–18]. On the basis of these concepts,

we suggest to select VHC to specify the way in which the butterfly will move, and find

analytically all possible trajectories that are feasible for the ball to follow while keeping

contact with the plates. This solution has a number of advantages in comparison to other

approaches, because, most of all, it is analytical. This fundamental characteristic makes

it simple to apply and derive explicit solutions, rather than running extensive numerical

optimization search.

In summary, this article presents the following:a) a complete system modeling in-

cluding the calculation of reaction forces,b) a step-by-step procedure for selecting VHC

that allow planning feasible ball trajectories, andc) an exemplified approach for control

design of mechanical systems with under-actuation degree one.

The results presented are based on simulation studies underMatlab/Simulink. To this

end, we present two types of periodic motions that depend on the placement of the but-

2.2. Three-link Planar Biped Walker (Paper II) 9

terfly plates and are depicted in Fig. 2.3 and Fig. 2.4. These results show how the ball

exponentially converges to the desired cycle from different starting positions. This proce-

dure can be extended to design more sophisticated motions, e.g. perpetual revolutions of

the ball or trajectories to move the ball from the horizontalposition to the other end, etc.

(a) (b) (c)

Fig. 2.3: Butterfly juggling around its horizontal position

(a) (b) (c)

Fig. 2.4: Butterfly juggling around its vertical position

2.2 Paper II. Design of energy efficient walking gaits for

a three-link planar biped walker with two unactuated

degrees of freedom

In the particular field of biped robotics, or legged locomotion in general, there exists a

substantial amount of work devoted to understand the way biologically inspired walking

or running machines can be designed and controlled. Unlike traditional robotics, this is

also a research area that has grown aside from the standard field and today incorporates

elements of different subjects that include bio-mechanics, motion capturing, bio-inspired

mechanical engineering, prosthetic design, just to mention a few. Because of this reason

certain terminology can be quite different to what is known in traditional robotics or con-

trol system engineering. For instance, while it is often common to call motion/trajectory


planning to the task of specifying the way a given system willmove, for research in legged

locomotion, this is often referred to as gait synthesis. Theterm gait expresses a behavior

that mathematically corresponds to a limit cycle or periodic trajectory. It can be designed

empirically or by observing data recorded with motion capture systems. Likewise, unlike

formal control system terminology, some authors use the term stability to refer to dynamic

balance during motion, and the actual analysis of stabilitymight require more concepts

than that of Lyapunov’s stability theorems. In the text below, some of this terminology

will be adopted, and will also be read in the article supporting this section.

As pointed out, different methods exist to approach the problem of legged locomotion.

Today, they have been roughly classified into two categoriesand these are described in

literature as static and dynamic walking.

In the case of static walking, a conventional approach consists on designing robots

with nearly rigid links, motors and gearboxes. Applying concepts of traditional feedback

control, it is possible to make the robot’s body to stay standing upright keeping dynamic

balance. To design such controllers, we need to make the assumption that the robot’s

body can be reduced to a3D pendulum for simplifying the modeling complexity [19].

Applying high-gain feedback control that produces high joint torques, the controller can-

cels out the natural dynamics of the machine and the links canstrictly follow some desired

trajectories. This can result in behaviors that are similarto walking, but they can be un-

natural compared to their biological counterpart. This approach is widely known as the

zero-moment point (ZMP) method and it should be noted that despite the limitation that

the motion has to be carefully synthesized to preserve the ZMP condition, this method has

remained for a long time the only procedure for biped gait synthesis and control. Today,

this concept has been involved in diverse applications related to numerous anthropomor-

phic locomotion mechanisms of different degrees of complexity, such as ASIMO from

Honda [20], who resembles an astronaut walking with a space suit (see Fig. 2.6(a)). In

setups like ASIMO, the motions are carefully planned, very smooth, but highly unnatural.

Today, most of the biped mechanism joints are powered and directly controlled except

for the contact between the foot and the ground, which can be considered as an additional

passive DOF. The foot cannot be controlled directly, but indirectly by ensuring the ap-

propriate dynamics of the mechanism above the foot. Therefore, the trend of dynamic

walking is more recent and has been inspired on the understanding of these basic charac-

teristics of biped locomotion: (i) the possibility of rotation of the overall system about one

foot, which is equivalent to the appearance of an unpowered (passive) DOF, (ii) gait re-

peatability (symmetry), and (iii) natural interchangeability of single- and double-support


phases. Based on these concepts, in 1990 Tad McGeer [21] showed how a planar mech-

anism with two legs could dynamically walk stably down a shallow slope with no other

energy input or control than gravity alone2. This system acts like two coupled pendu-

lums, where the stance leg acts like an inverted pendulum andthe swing leg acts like a

free pendulum attached to the stance leg at the hip. Given sufficient mass at the hip, the

system will have a stable limit cycle, which is a nominal trajectory that repeats itself and

will return to this trajectory even if perturbed slightly. An extension of the two-segment

passive walker is to include knees (see Figs. 2.5 and 2.6(b)), which results in human-like

walking behavior3.

swing leg

pendulum

stance leg

inverted

pendulum

push-o�

trunk

velocity body

weight

support

collision

Dynamic Walking

Fig. 2.5: Passive dynamic walking4.

Mechanisms that are able to walk dynamically without the need of control input are

highly efficient in terms of energy consumption. Therefore,today this interesting concept

is studied deeply and classical research examples are the rimless-wheel [22], the compass-

biped [23], the kneed-biped [24], and different version of the compass-biped with upper

torso [25]. Apart of energy efficiency, the phenomena that attracts researchers to this

idea is the human-like walking behavior produced by these systems5. Most importantly,

all of these characteristics are the result of purely natural dynamics, which motivates the

importance of how the mechanisms and actuation of legged machines should be designed

to make more graceful and energy efficient legged machines.

From the theoretical aspects, authors of [21, 26–29] have laid concrete, fundamental

concepts to understand modeling assumptions, gait synthesis, and simulation examples of

2A video describing the ideas behind passive dynamic walking by the man who first discovered the idea: TadMcGeer.https://www.youtube.com/watch?v=WOPED7I5Lac

3A video showing this setup can be seen inhttps://www.youtube.com/watch?v=CK8IFEGmiKY4Source:

http://dyros.snu.ac.kr/concept-of-passive-dynamic-walking-robot/5A video comparison in between the gait of a passive kneed-walker and a person can be seen inhttp:

//drei.mech.nitech.ac.jp/~sano/contents/biped/Movie_W04.mpg


passive walking behaviors. This has been greatly important, because this has opened up

the possibility to expand the domain of passive walkers and make systems walk on flat

surfaces and through rough terrain by letting them move by their own natural dynamics.

To accomplish this, recent designs combine active and passive joints, which result in

under-actuated systems. Some examples are Flame6, the Rabbit robot [30], MABEL

robot [31], MARLO [32] and most recently, the line of robots from Boston Dynamics

[33]. Some of these examples are impressive machines that are able to walk dynamically

even without feet.

(a) ASIMO, image courtesy Honda[20]

(b) Passive walker, image courtesyNagoya Institute of Technology

(c) Petman Robot, image courtesyof Boston Dynamics [33]

Fig. 2.6:3 Examples of different types of walking robots7.

Conceptually, the trend of robots that are designed with passive dynamics fall in the

class of under-actuated mechanical systems, because not all DOF are directly controllable.

As presented in [21, 34], dynamics of these systems can be modeled applying concepts

of classical mechanics, which particularly involve Newton’s laws, Euler-Lagrange, and

impact events. This combination yields discontinuous dynamics, because it is considered

that the robot’s foot impacting the ground at the end of a stepcan be treated as an impulse

effect (see Fig. 2.5). In the case of running, this can also incorporate a short flying time,

6A video of the dutch robot Flame can be seenhttps://www.youtube.com/watch?v=7JU_zQkVOiI

7Sources:ASIMO, http://asimo.honda.com/ASIMO_DCTM/News/images/highres/ASIMO_one_hand_up.jpgBlue biped,http://drei.mech.nitech.ac.jp/~fujimoto/sano/movie/BlueBiped.jpg


which happens when both feet are off the ground. This form of dynamical systems falls

in the class of hybrid dynamical systems, which present various scientific challenges:

• performing gait synthesis given under-actuated hybrid dynamics, which is related

to the problem of trajectory planning and consists of designing stable hybrid-limit

cycles, either for fully passive systems, or under-actuated ones,

• designing robust controllers that can ensure the walking stability given perturba-

tions and unmodeled dynamics,

• analyzing stability of walking behaviors, which consist onverifying the stability of

a gait either with passive or active control,

• walking on uneven terrain by adapting the gait according to obstacles, which could

also consists on modifying the control law parameters adaptively,

• repeating the same for running.

Although this list is not exhaustive, it reflects some of the concepts studied in dynamic

walking. Today, most of these items are still challenging topics and proof of this has been

publicly observed during the latest DARPA ROBOTICS CHALLENGE in December of

2013 [35]. In this event, some of the most advanced humanoid robots were presented by

highly ranked institutions that include MIT, CMU, KAIST, NASA and many more. All of

these robots showed the difficulties found in today’s technology to perform simple human

tasks such as walking on uneven terrains8. Many of them lacked the ability to finish the

walking assignments and most of them could perform this taskonly by being remotely

controlled. However, there exists a number of robots that did not participate in this event,

as it is the case of a family of hydraulically-actuated legged robots, such as BigDog,

LS3, WildCat, Petman (see Fig. 2.6(c)) and ATLAS9, all belonging to Boston Dynamics

[33]. In video examples shown by this company, both BigDog10 and LS311 show the

outstanding ability to dynamically balance and walk in unplanned natural rough terrain

environments, which is also true for the biped version ATLAS12. While these robots are

8A video of the challenge related to walking in uneven terraincan be seen inhttps://www.youtube.com/watch?v=RgG1GCgShZQ

9while ATLAS was provided as a platform for the DARPA challenge, it was never operated by its designerBoston Dynamics

10A video showing BigDog’s abilities can be seen inhttps://www.youtube.com/watch?v=W1czBcnX1Ww

11A video showing LS3 abilities can be seen inhttps://www.youtube.com/watch?v=hNUeSUXOc-w

12A video of ATLAS walking on uneven terrain can be seen inhttps://www.youtube.com/watch?v=WYKgHa8hH1k


clearly impressive machines and raise the bar of what is achievable today, many research

questions remain unanswered, since details of neither the design, nor gait synthesis, nor

their control aspects are available to the research community at large.

In contrast, another family of robots pioneered by Prof. Jessy Grizzle, such as Rab-

bit [30], MABLE [31], and MARLO [32], have shown how fundamental analytical math-

ematical concepts in the subject of VHC can be used to achievehighly dynamic walking

and running behaviors exploiting under-actuated dynamics13. The work done with these

machines has been reported not only in research articles, but books [36], Master and PHD

theses, all belonging to public domain. In this literature we can see the advantages of

VHC for being a method that offers systematic and nearly analytical procedures for gait

synthesis and feedback control design. This is particularly true for the case of legged

mechanisms with one passive link, such as point-feet, wherethis method can be followed

as a recipe during development.

In relation to VHC, the work of my supervisor and research group has contributed

substantially to the development of the theory behind this approach. Years of research

have culminated on a number of publications, and one of them stands out for being a

generalization of the VHCs approach for under-actuated dynamical systems with impulse

effects [14, 37, 38]. This theory can be directly applied in many applications, including

the subject of legged locomotion.

In most cases, the work of [1] and [30] can be essentially treated similarly. However,

they present fundamental differences related to the way controllers are designed, and the

way stability is analyzed. Informally, for control design the work of [30] applies a proce-

dure of standard feedback linearization with PD control, and the stability of the resulting

closed-loop system is analyzed based on Poincaré maps. Thisis decisive for demonstrat-

ing exponential orbital stability of a limit cycle. On the other hand, the work of [1] is

based on concepts of transverse dynamics [14], which is a more concise mathematical

approach to analyze stability of limit cycles (gaits), as well as for estimating the region

of attraction and possible deviations from nominal trajectories in response to small para-

metric perturbations. Control systems designed accordingto transverse dynamics have

superior performance and robustness, as they are able to guarantee global exponential

orbital stability.

To provide an insight of how VHC can be used in biped locomotion for systems with

many passive links, the work presented in this article considers a reduced planar model of

a human body, i.e. a torso and two legs, Fig. 2.7. This system has three characteristics:1)

13Video examples of these machines walking and running can be seen inhttps://www.youtube.com/user/DynamicLegLocomotion


point feet,2) one actuator in between the legs, and3) a passive torso maintained upright

by springs. Hence, the dynamics of this model considers a case of under-actuation de-

gree two, and the resulting mechanical characteristics resemble the working principle of

the robot MARLO [32]. One of the main challenges behind this system consists on for-

mulating a gait synthesis algorithm (trajectory generation), and the control design having

two un-actuated links. This example has been originally inspired on the work of [27,28],

where the authors attempt to find passive walking gaits. Unlike that case, our attempt is to

make this mechanism walk on flat surfaces. To this end, an initial solution was proposed

earlier by my research group in the work of [39]. My purpose isto present an alternative

solution, which I believe makes the VHC approach easier to apply.

Fig. 2.7: Schematics of the biped in the sagittal plane and level

From the theoretical aspect, this example presents some of the following fundamental

challenges:

1. Finding passive gaits while walking down slopes.

2. Walking on flat ground.

3. Walking on uneven terrain by adapting the gait according to the obstacles.

4. Repeating the same for running.

This article presents solutions to some of these challengesand particularly:


• The way gait synthesis can be formulated to apply VHC in under-actuated mechan-

ical systems with more than one passive links. To this end, wealso incorporate

concepts of optimization to involve energy efficiency.

• The way concepts of transverse dynamics can be applied, including the procedure

we use for deriving it.

• The way concepts of linearization of transverse dynamics can be used for designing

robust feedback controllers.

• The way simulation methods (see Fig. 2.8) can be used to analyze performance

under modeling uncertainty, external disturbances, etc.

(a) (b) (c) (d) (e)

Fig. 2.8:3-Link planar walker biped simulation

2.3 Papers III-VI. Hydraulic manipulators

In our daily life we find hydraulic systems all around us. Theyare so fundamental to our

world, but we hardly notice it. Nevertheless, hydraulic technology provides the muscles

to lift, steer, build and mine resources that power countless industries. Whether these

systems are used for transportation, construction, heavy duty, robotics, process industry,

or entertainment, our industrial world depends heavily on hydraulics.

The greatest example of brute strength that hydraulic technology can deliver is found

in the heavy-duty machine industry. Over the years the excavators, cranes and trucks

that have helped building our modern world have grown exponentially in size, achieving

unmatched efficiency empowered by hydraulics. Several of these machines are equipped

2.3. Hydraulic manipulators (Papers III-VI) 17

with hydraulic manipulators that work as large scale arms tohandle heavy loads and can

be seen in excavators, loaders, and cranes, just to mention afew.

Cylinders dominate the market in the hydraulic industry, because they represent a

robust component for converting the hydraulic energy into workable energy, allowing to

make well controlled back-and-forth motions. In heavy-duty machines, they are used as

the main actuator for suspension control and to move different DOF.

Most heavy-duty machines are formed by three main components: the cabin, the ve-

hicle and the manipulator. The cabin is highly important because it is used to comply with

ISO safety regulations, which involve protecting the operator from the working environ-

ment’s dangers. Therefore, most of the technology used for controlling these machines is

embedded in the cabin, and includes seats, computers, embedded processing units, key-

boards, joysticks, levers –all of which result in a complex human-machine interaction.

Levers or joysticks are quite relevant to this thesis, because they are the primary tools

used to directly command proportional valves, either mechanically or electrically. These

valves are fundamental for the control of the manipulators,because they regulate how the

hydraulic oil flows to produce the movement of the actuators.Operating machines in this

mode is a standard in many industries, and despite the difficulties associated with it, this

has prevail as the primary approach for decades.

When we look at different hydraulic manipulators, we can recognize standard designs

that are found in industrial robotics. For instance, it is common to see designs based

on parallel or serial kinematics where each DOF is controlled by one hydraulic actua-

tor. Despite differences in the mechanical design concepts, most machines are operated

according to the same principle, since each DOF has to be manually controlled either

by a joystick or a lever. Thus, the complexity of controllingthe system becomes obvi-

ous, as the operator needs to coordinate each DOF to positionthe end-effector in desired

locations. Since these manipulators are often designed with more than four DOF, this op-

eration involves a high redundancy problem. To this we have to include the simultaneous

problem of coordinating the vehicle and end-effector’s tools, which all result in the need

for advanced motor skills, multitasking, and concentration, that are highly demanding

both mentally and physically.

When we involve open-loop manual control, the operator is given the full responsibil-

ity for providing control inputs that cause the machine to complete a given task optimally;

For example, in minimum time or with the least energy consumption. However, the op-

timal control solutions of even very simple nonlinear systems are not intuitive and may

be beyond the capabilities of the human operator responsible for providing the control


input. As a result, machines controlled manually through open-loop commands are being

operated sub-optimally.

Sensing technology is the fundamental component needed fordesigning feedback

control that can be used for making autonomous decisions to relieve some of the oper-

ators’ tasks. However, in today’s hydraulic machinery the absence of sensors restricts the

possibility to introduce computerized support, as there isno indication of how the ma-

chine is operating. Because of this reason, even computerized decision-making tools are

missing, which restricts the possibility to provide working suggestions to the operator.

Consequently, estimating the efficiency of different processes is difficult, which results

in the absence of fundamental understanding that can be usedfor improving optimality,

training, and working efficiency.

Companies involved in training on virtual reality simulators offer solutions that allow

operators to attempt learning optimal ways to work with machines. This is often done to

reduce the environmental consequences of learning with real machines. However, to this

day, there are no clear methods of training or working with machines that are based on

fundamental understanding of motion/task optimization. This is a subject that is highly

developed in the robotics community and can be of great benefit to the hydraulic machine

industry. To this we should also add all the advantages of introducing all the advanced

motion control technologies and autonomous robotic functionalities that we find in many

other industries.

There are different driving forces that motivate the development in this thesis. Among

others, I see the need for developing smart machines to satisfy the increasing demand of

natural resources and infrastructure as one of the most important. One of the reasons is

the world’s exponentially increasing population that willchallenge the supply of many

fundamental elements such as housing, heating, electricity, and food, which are problems

that we are already facing today. These concerns are shared by many political entities. As

an example, I present below two paragraphs that have been extracted from the European

commission’s website [40]:

“The European Commission has today adopted a strategy to shift the European economy

towards greater and more sustainable use of renewable resources. With the world popu-

lation increasing by 40% by 2030, approaching9 billion by 2050 and natural resources

finite, Europe needs renewable biological resources for secure and healthy food and feed,

as well as for materials, energy, and other products.”

“Europe needs to make the transition to a post-petroleum economy. Greater use of renew-

able resources is no longer just an option, it is a necessity.We must drive the transition


from a fossil-based to a bio-based society with research andinnovation as the motor. This

is good for our environment, our food and energy security, and for Europe’s competitive-

ness for the future.”

Many machine manufacturers are well aware of these challenges, but they are also

aware of the difficulties found with the introduction of semi-automated technologies to

the dominating working generation. Despite this, many companies have been investing

on research for more than a decade in topics related to control systems, robotics, and au-

tomation. Today, as we are approaching a younger generationthat is prone to technology,

it is becoming increasingly important to introduce new products into the market to keep

younger customers interested, ease the learning process, and improve efficiency. Proof

of this is the recently released technologies that involve machines with embedded sen-

sors, computer controlled cranes, voice-activated control, (semi)autonomous navigation,

advanced Human-Machine Interfaces, Head-up displays, automatic data analysis, etc, see

Fig. 2.9. Many of these technologies are found in industriesrelated to mining, agriculture,

oil platforms, just to mention a few. However, they are missing in many other important

areas, as it is the case of the forestry industry.

(a) Loader with analog sensors (b) Excavator with wireless sensors

Fig. 2.9: Recent embedded sensing technology for hydraulicmachines14.

In terms of research and development, a lot of interesting research results in the in-

troduction of automation in several hydraulic machines which can be found in excava-

tors [41–47], loaders [48–50], handlers for construction of montage buildings [51], and

forestry cranes [52–56].

14Sources:Loader,http://www.ivtinternational.com/news.php?NewsID=52748Excavator,http://g-nestle.de/en/product/topcon-x-22/


The work found in this thesis adds some further insights intothe development of au-

tomation technology for forestry machines, and in particular, forestry cranes (see Fig. 2.10).

These are hydraulic machines that are used in a variety of assignments related to wood,

and forest biomaterial extraction, which are important renewable natural resources used

to produce several products, such as housing, electricity,heating, and more. Although

forestry machinery is quite similar to most heavy-duty machines, it present some impor-

tant differences:

• The suspension system and moving vehicle are designed to access highly dynamic

and rough terrain, which is often found in deep forest, and mountains. Because of

this reason, even military tank manufacturing companies are involved in the pro-

duction of forestry machine bogies.

• Cranes are often designed with at least one prismatic link toregulate the crane’s

length. This is used to avoid hitting the cabin, and reach a larger radius of operation

without needing to move the vehicle.

• The hydraulic circuits applied to these machines are not standard, and each manu-

facturer has a different way to treat the design. In many cases, this information is

private and represents a difficulty for performing research.

• Some forestry machines, particularly the harvesters, are recognized as some of the

most difficult machines to operate, and are highly comparable to helicopters.

The concept of designing autonomous services for the forestindustry has been on-

going since the1980s. For example, laser pointer assisted motion control of a forestry

crane was implemented and tested in [57]. Similarly, principles of interactive robotics for

forestry cranes were presented in [52]. Nevertheless, these premature solutions made no

impact for commercialization, since they were, at that time, too costly to be produced.

Today, the situation is different. Embedded processing units and sensing devices have

become more mature and accessible in terms of size and price,allowing to consider com-

plex solutions for motion control of forestry cranes [54]. Additionally, social factors, such

as urbanization, long learning period, and the increasing difficulty to attract or recruit new

operators, are making automation increasingly relevant. Therefore, forestry companies

have been recently embracing the overall benefits that robotics and automation can bring

to this industry [58–62].

Certainly, the scenario of full automation of these machines is unrealistic today, but

considering semi-autonomous computerized solutions to simplify the operation it is clearly


Fig. 2.10: Forwarder machine used in the forest industry forlogging operations.

interesting and feasible soon. However, experience has shown to me that transporting

methods found in the robotics and control literature into the forestry industry is easier

said that done, because there exists various theoretical and practical challenges that are

still unresolved. For instance, if we name the three fundamental subjects of system mod-

eling, identification and control design, we see that it is difficult to generalize a method

and apply it to an entire family of machines. Some of the reasons are the following:

1. Each degree of freedom in a forestry machine is controlledaccording to a differ-

ent hydraulic configuration. Thus, modeling the hydraulic systems can be quite

challenging.

2. When designing the hydraulic circuits it is thought that some links of the manipu-

lators are used for motion control, as well as for suspensionpurposes when lifting

heavy loads. This introduces complex flexibility phenomena, which reduce the

possibility of performing precise motion.

3. The load carried by a forestry crane can overpass its overall weight, which intro-

duces huge variations in the system parameters.

4. Although mechanically these systems might look alike, the design of the hydraulic


circuitry changes greatly from machine to machine.

5. The changes in temperature and weather conditions can alter the dynamic perfor-

mance of the cranes. One example of this is related to the changes in friction forces

during winter and summer.

Apart of these problems, we can also add some of the practicalones found in today’s

commercial machines:

1. Low processing capacity to implement control algorithms, since current machines

simply use a power amplifier to convert the joystick commandsinto control inputs

for the valves.

2. Absence of cranes with sensors, which are fundamental to design feedback con-

trollers.

3. Absence of visual technology to perform semi-autonomousnavigation.

4. The need of reliable embedded codes that can be easily integrated into the main

system without complete understanding of the programmers.

5. Absence of global position sensing needed to locate the machine in the forest.

In relation to automation and robotic motion control, thereare a couple of ideas that

are interesting to this industry, because they can facilitate the work, ease the learning

process, and increase efficiency. Some of these ideas are thefollowing:

1. To control the crane boom-tip directly instead of controlling the individual links,

which can reduce the amount of joystick inputs needed and work with the machine

more intuitively.

2. Performing autonomous motions for lifting logs, which can be used in machines

used for log-collection.

3. Performing autonomous motions for dragging and cutting trees, which can be used

in machines used for harvesting.

4. Teaching autonomous motions to the machine, such that both crane and vehicle are

able to repeat these motions.

5. Learning from experienced operators, such that similar data is used for training

beginners.


6. Navigation through pre-selected paths.

7. Automated working planning.

Although some of these scenarios can partly be solved today,by introducing sensors

and better processing capacity to machines, several of themrequire further development

in other areas. For instance:

1. It is difficult to find computer vision solutions that can easily transition to use in

forestry.

2. GPS-like technology for the forestry brand does not existtoday.

3. Autonomous navigation planning requires information about forest terrain, location

of trees, wet-surfaces, water streams, just to mention a few.

4. It is difficult to place sensors, since some machine parts are prone to collisions and

external damage, provoked by the working conditions.

5. To interact with a robotic machine the Human-Machine Interface needs to be up-

dated in order to cope with a diverse class of new functionalities.

6. Autonomous data analysis that is performed either offlineor in real-time, such that

the operator is able to receive feedback about his own work with suggestions on how

to improve it. This can also be used to make autonomous adaptation of parameters

without the need of human intervention.

7. Autonomous data analysis, that is performed either offline or in real-time, such that

simulators can provide feedback to the user about his own work, with suggestions

about how to improve it.

All the lists presented above are just examples of research areas derived from the

analysis of forestry automation. This shows the complexityof the subject and the rich

amount of solutions that can be derived from it, which have direct applications in other

areas. The solutions found in the papers of this thesis, related to this topic, cover a wide

variety of these problems and they can briefly be categorizedaccording to the following

subjects:

1. Modeling, in which I describe the mathematics needed to model forestry cranes,

presented inPapers III and IV .


2. Control Design, in which I present some of the results found by implementingboth

linear and nonlinear controllers. This is found inPapers IV and V.

3. Motion and trajectory planning, in which I present both theory and practice for

implementing ideas of semi-autonomous motions. As it will be inferred from these

articles, the main approach used for this task is based on VHC, which is an effi-

cient method for designing optimal movement. This approachhas its roots in the

development done by my research group, and has some relations with the method

widely known aspath constrained trajectory planningused extensively in robotics.

The mathematical foundations of this approach can be seen inPaper V, and some

of the applications will be seen inPapers V and VI.

4. Understanding the working methods and operators efficiency, in which I con-

sider one of the applications of our trajectory planning method for understanding

how operators work, and how this could be improved. To the best of my knowl-

edge, this is the first article attempting to understand how forestry operations could

be analyzed through motion data. This information is highlyrelevant to different

industries, and can be applied in a variety of forms, including machine learning,

online trajectory planning, autonomous adaptive control parameter configurations,

and design of simulators that provide feedback information, just to mention a few.

All of this is explained in detail inPaper VI.

3 Concluding remarks

“I am turned into a sort of machine for

observing facts and grinding out

conclusions.”

Charles Darwin

I have been working with the VHC approach for quite some time.In my opinion, this

approach is undoubtly a valuable tool for motion planning and control of different type

of mechanical systems, and in particular, under-actuated ones. In addition, I have found

applications outside the domain of under-actuated systemswhere this approach is very

useful to design optimal motion performance requirements.This is always needed in the

subject of robotics, since systems cannot be designed with infinite capabilities. Examples

of these are the motion planning and analysis of mechanical manipulators that are used in

a large portion of industry.

Although VHC can work as a recipe for a variety of problems, I see that there are quite

many challenges ahead that need further research. Among these challenges, the following

are of great importance:

1. Real-time adaptation. For instance, in the case of leggedlocomotion, it is unrealis-

tic to believe that robots would only walk on flat surfaces with small disturbances.

In reality, we would expect robots to walk in a variety of terrains with the need to

adapt their gaits according to obstacles and terrain. Therefore, motion planning and

controller design should be able to adapt according to theseunknown situations. In

current VHC implementations, trajectories are planned by solving differential equa-

tions that describe the reduced dynamics of the system, and transverse linearization

is used for controller design by solving a time-variant Riccati equation. Perform-

ing these operations in real-time is unfeasible today, and requires great advances in

computational numerical methods.

25

26 3. Concluding remarks

2. Collaboration with industry is quite challenging for researchers, since industry has

a different expectation of experimental results than thoseresearchers have. Often

industrial engineers are unfamiliar with modern nonlinearcontrol methods and it

is difficult for them to judge their advantages over simpler approaches. This is

particularly true for those industries that are recently adopting feedback control

system technologies. Therefore, simpler methods for practical implementation of

VHC should be applied when initiating this form of cooperation.

Despite these challenges, the rich mathematical concepts of VHC make this method

quite friendly. This is particularly true when a person has become familiar with this

approach. Nevertheless, there are cases that could challenge most approaches, including

VHC. For instance, Fig. 3.1 presents some problems derived from the butterfly robot,

that I believe some readers will find interesting. These sketches address challenges with

under-actuation degree higher than one, that could perhapsbe unsolvable1

(a) (b) (c)

Fig. 3.1: Sketches of butterfly-like challenges.

In a similar way, I consider that studying the problem of legged locomotion in the

sagittal plane is quite important to understand walking characteristics. However, if we

intend to design robots able to operate naturally, studyingthe problem in all dimensions

is highly relevant. Nevertheless, we often believe that machines are supposed to look like

some biological system, such as bipedal creatures. I believe that designing robotic legged

systems that are able to move safely and highly efficient according to their own dynamics

1Since there is no website for the work in this thesis, I will make a couple of things available to the readers.For example, the Solidworks models of the sketches presented here have been tested with 3D printers and lasercutting techniques, and they can be requested through email.Additionally, the algorithms presented can alsobe requested, and they have been tested in low-cost hardware, such as Arduino UNO, MEGA, and Due. Thecomputer vision software needed for implementation can also beavailable under request. All of the softwarehas been implemented with MATLAB/Simulink, including the solution for the planar biped model.

27

is more relevant, even when their motion behavior is unnatural. This can be achieved by

designing mechanisms with variable topologies. Some pioneering examples of this kind

are the12-TET robot from NASA, and the Virginia Tech’s IMPASS, to mention a few.

After working with the problem of automation in heavy-duty machines, I find many

open questions that need further research. Some of these arelisted below.

1. When we have a good understanding of forestry operations and the way experienced

operators handle the machine, it is feasible to design a new family of simulators able

to prepare beginners more efficiently. This can be done through methods presented

in this thesis and, in particular, applying the algorithms proposed in paper VI. These

type of simulators could also be used for several other purposes, e.g.a) to design

scientific methods to teach operators,b) to involve gamification with the aim of

challenging operators and improve their working performance. One example is the

standard self-competition against a simulated ghost representing the optimal way

of operation (see Fig. 3.2).

2. Cabins, at the moment are filled with buttons and joysticksand it seems inefficient

to add more buttons or joysticks, since this can complicate the work. If automa-

tion is incorporated in heavy-duty equipment, new human-machine interfaces will

be needed. This will be important to select among different automated tasks and

functions.

3. I have not covered anything related to the problem of computer vision or navigation.

However the use of cameras, radars, Lidar systems, can help to automate grasping

tasks, as well as autonomous navigation. These are basic technologies required to

automate large portions of the process and to reduce furtherthe necessity of high

human experience.

4. Systematic methods for designing controllers that can autonomously adapt to dif-

ferent types of cranes.

5. Mathematical models describing dynamics of the completeforestry machines, i.e.

cabin, manipulator and vehicle. As these systems are coupled, each component has

a strong effect on the remaining parts.

6. Considering that mathematical optimization is a mature subject in different areas,

designing cranes can be done differently today. Optimization algorithms could lead

to designs that are naturally efficient, unlike the empirical designs found in most

industries. This could lead to machines that instead of one crane are equipped with

28 3. Concluding remarks

a pair of cranes that work through autonomous decisions and managed by advanced

human-machine interfaces.

7. Human-Machine collaboration, and Machine-Machine collaboration is a topic that

will be needed to automate the work among machines.

Fig. 3.2: Gamer competing against his ghost that performed the fastest lap2.

2Source:https://www.lfs.net/attachment/3715

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