Soft Robots for Healthcare Applications: Design, Modeling, and Control

238 Pages·2017·9.666 MB·English

by  Shane Xie, Mingming Zhang, Wei Meng

HEALTHCARE TECHNOLOGIES SERIES 14
Soft Robots for
Healthcare Applications
Othervolumesinthisseries:
Volume1 NanobiosensorsforPersonalizedandOnsiteBiomedicalDiagnosis
P.Chandra(Editor)
Volume2 MachineLearningforHealthcareTechnologiesProf.DavidA.Clifton(Editor)
Volume3 PortableBiosensorsandPoint-of-CareSystemsProf.SpyridonE.Kintzios
(Editor)
Volume4 BiomedicalNanomaterials:FromDesigntoImplementationDr.Thomas
J.WebsterandDr.HilalYazici(Editors)
Volume6 ActiveandAssistedLiving:TechnologiesandApplicationsFlorez-Revuelta
andChaaraoui(Editors)
Volume9 HumanMonitoring,SmartHealthandAssistedLiving:Techniquesand
TechnologiesS.Longhi,A.Monteriu´ andA.Freddi(Editors)
Soft Robots for
Healthcare Applications
Design, modelling, and control
S. Xie, M. Zhang and W. Meng
The Institution of Engineering andTechnology
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Contents
Preface ix
Acknowledgements xv
AuthorBiographies xvii
1 Introduction 1
1.1 Healthcare requirements 1
1.2 Soft robots for healthcare applications 5
1.2.1 Definition of soft robots 6
1.2.2 Examples of soft robots for healthcare 8
1.2.3 Motivation of soft robots for healthcare 11
1.3 Critical issuesin developing soft robots for healthcare 12
1.3.1 Acceptance of healthcare robots 12
1.3.2 Soft actuators 13
1.3.3 Modelling and control of soft actuators 17
1.4 Bookoutline 17
1.5 Summary 18
References 18
2 State of the art 23
2.1 Rehabilitationrobots for healthcare 23
2.1.1 Upper-limb rehabilitation exoskeletons 23
2.1.2 Gait rehabilitationexoskeletons 25
2.1.3 Ankle rehabilitationrobots 27
2.2 Soft robots for healthcare 32
2.2.1 Soft robots forvarious applications 33
2.2.2 Soft robots forhealthcare 36
2.3 Summary 43
References 43
3 Concept andmodelling of asoftrehabilitation
actuator:the Peano muscle 49
3.1 Towards softand wearable actuation for rehabilitation systems 49
3.2 Fluid powered muscles for rehabilitation 51
3.3 Static modelling of the Peano muscle 53
3.4 The MECHALPstatic model 55
3.4.1 Model validation method 59
3.4.2 Model validation results and discussion 61
vi Soft robots for healthcare applications
3.5 Summary 63
References 63
4 Designof the reactive Peano muscle 67
4.1 Actuators that sense 67
4.1.1 Prior art inembedded sensorsfor linear fluid
poweredmuscles 68
4.2 The reactive Peano muscle 71
4.2.1 DE sensors 71
4.3 Fabrication of the reactive Peano muscle 73
4.4 Characterising the reactive Peano muscle 78
4.4.1 Methods 78
4.4.2 Muscle performance results and discussion 78
4.4.3 Sensor performance results and discussion 79
4.5 Summary 83
References 83
5 Softwrist rehabilitation robot 87
5.1 Introduction 87
5.2 Device design 88
5.3 Force and torque distribution 91
5.4 Control strategies 92
5.4.1 Pneumatic setup 92
5.4.2 Model-based control 93
5.4.3 Feedback-based control 95
5.4.4 Design comparison 98
5.5 System integrationand experiments 100
5.5.1 Software architecture 100
5.5.2 Experiments 101
5.6 Summary 105
References 105
6 Development of asoftankle rehabilitation robot 107
6.1 Ankle complex 108
6.2 Existing ankle rehabilitation robots 109
6.3 Design requirementsof ankle rehabilitation robots 111
6.3.1 Ankle range of motion and torque 111
6.3.2 Robot flexibility 111
6.4 Conceptual designof the soft ankle rehabilitation robot 112
6.5 Kinematics of the soft ankle rehabilitation robot 114
6.6 Dynamics of the soft ankle rehabilitationrobot 117
6.6.1 Ankle force and torque 117
6.6.2 Inertial property of the moving unit 119
6.6.3 Force distribution 120
6.6.4 Festo fluidic muscle modelling 123
Contents vii
6.7 Constructionof the soft ankle rehabilitation robot 127
6.8 Summary 129
Appendix A Bill of materials 129
References 129
7 Control of asoftankle rehabilitation robot 133
7.1 Introduction 133
7.2 Passive trainingcontrol 134
7.2.1 Force distribution based cascade control 134
7.2.2 IFT control for repetitive training 136
7.3 Active trainingcontrol 143
7.3.1 Trajectory adaptation-based intelligent control 143
7.3.2 Game guided training control 144
7.4 Summary 150
References 150
8 Design of aGAit Rehabilitation EXoskeleton 153
8.1 Introduction 153
8.2 Support structure and trunk mechanism 154
8.3 Lower limb exoskeleton 155
8.3.1 Actuation of the lower limb exoskeleton 155
8.3.2 Mechanical and pneumatic system designof
the lower limb mechanism 160
8.4 Instrumentation 161
8.5 Safety of GAREX 163
8.6 Summary 164
References 164
9 Modelling andcontrol strategies development of GAREX 167
9.1 Introduction 167
9.2 System modelling 169
9.2.1 Valve flowdynamics 170
9.2.2 Pneumatic muscle pressuredynamics 171
9.2.3 Pneumatic muscle force dynamics model 172
9.2.4 Load dynamics of the mechanism 177
9.3 Multi-input-multi-output sliding mode control for GAREX 179
9.4 Experimental validation 184
9.4.1 Experimentswith the knee joint mechanism 185
9.5 Pilot studyof gait training with GAREX 189
9.5.1 Generating reference gait trajectory 189
9.5.2 Treadmill-based gait experiment with
healthy subject 197
9.6 Summary 199
Appendix A The mechanism dynamics calculation 202
References 204
viii Soft robotsfor healthcare applications
10 Conclusionandfuture work 207
10.1 Bookcontributions 207
10.1.1 Physical modelling and embedded sensing forthe
Peano muscle 207
10.1.2 Design and control of a soft wrist rehabilitationrobot 208
10.1.3 Design and control of a soft ankle rehabilitation robot 208
10.1.4 Design and control of a soft robotic GAit
Rehabilitation EXoskeleton 210
10.2 Future work 211
10.2.1 Modelling and fabrication process of the Peano muscle 211
10.2.2 Two-degreesoffreedomforthewristrehabilitationrobot 211
10.2.3 Optimisation and improvement of the soft ankle
rehabilitation robot 212
10.2.4 Control and validation of the GAREX 212
10.3 Summary 213
References 214
Index 215
Preface
Robots are not new to healthcare applications. The most typical example is the
Da Vinci Surgical System. This system has conducted more than 20,000 surgeries
since the year of 2000 and has paved the way for robotic advancements in health-
care. Other robotic systems have also been developed to provide care to patients
and help perform various surgeries and physical therapies. For instance, Magnetic
Microbots are a group of tiny robots used in a variety of operations, such as
removing plaque from a patient’s arteries or helping with ocular conditions and
disease screenings.Robotshave also been usedtoimprovethe day-to-day lives of
patients,suchastheBesticdevicetoassisteatingandtheReWalkPersonalSystem
6.0to help patients regain his/her walking ability.
Most conventional robots are constructed from stiff materials such as steel,
aluminiumandABSplastics.Theyareusuallypowereddirectlybyelectric motors
orbypumpsforcinghydraulicfluidsthroughrigidtubes.Suchdevicesarecapable
of large forces and high speeds with great precision, which makes them very pro-
ductiveinfactoryassemblylines.However,veryfewofthemcanoperateinnatural
environment or in close proximity to humans with interaction. In addition to safety
concerns, these robots are simply not very good at adapting their behaviour when
interacting with different environments. They are not well matched to the require-
mentsduetothestiffmaterialsused.Toovercomesomeoftheseobstacles,thereisan
increasinginterestindevelopingrobotsfromsoftmaterials.
Soft robotics is an emerging discipline that employs soft flexible materials,
such as fluids, gels and elastomers, in order to enhance the use of robotics in
healthcare applications. Compared to their rigid counterparts, soft robotic systems
have flexible and rheological properties that are closely related to biological sys-
tems, thus allowing the development of adaptive and flexible interactions with
complexdynamicenvironments.Withnewtechnologiesarisinginbio-engineering,
the integration of living cells into soft robotic systems offers the possibility of
accomplishing multiple complex functions such as sensing and actuating upon
external stimuli. These emerging bio-hybrid systems are showing promising out-
comesandopening upnewavenues inthefield ofsoftroboticsforapplications in
healthcare and other fields.
Onegoalofsoftroboticsistomakemachinesthatareadaptableandsafeintheir
capabilitieswheninteractingwithhumanusers.We takeit forgrantedthathumans
can walk up and down stairs, navigate through a cluttered room or move delicate
objects, but these tasks are extraordinarily difficult even for the most advanced
roboticsystems.Thepotentialreasoncanbethatstiffrobotsarecontrolledwithgreat