Preface	8
	Contents	12
	Nomenclature	18
	1 Introduction	21
	1.1 Prologue	21
	1.1.1 Industrial Robots	22
	1.1.2 Humanoid Robots	24
	1.1.3 The Importance of Human-Like Motion	25
	1.1.4 Biologically Inspired Design	26
	1.1.5 Physical Safety and Active Compliance for Safety	26
	1.1.6 Robust and Adaptive Control	27
	1.2 Objective of the Book	27
	1.3 Guidance for the Reader	28
	1.3.1 Recommended Reading Routes	29
	References	30
	Part I Background on Humanoid Robots and Human Motion	33
	2 Humanoid Robots and Control	34
	2.1 Humanoid Robots	34
	2.1.1 Functional Tools	35
	2.1.2 Models of Humans	36
	2.1.3 Human–Robot Interaction	36
	2.2 Goals of Human-Like Motion	37
	2.3 Robot Motion Control Overview	38
	2.3.1 Kinematics-Based Robot Motion Control	40
	2.3.1.1 Forward Kinematics	41
	2.3.1.2 Inverse Kinematics	41
	2.3.1.3 Basic Inverse Kinematics Example	42
	2.3.1.4 Kinematics Control Discussion	43
	2.3.2 Dynamic-Based Robot Motion Control	44
	2.3.2.1 Dynamic Modelling	45
	2.3.2.2 Model Specification	46
	2.3.3 Optimal Control	47
	2.3.4 Operational Space Control	48
	2.3.5 Dual Robot Arm Control	49
	2.3.6 Hand Grasping Control	50
	2.4 Sensing and Robot Arm Motion	51
	2.5 Robot and Control Hardware	52
	2.5.1 Elumotion Robotic Platform	53
	2.5.2 Robot Structure	56
	2.5.3 Actuators	58
	2.5.4 Motor Drivers	58
	2.5.5 EPOS Interface method	59
	2.6 Summary	60
	References	60
	3 Human Motion	67
	3.1 Introduction	67
	3.2 Motion Studies	68
	3.3 Motion Models	69
	3.3.1 Kinematic Models	69
	3.3.2 Dynamic Models	71
	3.4 Physiological Modelling	73
	3.4.1 Muscle Models	73
	3.4.2 Physiological Complexity	74
	3.4.3 Neural Models	75
	3.4.4 Simplified Models	77
	3.5 Motion Capture Methods and Technology	77
	3.6 Human Motion Reproduction and Synthesis	81
	3.6.1 Direct Reproduction of Human Motion	81
	3.6.2 Learning Techniques	84
	3.6.3 Dynamic Movement Primitives	85
	3.6.4 Operational Space Control	86
	3.7 Summary	86
	References	88
	Part II Robot Control: Implementation	93
	4 Basic Operational Space Controller	94
	4.1 Introduction	94
	4.1.1 Human Verification	95
	4.1.2 Robot Specification	96
	4.1.3 Robot Goal Modification	98
	4.2 The Operational Space Mathematical Formulation	98
	4.3 Task Control	99
	4.3.1 Jacobian Pseudo Inverse	99
	4.3.2 Task-Space Dynamic Projection	100
	4.3.3 Feedback Linearisation	102
	4.4 Posture Control	103
	4.4.1 `Effort' Cost Function	103
	4.4.2 Task/Posture Isolation	106
	4.5 Simulation and Implementation	107
	4.5.1 Controller Realisation	107
	4.5.2 Simulation Results	109
	4.5.2.1 Task Only Control	109
	4.5.2.2 Posture-Dependent Trajectories	109
	4.5.3 Robot Implementation	114
	4.5.3.1 Increased Gains	115
	4.6 Summary	116
	References	117
	5 Sliding Mode Task Controller Modification	118
	5.1 Introduction	118
	5.2 Sliding Mode Control Overview	119
	5.3 Controller Design	120
	5.3.1 Switching Function	120
	5.3.2 Variable Structure Law	122
	5.4 Lyapunov Stability Analysis	124
	5.5 Results	125
	5.5.1 Simulation	125
	5.5.2 Physical Robot	128
	5.5.3 PID Results	128
	5.5.4 Sliding Mode Results	129
	5.5.5 Demand Filter	130
	5.6 Compliance	131
	5.7 Summary	131
	References	132
	6 Implementing `Discomfort' for Smooth Joint Limits	133
	6.1 Introduction	133
	6.1.1 Dynamic Model Simplicity	134
	6.2 Visualisation Technique	135
	6.2.1 Motion Analysis	137
	6.3 Joint Limit Function Design	138
	6.3.1 Integration with the Effort Function	139
	6.4 Results	140
	6.4.1 Simulated Results	140
	6.4.2 Practical Results	142
	6.5 Summary	144
	References	146
	7 Sliding Mode Optimal Controller	147
	7.1 Introduction	147
	7.2 Controller Design	148
	7.2.1 Optimal Sliding Surface	148
	7.2.1.1 Modified Model for Analysis and Controller Design	149
	7.2.1.2 Lyapunov Analysis of Surface	150
	7.2.2 Control Method	151
	7.2.2.1 Controller Design	151
	7.2.2.2 Controller Design with Estimates	152
	7.2.3 Velocity Decoupling	154
	7.2.3.1 Cost Function Revision for Decoupling	157
	7.2.3.2 Observation on the Cost Function Dynamics	158
	7.2.3.3 Sliding Mode Analysis for Posture Control Only	159
	7.2.4 Overall Controller	160
	7.3 Implementation Issues: Viscous Friction Identification and Compensation	162
	7.4 Simulated Implementation	163
	7.4.1 Controller Effort	165
	7.4.2 Friction Model	167
	7.4.3 Simulated Results	167
	7.5 Practical Implementation	172
	7.6 Summary	174
	References	175
	8 Adaptive Compliance Control with Anti-windup Compensation and Posture Control	176
	8.1 Introduction	176
	8.2 Adaptive Compliance Control for Task Motion	178
	8.2.1 Impedance Reference Model	180
	8.2.2 Principle of the Model Reference Scheme	181
	8.3 Effort-Minimising Posture Torque Controller	182
	8.4 Anti-windup Compensator	183
	8.5 Implementation	186
	8.6 One-Dimensional Adaptive Compliance Control of a Robot Arm	186
	8.6.1 Tracking	186
	8.6.2 Compliance Results	188
	8.6.3 Anti-windup Compensator Results	189
	8.7 Multidimensional Adaptive Compliance Control of a Robot Arm	196
	8.7.1 Joint Torque Sensors and Body Torque Estimates	197
	8.7.2 Tracking and Compliance Results	200
	8.7.3 Anti-windup Compensator Results	200
	8.8 Summary	205
	References	205
	Part III Human Motion Recording for Task Motion Modelling and Robot Arm Control	207
	9 Human Motion Recording and Analysis	208
	9.1 Initial Motion Capture Objective	208
	9.1.1 The Vicon System	212
	9.1.2 Experimental Set-up	213
	9.1.3 Results	214
	9.1.4 Summary of Initial Motion Capture Experiments	216
	9.2 Motion Capture for Robotic Implementation	219
	9.2.1 Human–Robot Kinematic Mismatch	219
	9.2.2 Motion Capture Process for Inconsistent Kinematic Models	221
	9.2.3 Extended Motion Capture Method	223
	9.2.4 Vicon Skeleton Model	225
	9.2.5 Incompatible Kinematics Removal	226
	9.2.6 Inverse Kinematics	228
	9.2.7 Trajectory Discrepancy	230
	9.3 Four Degrees of Freedom Comparative Trials	231
	9.3.1 Results	232
	9.4 Summary	234
	References	235
	10 Neural Network Motion Learning by Observation for Task Modelling and Control	237
	10.1 Introduction	237
	10.1.1 Learning by Observation	238
	10.2 Learning by Observation Method	240
	10.3 Minimal Trajectory Encoding	241
	10.3.1 Polynomial Encoding Issues	241
	10.3.2 Scaling and Fitting of Generated Trajectories	243
	10.4 Network Structure	245
	10.5 Experimental Procedure	246
	10.5.1 Sub-motion Splitting	247
	10.5.2 Training Data	247
	10.5.3 Neural Network Results	249
	10.6 Integration into the Robot Controller	252
	10.7 Summary	257
	References	257
	Appendix A Kinematics: Introduction	259
	A.1 Kinematics Notation	259
	A.1.1 Position Vector	260
	A.1.2 Rotation Matrix	261
	A.1.3 Transformation Matrix	263
	A.2 Denavit–Hartenberg Notation	263
	A.2.1 Frame Assignment Convention	264
	A.2.2 DH Parameters	264
	A.3 Applied Kinematics	265
	A.3.1 Forward Kinematics	265
	A.3.2 Inverse Kinematics	266
	A.4 Robot Jacobian	266
	References	267
	Appendix B Inverse Kinematics for BERUL2	268
	B.1 Denavit–Hartenberg Parameters	268
	B.2 Forward Kinematics	268
	B.3 Algebraic Solution	269
	Reference	275
	Appendix C Theoretical Summary of Adaptive Compliant Controller	276
	C.1 Proof of Theorem 1	276
	References	280
	Appendix D List of Videos	282
	Index	283