Biologically Inspired Control of Humanoid Robot Arms

Biologically Inspired Control of Humanoid Robot Arms
اسم المؤلف
Adam Spiers , Said Ghani Khan , Guido Herrmann
التاريخ
18 مايو 2021
التصنيف
المشاهدات
التقييم
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Biologically Inspired Control of Humanoid Robot Arms
Robust and Adaptive Approaches
Adam Spiers • Said Ghani Khan • Guido Herrmann
Contents
1 Introduction . 1
1.1 Prologue 1
1.1.1 Industrial Robots . 2
1.1.2 Humanoid Robots 4
1.1.3 The Importance of Human-Like Motion 5
1.1.4 Biologically Inspired Design . 6
1.1.5 Physical Safety and Active Compliance for Safety . 6
1.1.6 Robust and Adaptive Control 7
1.2 Objective of the Book 7
1.3 Guidance for the Reader . 8
1.3.1 Recommended Reading Routes 9
References 10
Part I Background on Humanoid Robots and Human Motion
2 Humanoid Robots and Control . 15
2.1 Humanoid Robots 15
2.1.1 Functional Tools 16
2.1.2 Models of Humans . 17
2.1.3 Human–Robot Interaction 17
2.2 Goals of Human-Like Motion . 18
2.3 Robot Motion Control Overview 19
2.3.1 Kinematics-Based Robot Motion Control . 21
2.3.2 Dynamic-Based Robot Motion Control . 25
2.3.3 Optimal Control 28
2.3.4 Operational Space Control . 29
2.3.5 Dual Robot Arm Control . 30
2.3.6 Hand Grasping Control . 31
2.4 Sensing and Robot Arm Motion . 32
xixii Contents
2.5 Robot and Control Hardware 33
2.5.1 Elumotion Robotic Platform . 34
2.5.2 Robot Structure . 37
2.5.3 Actuators 39
2.5.4 Motor Drivers . 39
2.5.5 EPOS Interface method 40
2.6 Summary 41
References 41
3 Human Motion 49
3.1 Introduction . 49
3.2 Motion Studies . 50
3.3 Motion Models . 51
3.3.1 Kinematic Models 51
3.3.2 Dynamic Models 53
3.4 Physiological Modelling . 55
3.4.1 Muscle Models 55
3.4.2 Physiological Complexity 56
3.4.3 Neural Models 57
3.4.4 Simplified Models 59
3.5 Motion Capture Methods and Technology 59
3.6 Human Motion Reproduction and Synthesis 63
3.6.1 Direct Reproduction of Human Motion . 63
3.6.2 Learning Techniques . 66
3.6.3 Dynamic Movement Primitives 67
3.6.4 Operational Space Control . 68
3.7 Summary 68
References 70
Part II Robot Control: Implementation
4 Basic Operational Space Controller 77
4.1 Introduction . 77
4.1.1 Human Verification . 78
4.1.2 Robot Specification . 79
4.1.3 Robot Goal Modification . 81
4.2 The Operational Space Mathematical Formulation . 81
4.3 Task Control 82
4.3.1 Jacobian Pseudo Inverse 82
4.3.2 Task-Space Dynamic Projection . 83
4.3.3 Feedback Linearisation . 85
4.4 Posture Control . 86
4.4.1 ‘Effort’ Cost Function 86
4.4.2 Task/Posture Isolation 89Contents xiii
4.5 Simulation and Implementation . 90
4.5.1 Controller Realisation 90
4.5.2 Simulation Results 92
4.5.3 Robot Implementation 97
4.6 Summary 99
References 100
5 Sliding Mode Task Controller Modification . 101
5.1 Introduction . 101
5.2 Sliding Mode Control Overview . 102
5.3 Controller Design 103
5.3.1 Switching Function . 103
5.3.2 Variable Structure Law . 105
5.4 Lyapunov Stability Analysis . 107
5.5 Results 108
5.5.1 Simulation 108
5.5.2 Physical Robot 111
5.5.3 PID Results . 111
5.5.4 Sliding Mode Results . 112
5.5.5 Demand Filter . 113
5.6 Compliance . 114
5.7 Summary 114
References 115
6 Implementing ‘Discomfort’ for Smooth Joint Limits . 117
6.1 Introduction . 117
6.1.1 Dynamic Model Simplicity 118
6.2 Visualisation Technique 119
6.2.1 Motion Analysis 121
6.3 Joint Limit Function Design . 122
6.3.1 Integration with the Effort Function . 123
6.4 Results 124
6.4.1 Simulated Results . 124
6.4.2 Practical Results 126
6.5 Summary 128
References 130
7 Sliding Mode Optimal Controller . 131
7.1 Introduction . 131
7.2 Controller Design 132
7.2.1 Optimal Sliding Surface 132
7.2.2 Control Method . 135
7.2.3 Velocity Decoupling 138
7.2.4 Overall Controller 144
7.3 Implementation Issues: Viscous Friction Identification
and Compensation 146xiv Contents
7.4 Simulated Implementation . 147
7.4.1 Controller Effort 149
7.4.2 Friction Model 151
7.4.3 Simulated Results . 151
7.5 Practical Implementation 156
7.6 Summary 158
References 159
8 Adaptive Compliance Control with Anti-windup
Compensation and Posture Control 161
8.1 Introduction . 161
8.2 Adaptive Compliance Control for Task Motion 163
8.2.1 Impedance Reference Model . 165
8.2.2 Principle of the Model Reference Scheme 166
8.3 Effort-Minimising Posture Torque Controller 167
8.4 Anti-windup Compensator . 168
8.5 Implementation . 171
8.6 One-Dimensional Adaptive Compliance Control
of a Robot Arm . 171
8.6.1 Tracking . 171
8.6.2 Compliance Results 173
8.6.3 Anti-windup Compensator Results 174
8.7 Multidimensional Adaptive Compliance Control of a Robot Arm. 181
8.7.1 Joint Torque Sensors and Body Torque Estimates 182
8.7.2 Tracking and Compliance Results . 185
8.7.3 Anti-windup Compensator Results 185
8.8 Summary 190
References 190
Part III Human Motion Recording for Task Motion
Modelling and Robot Arm Control
9 Human Motion Recording and Analysis . 195
9.1 Initial Motion Capture Objective 195
9.1.1 The Vicon System 199
9.1.2 Experimental Set-up 200
9.1.3 Results 201
9.1.4 Summary of Initial Motion Capture Experiments . 203
9.2 Motion Capture for Robotic Implementation . 206
9.2.1 Human–Robot Kinematic Mismatch 206
9.2.2 Motion Capture Process for Inconsistent
Kinematic Models 208
9.2.3 Extended Motion Capture Method . 210
9.2.4 Vicon Skeleton Model 212
9.2.5 Incompatible Kinematics Removal 213Contents xv
9.2.6 Inverse Kinematics . 215
9.2.7 Trajectory Discrepancy . 217
9.3 Four Degrees of Freedom Comparative Trials 218
9.3.1 Results 219
9.4 Summary 221
References 222
10 Neural Network Motion Learning by Observation for
Task Modelling and Control . 225
10.1 Introduction . 225
10.1.1 Learning by Observation . 226
10.2 Learning by Observation Method . 228
10.3 Minimal Trajectory Encoding . 229
10.3.1 Polynomial Encoding Issues . 229
10.3.2 Scaling and Fitting of Generated Trajectories . 231
10.4 Network Structure 233
10.5 Experimental Procedure 234
10.5.1 Sub-motion Splitting . 235
10.5.2 Training Data . 235
10.5.3 Neural Network Results 237
10.6 Integration into the Robot Controller 241
10.7 Summary 245
References 245
Appendix A Kinematics: Introduction . 247
A.1 Kinematics Notation . 247
A.1.1 Position Vector 248
A.1.2 Rotation Matrix . 249
A.1.3 Transformation Matrix . 251
A.2 Denavit–Hartenberg Notation . 251
A.2.1 Frame Assignment Convention 252
A.2.2 DH Parameters 252
A.3 Applied Kinematics 253
A.3.1 Forward Kinematics 253
A.3.2 Inverse Kinematics . 254
A.4 Robot Jacobian . 254
References 255
Appendix B Inverse Kinematics for BERUL2 257
B.1 Denavit–Hartenberg Parameters . 257
B.2 Forward Kinematics 257
B.3 Algebraic Solution . 258
Reference . 264xvi Contents
Appendix C Theoretical Summary of Adaptive Compliant Controller 265
C.1 Proof of Theorem 1 265
References 269
Appendix D List of Videos 271
Index .
Index
A
Activation Matrix, 87, 111
Actuator Dynamics, 98
Actuator Saturation, 138, 163, 168
Adaptation, 226
Adaptive, 161
Adaptive Compliance Control, 163
Adaptive Control, 7, 21, 68, 163
Anatomical Landmarks, 213
Antagonism, 56, 87
Anthropomorphic, 18
Anti-Windup Compensator, 161, 163, 168,
174, 185
Approach and Adjustment, 51
AW-compensator, 265
B
Balance, 54
Bang-Bang Control, 106
Bell-shaped velocity profiles, 51, 52
BERT, 34, 97
BERT/BERUL Actuators, 39
BERT2, 36, 162
BERUL, 34, 90, 97, 111, 126, 146, 156
BERUL2, 36, 126, 195, 206, 241, 257
Biologically Inspired, 5, 6, 19, 130
Biomechanical, 5
Boundary Layer, 145, 152
Branched Systems, 27
C
CAN, 39
Cartesian Workspace, 23
Centrifugal, 26, 81, 84
Chattering, 106
Co-ordinate Frame, 248
Co-ordinate System, 20
Compliance, 6, 24, 67, 114, 161, 162, 173
Compliance Control, 162, 163
Confidence, 5
Controller Effort, 149
Convergence, 161
Coordinate Frame, 248
Coriolis, 26, 81, 84
Cost Function, 78, 123, 141
Cost Gradient, 122
Coulomb Friction, 151
Curved Motion Trajectories, 52
D
Damaging Hardware, 117
Danger, 131
Decoupling, 139
Decoupling Term, 89
Demand Filter, 113
Denavit–Hartenberg, 251, 257
Discomfort, 79, 122, 123, 130
Disturbance, 114, 131
dSPACE, 39, 90
Dual Arm Control, 30
Dynamic Control, 2
Dynamic Model, 7, 118
Dynamic Modelling, 26
Dynamic Models of Human Motion, 53
Dynamic Motion Primitives, 67
Dynamic Muscle Modelling, 59
Dynamics, 164
© Springer International Publishing Switzerland 2016
A. Spiers et al., Biologically Inspired Control of Humanoid Robot Arms,
DOI 10.1007/978-3-319-30160-0
273274 Index
E
Effort, 86, 87, 225
effort, 124
Effort Function, 86, 93, 119, 123
Effort Minimisation, 96, 97, 132, 147
Effort Optimisation, 227
Effort Plot, 120
Effort Profiles, 93
Effort Visualisation, 119
Elbow Configuration, 119
Elumotion, 34, 111
Encoders, 111
EPOS, 40, 90
Equilibrium Point Hypothesis, 52
Evolution, 6
Exoskeleton, 58
F
Feedback Linearisation, 77, 85, 92, 104, 134,
147
Filter, 113
Fitt’s Law, 51
Fitting, 231
Force Control, 6, 20, 81, 85
Forgetting Factor, 165
Forward Kinematics, 22, 253, 257
Friction, 146
Friction Compensation, 134, 146
Friction Model, 151
G
Goals of Human-Like Motion, 18
Gravity, 26, 52, 53, 197, 200, 227
Gravity Compensation, 146
H
Harmonic Drive, 98, 146
Hazardous Environments, 4
Hierarchical Control, 68
Hill Equation, 55
Human Arm, 16
Human Behaviour, 17, 18
Human Motion, 49, 197
Human Motion Models, 50, 51
Human Motion Synthesis, 15
Human–Human Interaction, 20
Human–Robot Interaction, 4, 6, 17, 24, 68,
131, 138, 159, 161, 226
Human-Like Appearance, 5
Human-Like Motion, 5, 18
I
Imitation, 207
Impedance Control, 162
Implementation, 90
Inconsistent Kinematic Models, 208
Industrial Robotics, 2, 6, 18, 24
Input Passive, 269
Instability, 20, 68
Inverse Kinematics, 2, 20, 22, 119, 215, 254,
257
J
Jacobian, 82, 111, 139, 254
Jacobian Pseudo-Inverse, 77, 82, 139, 164
Jerk, 52
Joint Level Control, 20
Joint Limits, 65, 117, 122, 132, 152
Joint Space, 23
Joint Space Dynamics, 83
K
Kinematic Mismatch, 206
Kinematic Models of Human Motion, 51
Kinematics, 247
Kinematics Notation, 247
KUKA, 21
L
Lagrangian, 26
Lagrangian Dynamics, 85
Learning, 19, 66, 225, 226
Learning by Demonstration, 226
Learning by Observation, 225, 226, 228
Levenberg-Marquardt algorithm, 233
Limit Switch, 117, 126
Local Stability, 138
Lyapunov Analysis of Sliding Surface, 134
Lyapunov Function, 131, 135, 266
Lyapunov Methods, 132
Lyapunov Stability, 107, 161
Lyapunov Theory, 132
M
Maple, 85, 90
MATLAB, 90, 234
Metabolic, 55, 56, 87
Minimal Energy Criteria, 54
Model Mismatch, 7, 111, 150, 152
Model Reference Scheme Principle, 166
Motion Capture, 59, 195, 199, 200, 206, 227,
234Index 275
Motion Priority, 82
Motion Studies, 50
Motor Driver, 39
MRAC – Model Reference Adaptive Control,
162, 174
Muscle, 18, 53, 87
Muscle Control, 51
Muscle Dynamics, 56
Muscle Effort, 79
Muscle Matrix, 87, 88, 92, 125
Muscle Modelling, 57
Musculoskeletal System, 88
Muybridge, 50
N
Neural Models of Motion, 57
Neural Network, 163, 225, 233
Non-linear System, 20
Non-Monotonic, 52, 78
Normalisation, 229
Null Space, 81, 89, 165, 269
O
Objectives of Humanoid Robots, 1
Operational Space, 68, 77, 81, 89, 104, 138,
143
Operational Space Control, 29, 131
Optimal Control, 28
Optimal Controller, 86, 128
Optimality in Robot Motion, 25
Optimisation, 52, 53, 77, 132
Optimisation in Human Motion, 2
Overhead Reaching Task, 78
P
Pain, 123
Passivity, 161, 269
PD, 109, 138
Pectoral Girdle, 214
Perceptron, 233
Phase Plane, 109
Physical Safety, 6
Physiological, 198, 225, 227
Physiology, 55, 207, 227
PID Control, 111
Planar Robot, 23, 78
Polynomial Encoding, 228, 229
Position Vector, 248
Posture, 21, 77, 87
Posture Controller, 82, 86, 117
Posture Space Dynamics, 268
Posture Torque Controller, 167
Potential Field, 117, 122, 128
Projection, 84
R
Reaching Motion, 49, 77, 78, 110, 125, 197,
200, 203, 227
Reaching Phase, 102
Recursive Newton-Euler Algorithm, 26
Redundancy, 20, 22
Redundancy Problem, 50, 54
Reference Model, 165
Reinforcement Learning, 67
Robot Dynamics, 20
Robot Motion Control, 19
Robot Structure, 37
Robust, 105
Robust Control, 7, 21, 68
Robustness, 131, 165, 269
Rotation Matrix, 249
S
S-function, 90
Safety, 2, 4, 6, 24
Safety Critical, 131
Saturation, 269
Saturation Function, 151
Scaling, 231
Scheduling Element, 168, 189
Shoulder, 207
SimMechanics, 87, 90
Simulations, 18
Simulink, 90, 151
Singularity, 111, 121
Sliding Mode Control, 7, 102, 108, 109, 131
Sliding Optimal, 147
Sliding Optimal Controller, 131, 132, 144, 147,
220
Sliding Phase, 102
Sliding Surface, 102, 106, 109, 132, 133
Social Robotics, 17
Social Robots, 161
Spatial Notation, 27
Spline Curve, 23
Stability, 105, 165, 269
Stability Condition, 145
Stability Proof, 265
Static Friction, 147
Steepest Descent, 132, 133, 142
Stiffness Matrix, 165
Straight Line Motion Trajectories, 52
Switching Control, 132276 Index
Switching Function, 103
Synthetic Motion, 77
System Identification, 146
T
Task Control, 85
Task Controller, 82, 85, 103
Task Space, 81
Task Space Projection, 84
Task/Posture Decoupling, 138
Tendon, 56
Tool Use, 16
Torque Metric, 149, 153
Torque Sensors, 36
Tracking, 171, 241
Training, 235
Transformation Matrix, 251
U
Ultimate Bounded Stability, 165
Ultimately Bounded Stable, 265, 269
Un-modelled Dynamics, 28
Uncertainty, 102, 105, 131, 136, 137, 151
Unmodelled Dynamics, 98
Unnatural Posture, 117
V
Variable Structure, 105
Vertical Reaching, 52
Vicon, 64, 199, 212, 227, 234, 259
Viscous Friction, 134, 146
Visualisation, 119
VITE model, 51
W
Windup, 163
Work Cell, 2, 3, 18
Z
Zero-Gravity, 146
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