Kawamura / Svinin Advances in Robot Control

From Everyday Physics to Human-Like Movements
1. Auflage 2007
ISBN: 978-3-540-37347-6
Verlag: Springer Berlin Heidelberg
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 328 Seiten

This volume surveys three decades of modern robot control theory and describes how the work of Suguru Arimoto shaped its development. Twelve survey articles written by experts associated with Suguru Arimoto at various stages in his career treat the subject comprehensively. This book provides an important reference for graduate students and researchers, as well as for mathematicians, engineers and scientists whose work involves robot control theory.

Autoren/Hrsg.

Kawamura, Sadao

Svinin, Mikhail

Inhaltsverzeichnis

1;Preface;7
2;Curriculum Vitae: Suguru Arimoto;11
2.1;Professional Activities;11
2.2;Awards and Honors;12
2.3;List of Ph.D. Students;13
3;Main Scientific Contributions;17
3.1;1 Information Theory;17
3.2;2 Signal Processing;18
3.3;3 General Control Theory;18
3.4;4 Theory of Robot Control;19
4;List of Contributors;27
5;Contents;31
6;Human Robotics: A Vision and A Dream;33
6.1;References;37
7;Part I From Everyday Physics to Robot Control;39
7.1;Natural Motion and Singularity-Consistent Inversion of Robot Manipulators;41
7.1.1;1 Introduction;41
7.1.2;2 Singularity-Consistent Formulation of Inverse Kinematics;42
7.1.3;3 Natural Motion of a Manipulator;45
7.1.4;4 Vector-Parameterization of Configuration Space;46
7.1.5;5 Second-Order Singularity-Consistent Inverse Kinematics;47
7.1.6;6 Dynamic Analysis;48
7.1.7;7 An Example;51
7.1.8;8 Singularity-Consistent Controllers;55
7.1.9;9 Conclusions;61
7.1.10;References;63
7.2;Approximate Jacobian Control for Robot Manipulators;67
7.2.1;1 Introduction;67
7.2.2;2 Robot Kinematics and Dynamics;68
7.2.3;3 Approximate Jacobian Setpoint Control of Robots;70
7.2.4;4 Adaptive Jacobian Tracking Control of Robots;76
7.2.5;5 Conclusion;83
7.2.6;References;83
7.3;Adaptive Visual Servoing of Robot Manipulators;87
7.3.1;1 Introduction;87
7.3.2;2 Kinematics and Dynamics;90
7.3.3;3 Adaptive Dynamic Visual Servoing;96
7.3.4;4 Uncalibrated Visual Servoing for Multiple Feature Points;103
7.3.5;5 Experiments;105
7.3.6;6 Conclusions;111
7.3.7;References;111
7.4;Orthogonalization Principle for Dynamic Visual Servoing of Constrained Robot Manipulators;115
7.4.1;1 Introduction;115
7.4.2;2 The Orthogonalization Principle: Robot Force Control;116
7.4.3;3 The Visual Force Control Problem;118
7.4.4;4 Dynamics of the Visually Driven Constrained Robot;121
7.4.5;5 Control Design;125
7.4.6;6 Discussions;126
7.4.7;7 Experimental System;127
7.4.8;8 Conclusions;131
7.4.9;Appendix: Proof of Theorem 1;131
7.4.10;References;136
7.5;Passivity-Based Control of Multi-Agent Systems;139
7.5.1;1 Introduction and Motivation;139
7.5.2;2 Output Synchronization;142
7.5.3;3 Nonlinear Coupling with Time-Delay;151
7.5.4;4 Examples;155
7.6;Navigation Functions for Dynamical, Nonholonomically Constrained Mechanical Systems;167
7.6.1;1 Introduction;167
7.6.2;2 Hybrid Controller for Nonholonomic Kinematic Systems;169
7.6.3;3 Hybrid Controller for Nonholonomic Dynamic Systems;176
7.6.4;4 Simulations;180
7.6.5;5 Conclusions;184
7.6.6;6 Acknowledgments;186
7.6.7;References;186
7.7;Planning and Control of Robot Motion Based on Time- Scale Transformation;189
7.7.1;1 Introduction;189
7.7.2;2 Characterization of Robot and Environment Dynamics;192
7.7.3;3 Planning and Control of Robot Motion under an Endpoint Constraint;194
7.7.4;4 Planning and Control of the Underwater Robot’s Motion;201
7.7.5;5 Experiments;206
7.7.6;6 Conclusion;208
7.7.7;7 Acknowledgments;209
7.7.8;References;210
8;Part II From Robot Control to Human-Like Movements;212
8.1;Modularity, Synchronization, and What Robotics May Yet Learn from the Brain;213
8.1.1;1 Introduction;213
8.1.2;2 Modularity, Stability, and Evolution;214
8.1.3;3 Synchronization;217
8.1.4;4 Polyrhythms;220
8.1.5;References;228
8.2;Force Control with A Muscle-Activated Endoskeleton;233
8.2.1;1 Introduction;233
8.2.2;2 Workless Forces;235
8.2.3;3 Coordinating Muscle Forces;238
8.2.4;4 Kinematic Instability;241
8.2.5;5 Concluding Remarks;246
8.2.6;Acknowledgements;247
8.2.7;References;247
8.3;On Dynamic Control Mechanisms of Redundant Human Musculo- Skeletal System;249
8.3.1;1 Introduction;249
8.3.2;2 Human-Like Reaching Movements;252
8.3.3;3 Human-Like Pinching Movements;264
8.3.4;4 Summary;274
8.3.5;References;278
8.4;Principle of Superposition in Human Prehension;281
8.4.1;1 History of the Principle of Superposition;281
8.4.2;2 Arimoto’s Principle of Superposition;282
8.4.3;3 Major Issues in the Control of Redundant Biological Systems;283
8.4.4;4 Prehension Synergies: The Hierarchical Control;284
8.4.5;5 Prehension Synergies: The Principle of Superposition in Static Tasks;286
8.4.6;6 Prehension Synergies: The Principle of Superposition in Reactions to Perturbations;288
8.4.7;7 Concluding Comments;290
8.4.8;References;291
8.5;Motion Planning of Human-Like Movements in the Manipulation of Flexible Objects;295
8.5.1;1 Introduction;295
8.5.2;2 Beta Function as a Model of Reaching Movements;297
8.5.3;3 Reaching Movements in Dynamic Environments;303
8.5.4;4 Experimental Results;313
8.5.5;5 Conclusions;321
8.5.6;References;321
8.6;Haptic Feedback Enhancement Through Adaptive Force Scaling: Theory and Experiment;325
8.6.1;1 Introduction;326
8.6.2;2 Position Based 3-D Force Scaling during Human- Robot Co- Manipulation: Problem Statement;328
8.6.3;3 Three Force Control Algorithms: A Review;330
8.6.4;4 Theory of Pushing and Poking;331
8.6.5;5 Three Force Scaling Algorithms;333
8.6.6;6 Experimental Results;335
8.6.7;7 Conclusions;345
8.6.8;Acknowledgments;346
8.6.9;References;347
8.7;Learning to Dynamically Manipulate: A Table Tennis Robot Controls a Ball and Rallies with a Human Being;349
8.7.1;1 Introduction;349
8.7.2;2 Robot Table Tennis;351
8.7.3;3 Ball Events in One Stroke;353
8.7.4;4 Paddle Motion Decision;355
8.7.5;5 Generation of Paddle Movement;359
8.7.6;6 Experimental Results;362
8.7.7;7 Conclusion;369
8.7.8;Appendix: Locally Weighted Regression(LWR)[8];371
8.7.9;References;372

Leseproben

Approximate Jacobian Control for Robot Manipulators (P. 35)

Chien Chern Cheah
School of Electrical and Electronic Engineering
Nanyang Technological University
Block S1, Nanyang Avenue, S(639798)
Republic of Singapore

Summary.

Most research so far in robot control has assumed either kinematics or Jacobian matrix of the robots from joint space to task space is known exactly. Unfortunately, no physical parameters can be derived exactly. In addition, when the robot picks up objects of uncertain lengths, orientations or gripping points, the kinematics and dynamics become uncertain and change according to different tasks.

This paper presents several approximate Jacobian control laws for robots with uncertainties in kinematics and dynamics. Lyapunov functions are presented for stability analysis of feedback control problems with uncertain kinematics. We shall show that the end-effector’s position converges to a desired position even when the kinematics and Jacobian matrix are uncertain.

1 Introduction

It is interesting to observe from human reaching movements that we do not need an accurate knowledge of kinematics and dynamics of our arms. We are also able to pick up a new tool or object and manipulate it skillfully to accomplish a task, using only the approximate knowledge of the length, mass, orientation and gripping point of the tool. Such basic ability of sensing and responding to changes without an accurate knowledge of sensory-to-motor transformation gives us a high degree of flexibility in dealing with unforseen changes in the real world.

The kinematics and dynamics of robot manipulators are highly nonlinear. By exploring physical properties of the robot system and using Lyapunov method, Takegaki and Arimoto [1], Arimoto and Miyazaki [2] showed that simple controllers such as the PD and PID feedback are effective for setpoint control despite the nonlinearity and uncertainty of the robot dynamics.

To deal with trajectory-tracking control, Slotine and Li [3, 4] proposed an adaptive control law for robotic manipulator using Lyapunov method. After more than two decades of research, much progress has been made in control of robots with dynamic uncertainty [1]-[19].

However, most research on robot control has assumed that the exact kinematics and Jacobian matrix of the manipulator from joint space to Cartesian space are known. In the presence of uncertainty in kinematics, it is impossible to derive the desired joint angle from the desired end effector path by solving the inverse kinematics problem.

In addition, the Jacobian matrix of the mapping from joint space to task space could not be exactly derived. This assumption leads us to several problems in the development of robot control laws today. In free motion [20], this implies that the exact lengths of the links, joint o.sets and the object which the robot is holding, must be known. Unfortunately, no physical parameters could be derived exactly.

In addition, when the robot picks up objects or tools of di.erent lengths, unknown orientations and gripping points, the overall kinematics are changing and therefore difficult to derive exactly.