Industrial Robotics Theory, Modelling and Control Industrial Robotics Theory, Modelling and Control Edited by Sam Cubero pro literatur Verlag Published by the plV pro literatur Verlag Robert Mayer-Scholz plV pro literatur Verlag Robert Mayer-Scholz Mammendorf Germany Abstracting and non-profit use of the material is permitted with credit to the source. State- ments and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. Publisher assumes no responsibility liability for any damage or injury to persons or property arising out of the use of any materials, in- structions, methods or ideas contained inside. After this work has been published by the Ad- vanced Robotic Systems International, authors have the right to republish it, in whole or part, in any publication of which they are an author or editor, and the make other personal use of the work. © 2007 Advanced Robotic Systems International www.ars-journal.com Additional copies can be obtained from: publication@ars-journal.com First published January 2007 Typeface Palatino Linotype 10/11/12 pt Printed in Croatia A catalog record for this book is available from the German Library. Industrial Robotics: Theory, Modelling and Control / Edited by Sam Cubero. p. cm. ISBN 3-86611-285-8 1. Manipulators. 2. Kinematic. 3. Design I. Title. V Contents Preface IX 1. Robotic Body-Mind Integration: Next Grand Challenge in Robotics 1 K. Kawamura, S. M. Gordon and P. Ratanaswasd 2. Automatic Modeling for Modular Reconfigurable Robotic Systems – Theory and Practice 43 I-M. Chen, G. Yang and S. H. Yeo 3. Kinematic Design and Description of Industrial Robotic Chains 83 P. Mitrouchev 4. Robot Kinematics: Forward and Inverse Kinematics 117 S. Kucuk and Z. Bingul 5. Structure Based Classification and Kinematic Analysis of Six-Joint Industrial Robotic Manipulators 149 T. Balkan, M. K. Özgören and M. A. S. Arıkan 6. Inverse Position Procedure for Manipulators with Rotary Joints 185 I. A. Sultan 7. Cable-based Robot Manipulators with Translational Degrees of Freedom 211 S. Behzadipour and A. Khajepour 8. A Complete Family of Kinematically-Simple Joint Layouts: Layout Models, Associated Displacement Problem Solutions and Applications 237 S. Nokleby and R. Podhorodeski 9. On the Analysis and Kinematic Design of a Novel 2-DOF Translational Parallel Robot 265 J. Wang, X-J. Liu and C. Wu 10. Industrial and Mobile Robot Collision–Free Motion Planning Using Fuzzy Logic Algorithms 301 S. G. Tzafestas and P. Zavlangas 11. Trajectory Planning and Control of Industrial Robot Manipulators 335 S. R. Munasinghe and M. Nakamura VI 12. Collision free Path Planning for Multi-DoF Manipulators 349 S. Lahouar, S. Zeghloul and L. Romdhane 13. Determination of Location and Path Planning Algorithms for Industrial Robots 379 Y. Ting and H C. Jar 14. Homogeneous Approach for Output Feedback Tracking Control of Robot Manipulators 393 L. T. Aguilar 15. Design and Implementation of FuzzyControl for Industrial Robot 409 M. S. Hitam 16. Modelling of Parameter and Bound Estimation Laws for Adaptive-Robust Control of Mechanical Manipulators Using Variable Function Approach 439 R. Burkan 17. Soft Computing Based Mobile Manipulator Controller Design 467 A. Foudil and B. Khier 18. Control of Redundant Robotic Manipulators with State Constraints 499 M. Galicki 19. Model-Based Control for Industrial Robots: Uniform Approaches for Serial and Parallel Structures 523 H. Abdellatif and B. Heimann 20. Parallel Manipulators with Lower Mobility 557 R. Di Gregorio 21. Error Modeling and Accuracy of Parallel Industrial Robots 573 H. Cui and Z. Zhu 22. Networking Multiple Robots for Cooperative Manipulation 647 M. Moallem 23. Web-Based Remote Manipulation of Parallel Robot in Advanced Manufacturing Systems 659 D. Zhang, L. Wang and E. Esmailzadeh 24. Human-Robot Interaction Control for Industrial Robot Arm through Software Platform for Agents and Knowledge Management 677 T. Zhang, V. Ampornaramveth and H. Ueno 25. Spatial Vision-Based Control of High-Speed Robot Arms 693 F. Lange and G. Hirzinger 26. Visual Control System for Robotic Welding 713 D. Xu, M. Tan and Y. Li VII 27. Visual Conveyor Tracking in High-speed Robotics Tasks 745 T. Borangiu 28. Learning-Based Visual Feedback Control of an Industrial Robot 779 X. Nan-Feng and S. Nahavandi 29. Joystick Teaching System for Industrial Robots Using Fuzzy Compliance Control 799 F. Nagata, K. Watanabe and K. Kiguchi 30. Forcefree Control for Flexible Motion of Industrial Articulated Robot Arms 813 S. Goto 31. Predictive Force Control of Robot Manipulators in Nonrigid Environments 841 L. F. Baptista, J. M. C. Sousa and J. M. G. Sa da Costa 32. Friction Compensation in Hybrid Force/Velocity Control for Contour Tracking Tasks 875 A. Visioli, G. Ziliani and G. Legnani 33. Industrial Robot Control System Parametric Design on the Base of Methods for Uncertain Systems Robustness 895 A. A. Nesenchuk and V. A. Nesenchuk 34. Stochastic Analysis of a System containing One Robot and (n-1) Standby Safety Units with an Imperfect Switch 927 B. S. Dhillon and S. Cheng Corresponding Author List 951 IX Preface Robotics is the applied science of motion control for multi-axis manipulators and is a large subset of the field of "mechatronics" (Mechanical, Electronic and Software engineering for product or systems development, particularly for motion control applications). Mechatronics is a more general term that includes robotic arms, positioning systems, sensors and machines that are controlled by electronics and/or software, such as automated machinery, mobile ro- bots and even your computer controlled washing machine and DVD movie player. Most of the information taught in mechatronic engineering courses around the world stems from indus- trial robotics research, since most of the earliest actuator and sensor technologies were first developed and designed for indoor factory applications. Robotics, sensors, actuators and controller technologies continue to improve and evolve at an amazing rate. Automation systems and robots today are performing motion control and real- time decision making tasks that were considered impossible just 40 years ago. It can truly be said that we are now living in a time where almost any form of physical work that a human being can do can be replicated or performed faster, more accurately, cheaper and more consis- tently using computer controlled robots and mechanisms. Many highly skilled jobs are now completely automated. Manufacturing jobs such as metal milling, lathe turning, pattern mak- ing and welding are now being performed more easily, cheaper and faster using CNC ma- chines and industrial robots controlled by easy-to-use 3D CAD/CAM software. Designs for mechanical components can be quickly created on a computer screen and converted to real- world solid material prototypes in under one hour, thus saving a great deal of time and costly material that would normally be wasted due to human error. Industrial robots and machines are being used to assemble, manufacture or paint most of the products we take for granted and use on a daily basis, such as computer motherboards and peripheral hardware, automo- biles, household appliances and all kinds of useful whitegoods found in a modern home. In the 20th century, engineers have mastered almost all forms of motion control and have proven that robots and machines can perform almost any job that is considered too heavy, too tiring, too boring or too dangerous and harmful for human beings. Human decision making tasks are now being automated using advanced sensor technologies such as machine vision, 3D scanning and a large variety of non-contact proximity sensors. The areas of technology relating to sensors and control are still at a fairly primitive stage of devel- opment and a great deal of work is required to get sensors to perform as well as human sen- sors (vision, hearing, touch/tactile, pressure and temperature) and make quick visual and auditory recognitions and decisions like the human brain. Almost all machine controllers are very limited in their capabilities and still need to be programmed or taught what to do using an esoteric programming language or a limited set of commands that are only understood by highly trained and experienced technicians or engineers with years of experience. Most ma- chines and robots today are still relatively "dumb" copiers of human intelligence, unable to learn and think for themselves due to the procedural nature of most software control code. X In essence, almost all robots today require a great deal of human guidance in the form of soft- ware code that is played back over and over again. The majority of machine vision and object recognition applications today apply some form of mechanistic or deterministic property- matching, edge detection or colour scanning approach for identifying and distinguishing dif- ferent objects in a field of view. In reality, machine vision systems today can mimic human vi- sion, perception and identification to a rather crude degree of complexity depending on the human instructions provided in the software code, however, almost all vision systems today are slow and are quite poor at identification, recognition, learning and adapting to bad images and errors, compared to the human brain. Also, most vision systems require objects to have a colour that provides a strong contrast with a background colour, in order to detect edges relia- bly. In summary, today's procedural-software-driven computer controllers are limited by the amount of programming and decision-making "intelligence" passed onto it by a human pro- grammer or engineer, usually in the form of a single-threaded application or a complex list of step-by-step instructions executed in a continuous loop or triggered by sensor or communica- tion "interrupts". This method of control is suitable for most repetitive applications, however, new types of computer architecture based on how the human brain works and operates is un- chartered research area that needs exploration, modelling and experimentation in order to speed up shape or object recognition times and try to minimize the large amount of human ef- fort currently required to program, set up and commission "intelligent" machines that are ca- pable of learning new tasks and responding to errors or emergencies as competently as a hu- man being. The biggest challenge for the 21st century is to make robots and machines "intelligent" enough to learn how to perform tasks automatically and adapt to unforeseen operating conditions or errors in a robust and predictable manner, without the need for human guidance, instructions or programming. In other words: "Create robot controllers that are fast learners, able to learn and perform new tasks as easily and competently as a human being just by showing it how to do something only once. It should also learn from its own experiences, just like a young child learning and trying new skills." Note that a new-born baby knows practically nothing but is able to learn so many new things automatically, such as sounds, language, objects and names. This is a "tall order" and sounds very much like what you would expect to see in a "Star Wars" or "Star Trek" science fiction film, but who would have thought, 40 years ago, that most people could be instantly contacted from almost anywhere with portable mobile phones, or that you could send photos and letters to friends and family members instantly to almost anywhere in the world, or that programmable computers would be smaller than your fingernails? Who ever thought that a robot can automatically perform Cochlear surgery and detect miniscule force and torque changes as a robotic drill makes contact with a thin soft tissue membrane which must not be penetrated? (A task that even the best human surgeons cannot achieve con- sistently with manual drilling tools) Who would have imagined that robots would be assem- bling and creating most of the products we use every day, 40 years ago? At the current accel- erating rate of knowledge growth in the areas of robotics and mechatronics, it is not unreasonable to believe that "the best is yet to come" and that robotics technology will keep on improving to the point where almost all physical jobs will be completely automated and at very low cost. Mobile or "field" robotics is also a rapidly growing field of research, as more [...]... process sensory information and to control action pur- 8 Industrial Robotics: Theory, Modelling and Control posefully The development and maintenance of complex or large-scale software systems can benefit from domain-specific guidelines that promote code reuse and integration The Intelligent Machine Architecture (IMA) was designed to provide such guidelines in the domain of robot control [Kawamura, et al,... Central Executive Agent ISAC’s cognitive control function is modeled and implemented based on Baddeley and Hitch’s psychological human working memory model [Baddeley, 1986] Their model consists of the “central executive” which controls two working memory systems, i.e., phonological loop and visuo-spatial sketch pad (Figure 18) Industrial Robotics: Theory, Modelling and Control 22 Phonological Loop Central... body, sensor and AI software led to a wide variety of robotic systems For example, Sony’s QRIO (see Figure 1) can dance and play a trumpet The da Vinci robotic surgical system by Intuitive Surgical Inc (www.intuitivesurgical.com) can assist surgeon in laparoscopic (abdominal) surgery 1 Industrial Robotics: Theory, Modelling and Control 2 • The 1996 ICRA panel discussion (IEEE Robotics and Automation... situations such as aircraft-cockpit operation and air-traffic control [Kieras, et al, 1999] 6 Industrial Robotics: Theory, Modelling and Control Figure 5 EPIC architecture [Meyer & Kieras, 1997] 3.Multiagent Systems 3.1 Multiagent Systems In robotics, the term ‘agent’ is commonly used to mean an autonomous entity that is capable of acting in an environment and with other agents It can be a robot, a human... information about the current environment while the LTM holds learned behaviors, semantic knowledge, and past experience, i.e., episodes The WMS holds task-specific STM and LTM information and streamlines the information flow to the cognitive processes during the task 14 Industrial Robotics: Theory, Modelling and Control 5.1 Short-term memory: The Sensory EgoSphere Currently, we are using a structure called... Figure 14 (a) Example motions and their keytimes [Spratley, 2006], (b) Structure of PM data representation for Verbs and Adverbs 18 Industrial Robotics: Theory, Modelling and Control Each verb can have any number of adverbs, each of which relate to a particular space of the motion For example, the verb reach could have two adverbs: the first related to the direction of the reach and the second related to... [Philips, Industrial Robotics: Theory, Modelling and Control 20 2006] Section 7.1 in this chapter details working memory training and task learning conducted on ISAC Percepts SES Candidate Chunks List Learned Network Weights WM Memory chunks LTM Figure 16 Structure of the working memory system 6 Cognitive Control and Central Executive Agent 6.1 Cognitive Control Cognitive control in humans is a part of... current task, which is closely tied to the learning and execution of tasks Figure 7 depicts the key IMA agents and the memory structure within the ISAC cognitive architecture Industrial Robotics: Theory, Modelling and Control 10 Atomic Agents Head Agent Arm Agents Human Agent Stimuli Sensors Perception Encodings SES STM Attention Network Actuators Action Hand Agents CEA Self Agent Working Memory System... as reaching, pointing, track- ing, etc • Human Interaction – Performing such behaviors as face tracking, greeting, handshaking, waiting for commands, etc Industrial Robotics: Theory, Modelling and Control 24 Figure 20 shows the architecture being used to train each these WMS Figure 20 Control architecture used during working memory training During training, a reward rule is used to inform WMS how well... 2003] Industrial Robotics: Theory, Modelling and Control 12 Cognition Lower Cognition Reactive Deliberative Robotics Behavior-based Robot Self-Awareness Sense-Plan-Act Robot Cognitive Robot Higher Self-Conscious Conscious Robot Figure 9 Spectrum of cognition in robotics 4.2 Human agent The Human Agent (HA) comprises a set of agents that detect and keep track of human features and estimate the intentions . Industrial Robotics Theory, Modelling and Control Industrial Robotics Theory, Modelling and Control Edited by Sam Cubero pro literatur Verlag. necessary to process sensory information and to control action pur- 8 Industrial Robotics: Theory, Modelling and Control posefully. The development and maintenance of complex or large-scale. task situations such as aircraft-cockpit operation and air-traffic control [Kieras, et al, 1999]. 6 Industrial Robotics: Theory, Modelling and Control Figure 5. EPIC architecture [Meyer &