Control in Robotics and Automation,9780122818455

Control in Robotics and Automation

by ; ;
ISBN13: 9780122818455
ISBN10: 0122818458
Format: Hardcover
Pub. Date: 1999-03-01
Publisher(s): Academic Pr
List Price: $120.00

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Summary

Microcomputer technology and micromechanical design have contributed to recent rapid advances in Robotics. Particular advances have been made in sensor technology that allow robotic systems to gather data and react "intelligently" in flexible manufacturing systems. The analysis and recording of the data are vital to controlling the robot. In order to solve problems in control and planning for a Robotic system it is necessary to meet the growing need for the integration of sensors in to the system. Control in Robotics and Automation addresses this need. This book covers integration planning and control based on prior knowledge and real-time sensory information. A new task-oriented approach to sensing, planning and control introduces an event-based method for system design together with task planning and three dimensional modeling in the execution of remote operations. Typical remote systems are teleoperated and provide work efficiencies that are on the order of ten times slower than what is directly achievable by humans. Consequently, the effective integration of automation into teleoperated remote systems offers potential to improve remote system work efficiency. The authors introduce visually guided control systems and study the role of computer vision in autonomously guiding a robot system. * Sensor-Based Planning and Control in an Event-Based Approach * Visually Guided Sensing and Control * Multiple Sensor Fuson in Planning and Control * System Integration and Implementation * Practical Applications

Table of Contents

Preface ix(2)
Contributors xi
Section I INTRODUCTION 1(56)
1. Sensor-Based Planning and Control for Robotic Systems: An Event-Based Approach
3(54)
Xi
Tarn
1. Introduction
3(2)
2. Event-Based Planning and Control
5(5)
3. Event-Based Motion Planning and Control for a Robot Arm
10(13)
4. Event-Based Planning and Control for Multirobot Coordination
23(19)
5. Implementation of Event-Based Planning and Control
42(9)
6. Conclusions
51(1)
References
52(5)
Section II VISUALLY GUIDED SENSING AND CONTROL 57(88)
2. Observer-Based Visual Servoing
59(32)
Hashimoto
1. Introduction
60(2)
2. Mathematical Formulation
62(3)
3. Jacobians
65(3)
4. Nonlinear Control Law
68(8)
5. Linearized Controller
76(1)
6. Experiments
76(11)
7. Conclusions
87(1)
References
88(3)
3. Using Active Deformable Models in Visual Servoing
91(24)
Sullivan
Papanikolopoulos
Singh
Pavlidis
1. Introduction
91(1)
2. Importance of the Visual Servoing Problem
92(1)
3. Issues
93(2)
4. Previous Work
95(1)
5. Proposed Approach
95(8)
6. The Minnesota Robotic Visual Tracker
103(2)
7. Experiments
105(5)
8. Discussion
110(1)
9. Future Work
111(1)
10. Conclusions
112(1)
References
112(3)
4. Visually Guided Tracking and Manipulation
115(30)
Lei
Ghosh
1. Introduction
115(2)
2. Modeling of the Tracking and Grasping System
117(4)
3. Estimation of the Motion Field of the Reference Point
121(9)
4. The Control Design for Tracking and Grasping
130(9)
5. Simulation Results and Discussion
139(3)
6. Conclusions
142(1)
References
143(2)
Section III MULTIPLE SENSOR FUSION IN PLANNING AND CONTROL 145(98)
5. Complementary Sensor Fusion in Robotic Manipulation
147(36)
Ghosh
Yu
Xiao
Xi
Tarn
1. Introduction
148(3)
2. Grasping
151(18)
3. Tracking an Unknown Trajectory on a Surface
169(9)
4. Conclusion
178(1)
References
179(2)
5. Appendix
181(2)
6. Feedback Control with Force and Visual Sensor Fusion
183(34)
Nelson
Khosla
1. Introduction
184(2)
2. Previous Work
186(2)
3. Sensor Resolvability
188(14)
4. Visual Servoing Formulation
202(2)
5. Vision-Force Servoing
204(3)
6. Experimental Results
207(6)
7. Conclusion
213(1)
References
214(3)
7. Sensor-Referenced Impact Control in Robotics
217(26)
Wu
Tarn
Xi
Isidori
1. Introduction
217(1)
2. History and Background
218(2)
3. Impact Dynamics
220(2)
4. Robust Impact Control
222(4)
5. Switching Control
226(7)
6. Experiments
233(2)
7. Summary
235(5)
References
240(1)
Suggested Readings
241(2)
Section IV SYSTEM INTEGRATION, MODELING, AND CONTROLLER DESIGN 243(68)
8. A Modular Approach to Sensor Integration
245(24)
Anderson
1. Introduction
245(1)
2. Terminology
246(1)
3. The Problem of Algebraic Loops
246(3)
4. Scattering Theory
249(3)
5. Applying Scattering Theory to Robot Modules
252(3)
6. Computing the Jacobian Scattering Operator
255(3)
7. Discretizing Dynamic Networks
258(2)
8. Imposing Nonlinear Constraints Using Sensor Feedback
260(4)
9. Implementation
264(1)
10. Examples
264(3)
11. Conclusions
267(1)
References
267(2)
9. A Circuit-Theoretic Analysis of Robot Dynamics and Control
269(16)
Arimoto
1. Introduction
269(2)
2. Passivity of Robot Dynamics and Nonlinear Position-Dependent Circuits
271(3)
3. SP-ID Control
274(1)
4. Adaptability and Learnability
275(3)
5. Realization of Friction/Gravity-Free Robots
278(1)
6. Generalization of Impedance Matching to Nonlinear Dynamics
279(2)
7. Learning as Making Progress Toward Impedance Matching
281(2)
8. Conclusion
283(1)
References
283(2)
10. Sensor-Based Planning and Control in Telerobotics
285(26)
Hamel
1. Introduction
285(1)
2. History of Teleoperations and Remote Handling
286(9)
3. The Notion of Telerobotics
295(2)
4. Typical Application Domain
297(3)
5. A Robust Telerobotic Concept
300(2)
6. Current Research in Integrated D&D Telerobotics
302(5)
7. Key Remaining Challenges and Summary
307(1)
References
308(3)
Section V APPLICATION 311(108)
11. Automated Integration of Multiple Sensors
313(34)
Baker
1. Introduction
313(1)
2. Background
314(6)
3. Automated MSI System
320(10)
4. Target Sensor Domains
330(2)
5. Sensor Anomaly Correction
332(2)
6. Empirical Evaluation
334(4)
7. Validation
338(5)
8. Sumary
343(1)
References
344(3)
12. Robotics with Perception and Action Nets
347(34)
Lee
Ro
1. Introduction
348(1)
2. Pan Architecture
349(3)
3. Uncertainty Management
352(8)
4. Error Monitoring and Recovery
360(2)
5. Planetary Robotic Science Sampling
362(3)
6. Simulation
365(8)
7. Experimentation
373(4)
8. Conclusion
377(1)
References
378(1)
Appendix
379(2)
13. A Fuzzy Behaviorist Approach to Sensor-Based Reasoning and Robot Navigation
381(38)
Pin
1. Introduction
381(3)
2. Fuzzy Behaviorist Approach and Rule Generation for Vehicle Navigation
384(11)
3. Rule Base Generation Method and Automated System
395(2)
4. Sample Experimental Results
397(10)
5. Augmenting the System with Memory and Memory-Processing Behaviors
407(9)
6. Concluding Remarks
416(1)
References
417(2)
Index 419

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