Autonomous Mobile Robots: Sensing, Control, Decision Making and Applications

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ISBN: 9780849337482 | 0849337488
Cover: Hardcover
Copyright: 5/4/2006

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It has long been the goal of engineers to develop tools that enhance our ability to do work, increase our quality of life, or perform tasks that are either beyond our ability, too hazardous, or too tedious to be left to human efforts. Autonomous mobile robots are the culmination of decades of research and development, and their potential is seemingly unlimited.Roadmap to the FutureServing as the first comprehensive reference on this interdisciplinary technology, Autonomous Mobile Robots: Sensing, Control, Decision Making, and Applications authoritatively addresses the theoretical, technical, and practical aspects of the field. The book examines in detail the key components that form an autonomous mobile robot, from sensors and sensor fusion to modeling and control, map building and path planning, and decision making and autonomy, and to the final integration of these components for diversified applications.Trusted GuidanceA duo of accomplished experts leads a team of renowned international researchers and professionals who provide detailed technical reviews and the latest solutions to a variety of important problems. They share hard-won insight into the practical implementation and integration issues involved in developing autonomous and open robotic systems, along with in-depth examples, current and future applications, and extensive illustrations.For anyone involved in researching, designing, or deploying autonomous robotic systems, Autonomous Mobile Robots is the perfect resource.

I Sensors and Sensor Fusion

(186)

Chapter 1 Visual Guidance for Autonomous Vehicles: Capability and Challenges

(36)

Andrew Shacklock, Jian Xu, and Han Wang

1.1 Introduction

(2)

1.1.1 Context

(1)

1.1.2 Classes of UGV

(1)

1.2 Visual Sensing Technology

(7)

1.2.1 Visual Sensors

(1)

1.2.1.1 Passive imaging

(1)

1.2.1.2 Active sensors

(2)

1.2.2 Modeling of Image Formation and Calibration

(1)

1.2.2.1 The ideal pinhole model

(1)

1.2.2.2 Calibration

(2)

1.3 Visual Guidance Systems

(18)

1.3.1 Architecture

(1)

1.3.2 World Model Representation

(2)

1.3.3 Physical Limitations

(2)

1.3.4 Road and Vehicle Following

(1)

1.3.4.1 State-of-the-art

(1)

1.3.4.2 A road camera model

(2)

1.3.5 Obstacle Detection

(1)

1.3.5.1 Obstacle detection using range data

(1)

1.3.5.2 Stereo vision

(1)

1.3.5.3 Application examples

(2)

1.3.6 Sensor Fusion

(5)

1.4 Challenges and Solutions

(3)

1.4.1 Terrain Classification

(1)

1.4.2 Localization and 3D Model Building from Vision

(2)

1.5 Conclusion

(1)

Acknowledgments

(1)

References

(3)

Biographies

(1)

Chapter 2 Millimeter Wave RADAR Power-Range Spectra Interpretation for Multiple Feature Detection

(58)

Martin Adams and Ebi Jose

2.1 Introduction

(2)

2.2 Related Work

(1)

2.3 FMCW RADAR Operation and Range Noise

(3)

2.3.1 Noise in FMCW Receivers and Its Effect on Range Detection

(1)

2.4 RADAR Range Spectra Interpretation

(16)

2.4.1 RADAR Range Equation

(1)

2.4.2 Interpretation of RADAR Noise

(1)

2.4.2.1 Thermal noise

(1)

2.4.2.2 Phase noise

(2)

2.4.3 Noise Analysis during Target Absence and Presence

(1)

2.4.3.1 Power-noise estimation in target absence

(1)

2.4.3.2 Power-noise estimation in target presence

(3)

2.4.4 Initial Range Spectra Prediction

(4)

2.5 Constant False Alarm Rate Processor for True Target Range Detection

(4)

2.5.1 The Effect of the High Pass Filter on CFAR

(1)

2.5.1.1 Missed detections with CFAR

(1)

2.5.1.2 False alarms with CFAR

(1)

2.6 Target Presence Probability Estimation for True Target Range Detection

(8)

2.6.1 Target Presence Probability Results

(2)

2.6.2 Merits of the Proposed Algorithm over Other Feature Extraction Techniques

(2)

2.7 Multiple Line-of-Sight Targets — RADAR Penetration

(3)

2.8 RADAR-Based Augmented State Vector

(10)

2.8.1 Process Model

(4)

2.8.2 Observation (Measurement) Model

(1)

2.8.2.1 Predicted power observation formulation

(4)

2.9 Multi-Target Range Bin Prediction — Results

(4)

2.10 Conclusions

(1)

Acknowledgments

(1)

References

(2)

Biographies

(2)

Chapter 3 Data Fusion via Kalman Filter: GPS and INS

(50)

Jingrong Cheng, Yu Lu, Elmer R. Thomas, and Jay A. Farrell

3.1 Introduction

100

(2)

3.1.1 Data Fusion—GPS and INS

101

(1)

3.2 Kalman Filter

102

(11)

3.2.1 Stochastic Process Models

102

(1)

3.2.1.1 Computation of Φ and Qk

104

(1)

3.2.2 Basic KF

105

(1)

3.2.2.1 Implementation issues

107

(2)

3.2.3 Extended KF

109

(4)

3.3 GPS Navigation System

113

(15)

3.3.1 GPS Measurements

113

(2)

3.3.2 Single-Point GPS Navigation Solution

115

(4)

3.3.3 KF for Stand-Alone GPS Solutions

119

(1)

3.3.3.1 Clock model

120

(1)

3.3.3.2 Stationary user (P model)

121

(1)

3.3.3.3 Low dynamic user (PV Model)

122

(1)

3.3.3.4 High dynamic user (PVA model)

122

(1)

3.3.3.5 GPS KF examples

123

(1)

3.3.3.6 Summary

127

(1)

3.4 Inertial Navigation System

128

(4)

3.4.1 Strapdown System Mechanizations

129

(2)

3.4.2 Error Model of INS System

131

(1)

3.4.3 EKF Latency Compensation

131

(1)

3.5 Integration of GPS and INS

132

(10)

3.5.1 INS with GPS Resetting

133

(1)

3.5.2 GPS Aided INS

133

(1)

3.5.2.1 Loosely coupled system

134

(1)

3.5.2.2 Tightly coupled system

135

(7)

3.6 Chapter Summary

142

(1)

Acknowledgments

143

(1)

References

144

(2)

Biographies

146

(3)

Chapter 4 Landmarks and Triangulation in Navigation

149

(38)

Huosheng Hu, Julian Ryde, and Jiali Shen

4.1 Introduction

150

(2)

4.2 Landmark-Based Navigation

152

(2)

4.3 Laser Scanner and Retro-Reflective Landmarks

154

(6)

4.3.1 Laser Scanner and Angle Observation

154

(1)

4.3.2 Triangulation Algorithm

155

(2)

4.3.3 KF-Based Navigation Algorithm

157

(2)

4.3.4 Implementation and Results

159

(1)

4.4 Vision and Digital Landmarks

160

(13)

4.4.1 Landmark Recognition

161

(1)

4.4.1.1 Region finding module

161

(1)

4.4.1.2 Digits finding module

163

(1)

4.4.1.3 Digits recognition module

163

(2)

4.4.2 Position Estimation

165

(1)

4.4.2.1 Triangulation method

166

(2)

4.4.3 Least Square Estimator (LSE)

168

(1)

4.4.3.1 Single-landmark LSE (SLSE)

169

(1)

4.4.3.2 Dual-landmark LSE (DLSE)

169

(1)

4.4.4 Implementation and Results

170

(3)

4.5 SICK Laser Scanner and Geometric Landmarks

173

(10)

4.5.1 Circular Hough Transform

174

(1)

4.5.2 Least Squares Fitting of Circles

175

(4)

4.5.3 Cooperative Position Estimation

179

(1)

4.5.4 Implementation and Results

180

(3)

4.6 Conclusions

183

(1)

References

184

(3)

II Modeling and Control

187

(144)

Chapter 5 Stabilization of Nonholonomic Systems

191

(38)

Alessandro Astolfi

5.1 Introduction

192

(1)

5.2 Preliminaries and Definitions

193

(4)

5.3 Discontinuous Stabilization

197

(11)

5.3.1 Stabilization of Discontinuous Nonholonomic Systems

198

(5)

5.3.2 The a Process

203

(1)

5.3.3 The Issue of Asymptotic Stability

204

(3)

5.3.4 An Algorithm to Design Almost Stabilizers

207

(1)

5.4 Chained Systems and Power Systems

208

(1)

5.5 Discontinuous Control of Chained Systems

209

(4)

5.5.1 An Example: A Car-Like Vehicle

210

(3)

5.5.2 Discussion

213

(1)

5.6 Robust Stabilization — Part I

213

(3)

5.7 Robust Stabilization — Part II

216

(3)

5.7.1 The Local Controller

216

(1)

5.7.2 The Global Controller

217

(1)

5.7.3 Definition of the Hybrid Controller and Main Result

218

(1)

5.7.4 Discussion

218

(1)

5.8 Robust Stabilitazion — Part III

219

(2)

5.8.1 Robust Sampled-Data Control of Power Systems

219

(1)

5.8.2 An Example: A Car-Like Vehicle Revisited

220

(1)

5.8.3 Discussion

221

(1)

5.9 Conclusions

221

(2)

Acknowledgments

223

(1)

References

223

(3)

Biography

226

(3)

Chapter 6 Adaptive Neural-Fuzzy Control of Nonholonomic Mobile Robots

229

(38)

Fan Hong, Shuzhi Sam Ge, Frank L. Lewis, and Tong Heng Lee

6.1 Introduction

229

(3)

6.2 Dynamics of Nonholonomic Mobile Robots

232

(5)

6.3 Multi-Layer NF Systems

237

(5)

6.4 Adaptive NF Control Design

242

(14)

6.5 Simulation Studies

256

(5)

6.6 Conclusion

261

(1)

References

262

(2)

Biographies

264

(3)

Chapter 7 Adaptive Control of Mobile Robots Including Actuator Dynamics

267

(28)

Zhuping Wang, Chun-Yi Su, and Shuzhi Sam Ge

7.1 Introduction

267

(2)

7.2 Dynamic Modeling and Properties

269

(8)

7.3 Control System Design

277

(8)

7.3.1 Kinematic and Dynamic Subsystems

277

(2)

7.3.2 Control Design at the Actuator Level

279

(6)

7.4 Simulation

285

(4)

7.5 Conclusion

289

(1)

References

290

(2)

Biographies

292

(3)

Chapter 8 Unified Control Design for Autonomous Car-Like Vehicle Tracking Maneuvers

295

(36)

Danwei Wang and Minhtuan Pham

8.1 Introduction

295

(2)

8.2 Dynamics of Tracking Maneuvers

297

(2)

8.2.1 Vehicle Kinematics and Dynamics

297

(1)

8.2.2 Dynamics of Tracking Maneuvers

298

(1)

8.3 A Unified Tracking Controller

299

(15)

8.3.1 Kinematics-Based Tracking Controller

308

(2)

8.3.2 Dynamics-Based Tracking Controller

310

(3)

8.3.3 Requirement of Measurements

313

(1)

8.4 Tracking Performance Evaluation

314

(10)

8.4.1 Forward Tracking Control

315

(1)

8.4.1.1 Influence of parameter p

315

(1)

8.4.1.2 Influence of parameter l

318

(2)

8.4.2 Backward Tracking Control

320

(1)

8.4.2.1 Influence of parameter p

321

(1)

8.4.2.2 Influence of parameter l

324

(1)

8.5 Conclusions

324

(3)

References

327

(1)

Bibliographies

328

(3)

III Map Building and Path Planning

331

(130)

Chapter 9 Map Building and SLAM Algorithms

335

(38)

José A. Castellanos, José Neira, and Juan D. Tardós

9.1 Introduction

335

(4)

9.2 SLAM Using the Extended Kalman Filter

339

(7)

9.2.1 Initialization

340

(1)

9.2.2 Vehicle Motion: The EKF Prediction Step

341

(1)

9.2.3 Data Association

342

(2)

9.2.4 Map Update: The EKF Estimation Step

344

(1)

9.2.5 Adding Newly Observed Features

344

(1)

9.2.6 Consistency of EKF–SLAM

345

(1)

9.3 Data Association in SLAM

346

(12)

9.3.1 Individual Compatibility Nearest Neighbor

346

(1)

9.3.2 Joint Compatibility

347

(3)

9.3.3 Relocation

350

(4)

9.3.4 Locality

354

(4)

9.4 Mapping Large Environments

358

(8)

9.4.1 Building Independent Local Maps

359

(1)

9.4.2 Local Map Joining

359

(2)

9.4.3 Matching and Fusion after Map Joining

361

(1)

9.4.4 Closing a Large Loop

361

(4)

9.4.5 Multi-robot SLAM

365

(1)

9.5 Conclusions

366

(1)

Appendix: Transformations in 2D

367

(1)

Acknowledgment

368

(1)

References

368

(3)

Bibliography

371

(2)

Chapter 10 Motion Planning: Recent Developments

373

(44)

Hector H. González-Baños, David Hsu, and Jean-Claude Latombe

10.1 Introduction

374

(1)

10.2 Path Planning

375

(11)

10.2.1 Configuration Space

376

(1)

10.2.2 Early Approaches

377

(1)

10.2.2.1 Roadmap

378

(1)

10.2.2.2 Cell decomposition

379

(1)

10.2.2.3 Potential field

380

(1)

10.2.3 Random Sampling

381

(1)

10.2.3.1 Multi-query planning

382

(1)

10.2.3.2 Single-query planning

383

(1)

10.2.3.3 Probabilistic completeness

385

(1)

10.2.3.4 Advantages of random sampling

386

(1)

10.3 Motion Planning under Kinematic and Dynamic Constraints

386

(7)

10.3.1 Kinematic and Dynamic Constraints

386

(3)

10.3.2 General Approaches

389

(1)

10.3.3 Random Sampling

390

(1)

10.3.4 Case Studies on Real Robotic Systems

390

(1)

10.3.4.1 Motion planning of trailer-trucks for transporting Airbus A380 components

391

(1)

10.3.4.2 A space robotics test-bed

392

(1)

10.4 Motion Planning under Visibility Constraints

393

(16)

10.4.1 Sensor Placement

394

(1)

10.4.1.1 Sampling

395

(1)

10.4.1.2 Near-optimal set covers

397

(1)

10.4.1.3 Extensions

397

(1)

10.4.2 Indoor Exploration

398

(1)

10.4.2.1 Constraints on the NBW

398

(1)

10.4.2.2 Safe regions

399

(1)

10.4.2.3 Image registration

400

(1)

10.4.2.4 Evaluating next views

401

(1)

10.4.2.5 Computing the NBV

401

(1)

10.4.2.6 Extensions

402

(1)

10.4.3 Target Tracking

403

(1)

10.4.3.1 State transition equations

403

(1)

10.4.3.2 Visibility constraints

404

(1)

10.4.3.3 Tracking strategies

404

(1)

10.4.3.4 Backchaining and dynamic programming

407

(1)

10.4.3.5 Escape-time approximations

407

(1)

10.4.3.6 Robot localization

408

(1)

10.4.3.7 Other results and extensions

408

(1)

10.5 Other Important Issues

409

(1)

10.6 Conclusion

410

(1)

References

411

(5)

Biographies

416

(1)

Chapter 11 Multi-Robot Cooperation

417

(44)

Rafael Fierro, Luiz Chaimowicz, and Vijay Kumar

11.1 Introduction

418

(1)

11.2 Cooperative Multi-Robot Systems

419

(4)

11.2.1 Motion Planning and Control

421

(1)

11.2.1.1 Explicit approaches

421

(1)

11.2.1.2 Implicit approaches

422

(1)

11.2.2 Graph Theory Preliminaries

422

(1)

11.3 Formation Control

423

(9)

11.3.1 Full-State Linearization via Dynamic Feedback

425

(3)

11.3.2 Formation Reconfiguration

428

(4)

11.4 Optimization-Based Cooperative Control

432

(8)

11.4.1 Control of a Chain of Robots

437

(3)

11.5 Applications

440

(13)

11.5.1 Cooperative Manipulation

440

(1)

11.5.1.1 Dynamic role assignment

440

(1)

11.5.1.2 Modeling

442

(4)

11.5.2 Multi-Robot Perimeter Detection and Tracking

446

(1)

11.5.2.1 Cooperative hybrid controller

449

(1)

11.5.2.2 Random coverage controller

449

(1)

11.5.2.3 Potential field controller

452

(1)

11.5.2.4 Tracking controller

453

(1)

11.6 Conclusions

453

(2)

Acknowledgments

455

(1)

References

455

(3)

Biographies

458

(3)

IV Decision Making and Autonomy

461

(110)

Chapter 12 Knowledge Representation and Decision Making for Mobile Robots

465

(36)

Elena Messina and Stephen Balakirsky

12.1 Introduction

466

(1)

12.2 Introduction and a Brief Survey of Representation Approaches

466

(11)

12.2.1 Grounding Representation

466

(1)

12.2.2 Representation Approaches

467

(1)

12.2.2.1 Spatial representations

467

(1)

12.2.2.2 Topological representations

472

(1)

12.2.2.3 Symbolic representations

472

(1)

12.2.2.4 No representation

474

(1)

12.2.3 Multi-Representational Systems

474

(1)

12.2.4 Decision Making

475

(2)

12.3 Case Study: Knowledge Representation and Decision Making within a 4D/RCS

477

(12)

12.3.1 Procedural vs. Declarative Knowledge in 4D/RCS

480

(1)

12.3.1.1 Procedural knowledge

480

(1)

12.3.1.2 Declarative knowledge

483

(2)

12.3.2 Additional Considerations

485

(1)

12.3.2.1 Integration considerations

485

(1)

12.3.2.2 Integration within a single representation

486

(1)

12.3.2.3 Integration among disparate representations

486

(1)

12.3.2.4 Integration of decision systems

488

(1)

12.3.2.5 Implications for system maintainability

488

(1)

12.3.2.6 Implications for perception design

489

(1)

12.4 An Implementation Example

489

(4)

12.5 Conclusion

493

(1)

References

494

(4)

Biographies

498

(3)

Chapter 13 Algorithms for Planning under Uncertainty in Prediction and Sensing

501

(48)

Jason M. O'Kane, Benjamin Tovar, Peng Cheng, and Steven M. LaValle

13.1 Introduction and Preliminaries

502

(2)

13.2 Planning under Prediction Uncertainty

504

(21)

13.2.1 Making a Single Decision

505

(1)

13.2.1.1 Including an observation

506

(1)

13.2.1.2 Criticisms of decision theory

507

(1)

13.2.2 Making a Sequence of Decisions

508

(3)

13.2.3 Methods for Finding Optimal Solutions

511

(1)

13.2.3.1 Value iteration

511

(1)

13.2.3.2 Policy iteration

514

(1)

13.2.3.3 Other methods

515

(1)

13.2.4 Methods for Finding Approximate Solutions

516

(1)

13.2.4.1 Certainty equivalent control

516

(1)

13.2.4.2 Limited lookahead

516

(1)

13.2.5 Conquering Continuous Spaces

517

(3)

13.2.6 Variations

520

(1)

13.2.6.1 Infinite horizon models

520

(1)

13.2.6.2 Reinforcement learning

521

(1)

13.2.6.3 Additional decision makers

523

(2)

13.3 Planning under Sensing Uncertainty

525

(16)

13.3.1 Discrete State Spaces

526

(1)

13.3.1.1 Sensors

526

(1)

13.3.1.2 Definition of the information space

527

(1)

13.3.2 Deriving Information States

528

(5)

13.3.3 Continuous State Spaces

533

(1)

13.3.3.1 Sensors

533

(1)

13.3.3.2 Discrete-stage information spaces

533

(1)

13.3.3.3 Continuous-time information spaces

534

(1)

13.3.4 Examples of Planning in the Information Space

535

(1)

13.3.4.1 Moving in an L-shaped corridor

535

(1)

13.3.4.2 The Kalman filter

537

(1)

13.3.4.3 Sensorless manipulation

539

(2)

13.4 Conclusion and Bibliographical Remarks

541

(1)

References

541

(5)

Biographies

546

(3)

Chapter 14 Behavior-Based Coordination in Multi-Robot Systems

549

(22)

Chris Jones and Maja J. Mataric

14.1 Overview of Robot Control Architectures

550

(3)

14.1.1 Single Robot Control

550

(2)

14.1.2 Behavior-Based Control

552

(1)

14.2 From Single Robot Control to Multi-Robot Control

553

(2)

14.2.1 Advantages and Challenges of Multi-Robot Systems

554

(1)

14.2.2 Necessity of Coordination in MRS

554

(1)

14.3 From Local Interactions to Global Coordination

555

(6)

14.3.1 Interaction through the Environment

555

(1)

14.3.2 Interaction through the Environment Case Study: Object Clustering

556

(1)

14.3.3 Interaction through Sensing

557

(1)

14.3.4 Interaction through Sensing Case Study: Formation Marching

558

(1)

14.3.5 Interaction through Communication

559

(1)

14.3.6 Interaction through Communication Case Study: Multiple Target Tracking

560

(1)

14.4 Formal Design and Analysis of MRS

561

(3)

14.4.1 Analysis of MRS Using Macroscopic Models

561

(1)

14.4.2 Analysis of MRS Using Microscopic Models

562

(1)

14.4.3 Principled Synthesis of MRS Controllers

562

(2)

14.5 Conclusions and the Future of Multi-Robot Systems

564

(1)

References

564

(4)

Biographies

568

(3)

V System Integration and Applications

571

(126)

Chapter 15 Integration for Complex Consumer Robotic Systems: Case Studies and Analysis

573

(40)

Mario E. Munich, James P. Ostrowski, and Paolo Pirjanian

15.1 Introduction

574

(1)

15.2 Background

575

(2)

15.3 Related Work

577

(1)

15.4 System Integration as a Multi-Objective Optimization Problem

578

(2)

15.4.1 Complexity

578

(1)

15.4.2 Performance

579

(1)

15.4.3 Price

579

(1)

15.5 Tradeoffs and Challenges in Integration of a Complex Autonomous System

580

(4)

15.5.1 Component Simplicity vs. System Complexity

580

(1)

15.5.1.1 Hardware vs. software

580

(1)

15.5.1.2 Generalization vs. specialization

581

(1)

15.5.1.3 Abstraction and aggregation

582

(1)

15.5.2 Testing, Testing, Testing...

583

(1)

15.6 Integration through Architecture

584

(3)

15.7 A Software Architecture for Consumer Robotic Systems

587

(8)

15.7.1 ERSP in the Role of the System Integration Architecture?

589

(5)

15.7.2 Development Tools

594

(1)

15.8 Case Study 1: Sony AIBO

595

(3)

15.9 Case Study 2: Autonomous Capabilities for Vacuum Cleaning

598

(11)

15.9.1 Lessons Learned

606

(1)

15.9.2 Embedded Implementation of vSLAM

607

(2)

15.10 Conclusions

609

(1)

References

609

(1)

Biographies

610

(3)

Chapter 16 Automotive Systems/Robotic Vehicles

613

(42)

Michel R. Parent and Stéphane R. Petti

16.1 Introduction

614

(8)

16.1.1 A Key Product of the 20th Century

614

(1)

16.1.2 Problems with Safety

615

(1)

16.1.3 Problems of Congestion

616

(1)

16.1.4 Problems with Emissions and Nuisances

617

(2)

16.1.5 Car-Sharing and Cybercars

619

(1)

16.1.6 The Future of the Automobile

620

(2)

16.2 The Automotive Sensors

622

(15)

16.2.1 Ultrasound Sensors

622

(1)

16.2.2 Inertial Sensors

623

(1)

16.2.2.1 Gyroscopes — gyrometers

623

(1)

16.2.2.2 Accelerometers

624

(1)

16.2.3 Laser Detection and Ranging

624

(1)

16.2.3.1 Range measurement

625

(1)

16.2.3.2 Azimuth measurement

625

(2)

16.2.4 Radar

627

(1)

16.2.4.1 Range measurement

627

(1)

16.2.4.2 Azimuth measurement

628

(1)

16.2.4.3 Automotive radars

629

(1)

16.2.5 Vision Sensor

629

(1)

16.2.5.1 Principles

629

(1)

16.2.5.2 Specificities of automotive applications

629

(1)

16.2.5.3 Stereovision systems

630

(1)

16.2.5.4 Future vision processors

631

(1)

16.2.6 Global Navigation Satellite-Based System

632

(1)

16.2.6.1 Global positioning system

632

(1)

16.2.6.2 GLONASS

633

(1)

16.2.6.3 GALILEO

633

(1)

16.2.6.4 GPS receiver-based localization

633

(1)

16.2.6.5 Basic principle

634

(1)

16.2.6.6 Code range positioning

634

(1)

16.2.6.7 Code phase differential GPS

635

(1)

16.2.6.8 Augmented differential GPS

635

(1)

16.2.6.9 Internet-based differential GPS

636

(1)

16.2.6.10 Carrier phase differential GPS

636

(1)

16.2.6.11 Sensor fusion for improved localization

637

(1)

16.3 Automotive Actuators

637

(5)

16.3.1 Power Train Actuators

638

(1)

16.3.2 Brake Actuators

639

(1)

16.3.3 Steering Actuators

640

(2)

16.4 Vehicle Control

642

(9)

16.4.1 Longitudinal Control

643

(1)

16.4.1.1 Adaptive cruise control

643

(1)

16.4.1.2 Precrash system/automatic emergency braking

644

(1)

16.4.1.3 Stop and go

646

(1)

16.4.2 Lateral Control

646

(1)

16.4.3 Full Vehicle Control

647

(4)

References

651

(3)

Biographies

654

(1)

Chapter 17 Intelligent Systems

655

(42)

Sesh Commuri, James S. Albus, and Anthony Barbera

17.1 Architectural Requirements for Intelligent UGVs

656

(1)

17.2 Background on Intelligent Systems

657

(1)

17.3 4D/RCS Architecture and Methodology

658

(18)

17.3.1 4D/RCS Architecture

658

(5)

17.3.2 4D/RCS Methodology

663

(2)

17.3.3 Representing Knowledge in 4D/RCS

665

(1)

17.3.3.1 Procedural knowledge

668

(1)

17.3.3.2 Declarative knowledge

671

(5)

17.4 Experimental Results

676

(14)

17.4.1 Implementation of Reconfigurable UGV Teams at the OU

677

(1)

17.4.1.1 AL² architecture

677

(1)

17.4.1.2 Hardware and software design methodology

680

(1)

17.4.1.3 Design and implementation of UGV teams

681

(5)

17.4.2 Demo III Experimental Unmanned Vehicle (XUV) Project at NIST

686

(4)

17.5 Current Research and Future Directions

690

(1)

17.6 Conclusions

691

(1)

Acknowledgments

692

(1)

References

692

(4)

Biographies

696

(1)

Index

697

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Autonomous Mobile Robots: Sensing, Control, Decision Making and Applications

Summary

Table of Contents

Supplemental Materials