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

by Ge; Shuzhi Sam

• ISBN13: 9780849337482
• ISBN10: 0849337488
• Format: Hardcover
• Copyright: 2006-05-04
• Publisher: CRC Press

Summary

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.

Table of Contents

I Sensors and Sensor Fusion	1	(186)
Chapter 1 Visual Guidance for Autonomous Vehicles: Capability and Challenges	5	(36)
Andrew Shacklock, Jian Xu, and Han Wang
1.1 Introduction	6	(2)
1.1.1 Context	6	(1)
1.1.2 Classes of UGV	7	(1)
1.2 Visual Sensing Technology	8	(7)
1.2.1 Visual Sensors	8	(1)
1.2.1.1 Passive imaging	9	(1)
1.2.1.2 Active sensors	10	(2)
1.2.2 Modeling of Image Formation and Calibration	12	(1)
1.2.2.1 The ideal pinhole model	12	(1)
1.2.2.2 Calibration	13	(2)
1.3 Visual Guidance Systems	15	(18)
1.3.1 Architecture	15	(1)
1.3.2 World Model Representation	15	(2)
1.3.3 Physical Limitations	17	(2)
1.3.4 Road and Vehicle Following	19	(1)
1.3.4.1 State-of-the-art	19	(1)
1.3.4.2 A road camera model	21	(2)
1.3.5 Obstacle Detection	23	(1)
1.3.5.1 Obstacle detection using range data	23	(1)
1.3.5.2 Stereo vision	24	(1)
1.3.5.3 Application examples	26	(2)
1.3.6 Sensor Fusion	28	(5)
1.4 Challenges and Solutions	33	(3)
1.4.1 Terrain Classification	33	(1)
1.4.2 Localization and 3D Model Building from Vision	34	(2)
1.5 Conclusion	36	(1)
Acknowledgments	37	(1)
References	37	(3)
Biographies	40	(1)
Chapter 2 Millimeter Wave RADAR Power-Range Spectra Interpretation for Multiple Feature Detection	41	(58)
Martin Adams and Ebi Jose
2.1 Introduction	42	(2)
2.2 Related Work	44	(1)
2.3 FMCW RADAR Operation and Range Noise	45	(3)
2.3.1 Noise in FMCW Receivers and Its Effect on Range Detection	47	(1)
2.4 RADAR Range Spectra Interpretation	48	(16)
2.4.1 RADAR Range Equation	50	(1)
2.4.2 Interpretation of RADAR Noise	50	(1)
2.4.2.1 Thermal noise	51	(1)
2.4.2.2 Phase noise	51	(2)
2.4.3 Noise Analysis during Target Absence and Presence	53	(1)
2.4.3.1 Power-noise estimation in target absence	53	(1)
2.4.3.2 Power-noise estimation in target presence	57	(3)
2.4.4 Initial Range Spectra Prediction	60	(4)
2.5 Constant False Alarm Rate Processor for True Target Range Detection	64	(4)
2.5.1 The Effect of the High Pass Filter on CFAR	65	(1)
2.5.1.1 Missed detections with CFAR	66	(1)
2.5.1.2 False alarms with CFAR	67	(1)
2.6 Target Presence Probability Estimation for True Target Range Detection	68	(8)
2.6.1 Target Presence Probability Results	72	(2)
2.6.2 Merits of the Proposed Algorithm over Other Feature Extraction Techniques	74	(2)
2.7 Multiple Line-of-Sight Targets — RADAR Penetration	76	(3)
2.8 RADAR-Based Augmented State Vector	79	(10)
2.8.1 Process Model	80	(4)
2.8.2 Observation (Measurement) Model	84	(1)
2.8.2.1 Predicted power observation formulation	85	(4)
2.9 Multi-Target Range Bin Prediction — Results	89	(4)
2.10 Conclusions	93	(1)
Acknowledgments	94	(1)
References	95	(2)
Biographies	97	(2)
Chapter 3 Data Fusion via Kalman Filter: GPS and INS	99	(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