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E-raamat: Navigation and Control of Autonomous Marine Vehicles

Edited by (Plymouth University, UK), Edited by (National Institute of Technology Rourkela, Department of Electrical Engineering, India)
  • Formaat: EPUB+DRM
  • Sari: Transportation
  • Ilmumisaeg: 25-Apr-2019
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785613395
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Navigation and Control of Autonomous Marine VehiclesSuurem pilt
  • Formaat: EPUB+DRM
  • Sari: Transportation
  • Ilmumisaeg: 25-Apr-2019
  • Kirjastus: Institution of Engineering and Technology
  • Keel: eng
  • ISBN-13: 9781785613395
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This book covers the current state of research in navigation, modelling and control of marine autonomous vehicles, and deals with various related topics, including collision avoidance, communication, and a range of applications.



Robotic marine vessels can be used for a wide range of purposes, including defence, marine science, offshore energy and hydrographic surveys, and environmental surveys and protection. Such vessels need to meet a variety of criteria: they must be able to operate in salt water, and to communicate and be controlled over large distances, even when submerged or in inclement weather. Further challenges include 3D navigation of individual vehicles, groups or squadrons.

This book covers the current state of research in navigation, modelling and control of marine autonomous vehicles, and deals with various related topics, including collision avoidance, communication, and a range of applications. It provides valuable insights for an audience of researchers, academics and postgraduate students interested in autonomous marine vessels, robotics, and electrical and automobile engineering.

Preface xi
1 Modelling and control of autonomous marine vehicles 1(30)
Yuanchang Liu
Richard Bucknall
Abstract
1(1)
1.1 Introduction
1(6)
1.1.1 USV prototypes and core systems
2(3)
1.1.2 The control strategies of USV
5(2)
1.2 Mathematical modelling of autonomous marine vehicles
7(5)
1.2.1 Kinematic motion of marine vehicle
8(2)
1.2.2 Dynamic motion of marine vehicle
10(2)
1.3 Intelligent path planning and control of autonomous marine vehicles
12(16)
1.3.1 Collision risk assessment strategies
13(4)
1.3.2 Motion planning for USV
17(6)
1.3.3 Autonomous and intelligent navigation of a USV
23(5)
1.4 Conclusion
28(1)
References
28(3)
2 Efficient optimal path planning of unmanned surface vehicles 31(30)
Yogang Singh
Sanjay Sharma
Robert Sutton
Daniel Hatton
Asiya Khan
Abstract
31(1)
2.1 Introduction
31(7)
2.1.1 Review of heuristic approaches in path planning of USVs
35(2)
2.1.2 A* approach
37(1)
2.2 Methodology overview
38(5)
2.2.1 Environmental mapping
38(1)
2.2.2 Assumptions
39(1)
2.2.3 Challenges of incorporating COLREGs in path-planning algorithms
40(1)
2.2.4 Incorporating guidance and control system with path-planning algorithm
41(1)
2.2.5 Collision avoidance in close encounter situation
42(1)
2.3 Simulation results
43(11)
2.3.1 Comparing A* approach with and without safety distance
43(2)
2.3.2 Constrained A* approach under static and partially dynamic environment
45(4)
2.3.3 Constrained A* approach with environmental disturbances
49(3)
2.3.4 Constrained A* approach with single moving obstacle and environmental disturbance
52(2)
2.4 Conclusions
54(3)
References
57(4)
3 Collision avoidance of maritime vessels 61(24)
Wasif Naeem
Sable Campbell de Oliveira Henrique
Mamun Abu-Tair
Abstract
61(1)
3.1 Introduction
61(4)
3.1.1 Motivation and background
62(3)
3.2 COLREGs
65(3)
3.3 APFs
68(3)
3.4 Collision risk assessment
71(1)
3.5 COLREGs decision maker
72(3)
3.6 COLREGs zones for APF adaptation
75(1)
3.7 Simulation results
76(6)
3.7.1 Path dynamics
78(4)
3.8 Discussion and concluding remarks
82(1)
Acknowledgement
82(1)
References
82(3)
4 Sliding mode control for path planning guidance of marine vehicles 85(26)
Shashi Ranjan Kumar
Ashwini Ratnoo
Debasish Ghose
Abstract
85(1)
4.1 Introduction
85(2)
4.2 Problem statement
87(1)
4.3 Design of impact angle guidance
88(3)
4.4 Application of guidance scheme to underwater vehicles
91(2)
4.4.1 Sample and hold
91(1)
4.4.2 Linear interpolation
91(1)
4.4.3 Improved sample and hold
92(1)
4.5 Simulation results
93(14)
4.5.1 Implementation of guidance law with closed-loop feedback
94(2)
4.5.2 Implementation of guidance law in open loop
96(11)
4.6 Conclusions and future work
107(1)
References
107(4)
5 Experimentally based analysis of low altitude terrain following by autonomous underwater vehicles 111(24)
Sophia M. Schillai
Alexander B. Phillips
Eric Rogers
Stephen R. Turnock
Abstract
111(1)
5.1 Introduction
111(1)
5.2 Background
112(1)
5.3 Current terrain following strategies
113(1)
5.4 Terrain following with Delphin2
113(8)
5.4.1 Terrain detection
114(2)
5.4.2 Horizon tracking
116(4)
5.4.3 Altitude controller
120(1)
5.4.4 Actuation strategy
121(1)
5.5 Testwood lake experiment set-up
121(4)
5.5.1 Experiment parameter variation
123(1)
5.5.2 Performance analysis
123(2)
5.6 Results
125(6)
5.6.1 Repeatability and obstacle detection
125(3)
5.6.2 Actuation strategy
128(3)
5.7 Conclusion
131(1)
References
131(4)
6 Nonlinear Hinfinity control of autonomous underwater vehicles 135(28)
Subhasish Mahapatra
Bidyadhar Subudhi
Abstract
135(1)
6.1 Introduction
135(2)
6.2 Preliminaries
137(2)
6.3 Modeling of AUV
139(6)
6.3.1 AUV modeling: diving plane
141(1)
6.3.2 AUV modeling: steering plane
142(2)
6.3.3 Path kinematics: Serret-Frenet frame
144(1)
6.4 Development of nonlinear control algorithm
145(3)
6.4.1 Nonlinear state feedback Hinfinity controller
145(3)
6.5 Analysis of nonlinear Hinfinity controller
148(8)
6.5.1 Diving control
148(3)
6.5.2 Steering control
151(5)
6.6 Path following control
156(3)
6.6.1 Guidance law for path following
156(1)
6.6.2 Simulation results
157(2)
6.7 Concluding remarks
159(1)
References
160(3)
7 Energy optimal real-time trajectory re-planning of an uninhabited surface vehicle in a dynamically changing environment 163(24)
Haibin Huang
Yufei Zhuang
Sanjay Sharma
Abstract
163(1)
7.1 Introduction
163(2)
7.2 Mathematics representation
165(2)
7.2.1 Motion equations
165(1)
7.2.2 Ocean currents
166(1)
7.2.3 Wind load model
167(1)
7.3 Trajectory planning using pseudospectral method
167(3)
7.3.1 Problem statement
167(1)
7.3.2 Legendre pseudospectral method
168(1)
7.3.3 Discretization of the optimization problem
169(1)
7.4 Optimization using particle swarm optimization
170(1)
7.5 Re-planning strategy
171(4)
7.5.1 Problem statement of re-planning
171(3)
7.5.2 Problem reformulation in differentially flat outputs space
174(1)
7.6 Simulation results
175(9)
7.6.1 Simulation results without disturbance
176(4)
7.6.2 Simulation results with time vary disturbance
180(4)
7.7 Conclusion
184(1)
Acknowledgments
184(1)
References
185(2)
8 Cooperative path-following control with logic-based communications: theory and practice 187(38)
Francisco C. Rego
Nguyen T Hung
Colin N. Jones
Antonio M. Pascoal
Antonio Pedro Aguiar
Abstract
187(1)
8.1 Introduction
187(4)
8.1.1
Chapter structure
189(1)
8.1.2 Notation
189(2)
8.2 Cooperative path-following control system architecture
191(2)
8.3 Problem statement
193(7)
8.3.1 Path-following problem
193(2)
8.3.2 Coordination control problem
195(1)
8.3.3 Cooperative path-following
196(2)
8.3.4 Logic-based communication system
198(2)
8.4 Controller design: CPF for multiple AMVs
200(9)
8.4.1 Vehicle model
200(2)
8.4.2 Path-following controller
202(1)
8.4.3 Coordination controller
203(1)
8.4.4 Logic-based communication system
204(4)
8.4.5 Stability of the overall-closed loop system
208(1)
8.5 Field tests with AMVs
209(7)
8.5.1 Test set-up
209(2)
8.5.2 Results
211(5)
8.6 Conclusions
216(1)
Acknowledgements
216(1)
Appendix A
216(6)
References
222(3)
9 Formation control of autonomous marine vehicles 225(38)
Basant Kumar Sahu
Bidyadhar Subudhi
Sanjay Kumar Sharma
Abstract
225(1)
9.1 Introduction
225(2)
9.2 Classification of formation control techniques
227(3)
9.2.1 Selection of vehicles with targets
227(1)
9.2.2 Control abstraction
227(3)
9.3 Coordination strategies of autonomous vehicles
230(4)
9.3.1 Centralized approach
230(1)
9.3.2 Decentralized approach
231(1)
9.3.3 Distributed approach
232(2)
9.4 Formation control strategies
234(11)
9.4.1 Formation control using behavioral approach
234(1)
9.4.2 Formation control using leader-follower approach
235(4)
9.4.3 Formation control using virtual structure approach
239(1)
9.4.4 Formation control using artificial potentials approach
240(1)
9.4.5 Attractive potential functions
241(1)
9.4.6 Repulsive potential functions
241(1)
9.4.7 Formation control using graph-theory approach
242(1)
9.4.8 Other control strategies
243(2)
9.5 Communication issues in formation of multiple vehicles
245(2)
9.6 Formation control sub-problems
247(3)
9.6.1 Obstacle and collision avoidance
247(1)
9.6.2 Formation shape generation
247(2)
9.6.3 Switching between shapes according to situation
249(1)
9.6.4 Formation repair
250(1)
9.6.5 Movement of formation structure
250(1)
9.7 Conclusions
250(1)
References
250(13)
10 Hydro-acoustic communications and networking in contemporary underwater robotics: instruments and case studies 263(38)
Konstantin Kebkal
Oleksiy Kebkal
Veronika Kebkal
Ievgenii Glushko
Antonio Pascoal
Miguel Ribeiro
Manuel Rufino
Luis Sebastiao
Giovanni Indiveri
Lorenzo Pollini
Enrico Simetti
Abstract
263(1)
10.1 Introduction
264(3)
10.2 The S2C modem of Evologics as a platform for specialized user applications
267(1)
10.3 Architecture of the software framework EviNS
268(1)
10.4 Case studies
269(28)
10.4.1 Case study - operation of the UWA modems in an ad-hoc network
270(13)
10.4.2 Case study - operation of UWA modems in a bimodal network with a centralized topology
283(7)
10.4.3 Case study - UWA modems with integrated atomic clocks for positioning of a group of AUVs
290(7)
Acknowledgements
297(1)
References
297(4)
11 Commercial applications of ASVs 301(24)
Dan Hook
Abstract
301(1)
11.1 Introduction
301(1)
11.2 Defence applications
302(3)
11.2.1 Mine counter measures (MCM)
303(1)
11.2.2 Submarine warfare (ASW)
304(1)
11.2.3 Command, Control, Communications, Computers, Information/Intelligence, Surveillance, Targeting Acquisition and Reconnaissance (C4ISTAR)
304(1)
11.2.4 Targets and training systems
304(1)
11.2.5 Met-Ocean (METOC)
304(1)
11.2.6 Rapid environmental assessment (REA)
304(1)
11.3 Scientific applications overview
305(2)
11.3.1 Examples of scientific applications
306(1)
11.4 Technology overview
307(4)
11.5 ASV types
311(6)
11.5.1 Small systems
312(2)
11.5.2 Medium systems
314(1)
11.5.3 Large systems
315(2)
11.6 Industrial applications
317(5)
11.6.1 Proven
318(3)
11.6.2 In development
321(1)
11.6.3 Future
322(1)
11.7 Conclusion
322(3)
Index 325
Sanjay Sharma is an Associate Professor (Reader) in Intelligent Autonomous Control Systems, and Head of Autonomous Marine Systems (AMS) research group at Plymouth University, UK. His research focuses on the use of AI techniques; particularly for wave energy devices, robotics, automobiles and uninhabited marine vehicles for both surface and underwater operations. In addition, he serves as a member of the IMechE Mechatronics, Informatics & Control Group Board, is an IMechE representative to UKACC, and a member of IFAC TC 7.5 on Intelligent Autonomous Vehicles.



Bidyadhar Subudhi is a Professor in the Department of Electrical Engineering at the National Institute of Technology Rourkela, India. He is the Co-ordinator of Control and Automation Research Group and Centre of Excellence on Renewable Energy Systems. His research interests include Robust and Adaptive Control, Control of Autonomous Underwater and Flexible Robots, Renewable Power and Microgrid Control. He is a member of Technical Committee on Intelligent Control, IEEE Control System Society. He is a Fellow of IET(UK), Institution of Engineers (India) and a Senior Member of IEEE.
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