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E-raamat: Reliable Robot Localization - A Constraint- Programming Approach Over Dynamical Systems: A Constraint-Programming Approach Over Dynamical Systems [Wiley Online]

(ENSTA Bretagne-Lab-STICC, France), (University of Sheffield, UK), (ENSTA Bretagne-Lab-STICC, France), (University of Sheffield, UK), (ENSTA Bretagne-Lab-STICC, France)
  • Formaat: 288 pages
  • Ilmumisaeg: 25-Oct-2019
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119680972
  • ISBN-13: 9781119680970
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  • Wiley Online
  • Hind: 174,45 €*
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Reliable Robot Localization - A Constraint- Programming Approach Over Dynamical Systems: A Constraint-Programming Approach Over Dynamical SystemsSuurem pilt
  • Formaat: 288 pages
  • Ilmumisaeg: 25-Oct-2019
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • ISBN-10: 1119680972
  • ISBN-13: 9781119680970
Teised raamatud teemal:
Wiley Online e-raamatudRohkem infot Wiley Online kohta
Raamatu kodulehekülg: https://onlinelibrary.wiley.com/doi/book/10.1002/9781119680970
Localization for underwater robots remains a challenging issue. Typical sensors, such as Global Navigation Satellite System (GNSS) receivers, cannot be used under the surface and other inertial systems suffer from a strong integration drift. On top of that, the seabed is generally uniform and unstructured, making it difficult to apply Simultaneous Localization and Mapping (SLAM) methods to perform localization. Reliable Robot Localization presents an innovative new method which can be characterized as a raw-data SLAM approach. It differs from extant methods by considering time as a standard variable to be estimated, thus raising new opportunities for state estimation, so far underexploited. However, such temporal resolution is not straightforward and requires a set of theoretical tools in order to achieve the main purpose of localization. This book not only presents original contributions to the field of mobile robotics, it also offers new perspectives on constraint programming and set-membership approaches. It provides a reliable contractor programming framework in order to build solvers for dynamical systems. This set of tools is illustrated throughout this book with realistic robotic applications.
Preface xi
Notations xiii
Abbreviations xvii
Introduction xix
Part 1 Interval Tools
1(64)
Introduction to Part 1
3(2)
Chapter 1 Static Set-membership State Estimation
5(36)
1.1 Introduction
5(3)
1.2 Interval analysis
8(11)
1.2.1 Once upon a time
8(2)
1.2.2 Intervals
10(4)
1.2.3 Inclusion functions
14(2)
1.2.4 Pessimism and wrapping effect
16(3)
1.3 Constraint propagation
19(6)
1.3.1 Constraint networks
19(2)
1.3.2 Contractors
21(3)
1.3.3 Application to static range-only robot localization
24(1)
1.4 Set-inversion via interval analysis
25(10)
1.4.1 Subpaving
25(3)
1.4.2 SIVIA algorithm for set-inversion
28(1)
1.4.3 Illustration involving contractions
29(4)
1.4.4 Kernel characterization of an interval function
33(2)
1.5 Discussions
35(3)
1.5.1 From sensors to reliable results
36(1)
1.5.2 Numerical libraries
37(1)
1.5.3 Reliable tool for proof purposes
38(1)
1.6 Conclusion
38(3)
Chapter 2 Constraints Over Sets of Trajectories
41(24)
2.1 Towards dynamic state estimation
41(3)
2.1.1 Overall motivations
41(2)
2.1.2 The approach presented in this book
43(1)
2.2 Tubes
44(6)
2.2.1 Definitions
44(1)
2.2.2 Tube analysis
45(3)
2.2.3 Contractors
48(2)
2.3 Implementation
50(7)
2.3.1 Data structure
52(2)
2.3.2 Build a tube from real datasets
54(3)
2.3.3 Tubex, dedicated tube library
57(1)
2.4 Application: dead-reckoning of a mobile robot
57(3)
2.4.1 Test case
58(1)
2.4.2 Constraint network
58(1)
2.4.3 Resolution
59(1)
2.5 Discussions
60(3)
2.5.1 Limits
60(1)
2.5.2 Extract the most probable trajectory from a tube
61(1)
2.5.3 Application to path planning
62(1)
2.6 Conclusion
63(2)
Part 2 Constraints-related Contributions
65(64)
Introduction to Part 2
67(2)
Chapter 3 Trajectories under Differential Constraints
69(32)
3.1 Introduction
69(4)
3.1.1 The differential problem
69(1)
3.1.2 Attempts with set-membership methods
70(2)
3.1.3 Contribution of this work
72(1)
3.2 Differential contractor for L d/t: x(·) = v(·)
73(9)
3.2.1 Definition and proof
74(5)
3.2.2 Contraction of the derivative
79(1)
3.2.3 Implementation
80(2)
3.3 Contractor-based approach for state estimation
82(8)
3.3.1 Constraint network of state equations
84(1)
3.3.2 Fixed-point propagations
85(2)
3.3.3 Theoretical example of interest x = - sin(x)
87(3)
3.4 Robotic applications
90(9)
3.4.1 Causal kinematic chain
90(3)
3.4.2 Higher-order differential constraints
93(1)
3.4.3 Kidnapped robot problem
93(1)
3.4.4 Actual experiment with the Daurade AUV
94(5)
3.5 Conclusion
99(2)
Chapter 4 Trajectories Under Evaluation Constraints
101(28)
4.1 Introduction
101(4)
4.1.1 Contribution of this work
101(1)
4.1.2 Motivations to deal with time uncertainties
102(3)
4.2 Generic contractor for trajectory evaluation
105(9)
4.2.1 Tube contractor for the constraint Leval: z = y(t)
105(8)
4.2.2 Implementation
111
4.2.3 Application to state estimation
113(1)
4.3 Robotic applications
114(13)
4.3.1 Range-only robot localization with low-cost beacons
114(7)
4.3.2 Reliable correction of a drifting clock
121(6)
4.4 Conclusion
127(2)
Part 3 Robotics-related Contributions
129(82)
Introduction to Part 3
131(2)
Chapter 5 Looped Trajectories: From Detections to Proofs
133(32)
5.1 Introduction
133(2)
5.1.1 The difference between detection and verification
133(1)
5.1.2 Proprioceptive versus exteroceptive measurements
134(1)
5.1.3 The two-dimensional case
135(1)
5.2 Proprioceptive loop detections
135(6)
5.2.1 Formalization
136(1)
5.2.2 Loop detections in a bounded-error context
137(1)
5.2.3 Approximation of the solution set T
138(3)
5.3 Proving loops in detection sets
141(10)
5.3.1 Formalism: zero verification
141(1)
5.3.2 Topological degree for zero verification
141(4)
5.3.3 Loop existence test
145(4)
5.3.4 Reliable number of loops
149(2)
5.4 Applications
151(12)
5.4.1 The Redermor mission
152(4)
5.4.2 The Daurade mission
156(3)
5.4.3 Optimality of the approach
159(4)
5.5 Conclusion
163(2)
Chapter 6 A Reliable Temporal Approach for the SLAM Problem
165(46)
6.1 Introduction
165(7)
6.1.1 Motivations
165(2)
6.1.2 SLAM formalism
167(2)
6.1.3 Inter-temporalities
169(3)
6.2 Temporal SLAM method
172(18)
6.2.1 General assumptions
172(1)
6.2.2 Temporal resolution
173(1)
6.2.3 Lp⇒z: inter-temporal implication constraint
174(4)
6.2.4 The Cp⇒z contractor
178(8)
6.2.5 Temporal SLAM algorithm
186(4)
6.3 Underwater application: bathymetric SLAM
190(13)
6.3.1 Context
190(4)
6.3.2 Daurade's underwater mission, October 20, 2015
194(5)
6.3.3 Daurade's underwater mission, October 19, 2015
199(3)
6.3.4 Overview of the environment
202(1)
6.4 Discussions
203(4)
6.4.1 Relation to the state of the art
203(2)
6.4.2 About a Bayesian resolution
205(1)
6.4.3 Biased sensors
205(1)
6.4.4 Fluctuating measurements
205(2)
6.5 Conclusion
207(4)
Conclusion 211(6)
References 217(12)
Index 229
Simon Rohou is an Associate Professor at ENSTA Bretagne -Lab-STICC (Brest, France).

Luc Jaulin is Full Professor of Robotics at ENSTA Bretagne-Lab-STICC.

Lyudmila Mihaylova is Professor of Signal Processing and Control with the Department of Automatic Control and Systems Engineering at the University of Sheffield (UK).

Fabrice Le Bars is an Associate Professor at ENSTA Bretagne-Lab-STICC.

Sandor M. Veres holds a chair in Autonomous Control Systems, and leads the Robotics and Autonomous Systems Research Group at the Department of Automatic Control and Systems Engineering at the University of Sheffield.
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