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E-grāmata: Multi-UAV Planning and Task Allocation [Taylor & Francis e-book]

(Universite d'Evry, France)
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Multi-UAV Planning and Task AllocationPalielināt
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Multi-robot systems are a major research topic in robotics. Designing, testing, and deploying aerial robots in the real world is a possibility due to recent technological advances. This book explores different aspects of cooperation in multi-agent systems. It covers the team approach, as well as deterministic decision making. It also presents distributed receding horizon control, as well as conflict resolution, artificial potentials, and symbolic planning. The book also covers association with limited communications, as well as genetic algorithms and game theory reasoning. Multi-agent decision making and algorithms for optimal planning are also covered, along with case studies.

Key features:

·         Provides a comprehensive introduction to multi-robot systems planning and task allocation

·         Explores multi robot aerial planning, flight planning, orienteering and coverage, and deployment, patrolling, and foraging

·         Includes real-world case studies

·         Treats different aspects of cooperation in multi-agent systems

Both scientists and practitioners in the field of robotics will find this text valuable. Yasmina Bestaoui-Sebbane earned her PhD in Control and Computer Engineering from Ecole Nationale Superieure de Mecanique, Nantes, France, in 1989 (currently Ecole Centrale de Nantes), and the Habilitation to Direct Research in Robotics, from the University of Evry, France, in 2000. Her research interests included control, planning, and decision making of unmanned systems, particularly unmanned aerial vehicles and robots.

Author ix
Chapter 1 Multi-Aerial-Robot Planning
1(56)
1.1 Introduction
1(1)
1.2 Team Approach
2(13)
1.2.1 Cooperation
3(2)
1.2.2 Cascade-Type Guidance Law
5(2)
1.2.3 Consensus Approach
7(1)
1.2.3.1 Consensus Opinion
7(2)
1.2.3.2 Reachability and Observability
9(1)
1.2.4 Flocking Behavior
9(1)
1.2.4.1 Collective Potential of Flocks
10(1)
1.2.4.2 Distributed Flocking Algorithms
11(1)
1.2.5 Connectivity and Convergence of Formations
12(1)
1.2.5.1 Problem Formulation
12(1)
1.2.5.2 Stability of Formations in Time-Invariant Communication
13(2)
1.3 Deterministic Decision-Making
15(9)
1.3.1 Distributed Receding Horizon Control
16(1)
1.3.2 Conflict Resolution
17(1)
1.3.2.1 Distributed Reactive Collision Avoidance
18(1)
1.3.2.2 Deconfliction Maintenance
19(1)
1.3.3 Artificial Potential
19(1)
1.3.3.1 Velocity Field
19(1)
1.3.3.2 Artificial Potential Field
20(1)
1.3.3.3 Pattern Formation and Reconfigurability
21(1)
1.3.4 Symbolic Planning
22(2)
1.4 Association with Limited Communication
24(5)
1.4.1 Introduction
24(1)
1.4.2 Problem Formulation
24(2)
1.4.2.1 Decentralized Resolution of Inconsistent Association
26(1)
1.4.3 Genetic Algorithms
27(1)
1.4.4 Games Theory Reasoning
28(1)
1.4.4.1 Cooperative Protocol
29(1)
1.4.4.2 Non-Cooperative Protocol
29(1)
1.4.4.3 Leader/Follower Protocol
29(1)
1.5 Multiagent Decision-Making under Uncertainty
29(16)
1.5.1 Decentralized Team Decision Problem
30(1)
1.5.1.1 Bayesian Strategy
30(1)
1.5.1.2 Semi-Modeler Strategy
30(2)
1.5.1.3 Communication Models
32(4)
1.5.2 Algorithms for Optimal Planning
36(1)
1.5.2.1 Multiagent A* (MAA*): A Heuristic Search Algorithm for DEC-POMDP
36(1)
1.5.2.2 Policy Iteration for Infinite Horizon
37(1)
1.5.2.3 Linear-Quadratic Approach
37(1)
1.5.2.4 Decentralized Chance-Constrained Finite Horizon Optimal Control
38(1)
1.5.3 Task Allocation: Optimal Assignment
38(1)
1.5.3.1 Hungarian Algorithm
39(1)
1.5.3.2 Interval Hungarian Algorithm
40(2)
1.5.3.3 Quantifying the Effect of Uncertainty
42(1)
1.5.3.4 Uncertainty Measurement for a Single Utility
42(1)
1.5.4 Distributed Chance-Constrained Task Allocation
43(1)
1.5.4.1 Chance-Constrained Task Allocation
44(1)
1.5.4.2 Distributed Approximation to the Chance-Constrained Task Allocation Problem
45(1)
1.6 Case Studies
45(11)
1.6.1 Reconnaissance Mission
45(1)
1.6.1.1 General Vehicle Routing Problem
45(1)
1.6.1.2 Chinese Postman Problem
46(1)
1.6.1.3 Cluster Algorithm
47(1)
1.6.1.4 The Rural CPP
47(1)
1.6.2 Expanding Grid Coverage
48(1)
1.6.3 Optimization of Perimeter Patrol Operations
49(2)
1.6.3.1 Multiagent Markov Decision Process
51(1)
1.6.3.2 Anytime Error Minimization Search
51(2)
1.6.4 Stochastic Strategies for Surveillance
53(1)
1.6.4.1 Analysis Methods
53(1)
1.6.4.2 Problems in 1D
54(1)
1.6.4.3 Complete Graphs
55(1)
1.7 Conclusions
56(1)
Chapter 2 Flight Planning
57(74)
2.1 Introduction
57(2)
2.2 Path and Trajectory Planning
59(15)
2.2.1 Trim Trajectories
60(1)
2.2.2 Trajectory Planning
61(1)
2.2.2.1 Time Optimal Trajectories
61(1)
2.2.2.2 Nonholonomic Motion Planning
62(2)
2.2.3 Path Planning
64(1)
2.2.3.1 B-Spline Formulation
65(1)
2.2.3.2 Cubic Hermite Spline
65(1)
2.2.3.3 Quintic Hermite Spline
66(1)
2.2.3.4 Pythagorean Hodographs
66(1)
2.2.4 The Zermelo Problem: Aircraft in the Wind
67(1)
2.2.4.1 Initial Zermelo's Problem
67(3)
2.2.4.2 2D Zermelo's Problem on a Flat Earth
70(1)
2.2.4.3 3D Zermelo's Problem on a Flat Earth
71(1)
2.2.4.4 3D Zermelo's Problem on a Spherical Surface
72(1)
2.2.4.5 Virtual Goal
73(1)
2.3 Guidance and Collision/Obstacle Avoidance
74(25)
2.3.1 Guidance
75(1)
2.3.1.1 Proportional Navigation
76(1)
2.3.1.2 Method of Adjoints
76(1)
2.3.1.3 Fuzzy Guidance Scheme
77(3)
2.3.2 Static Obstacles Avoidance
80(1)
2.3.2.1 Discrete Methods
81(7)
2.3.2.2 Continuous Methods
88(2)
2.3.3 Moving Obstacles Avoidance
90(1)
2.3.3.1 D* Algorithm
91(2)
2.3.3.2 Artificial Potential Fields
93(1)
2.3.3.3 Online Motion Planner
94(1)
2.3.3.4 Zermelo-Voronoi Diagram
95(2)
2.3.4 Time Optimal Navigation Problem with Moving and Fixed Obstacles
97(1)
2.3.4.1 Problem Formulation
98(1)
2.3.4.2 Control Parametrization and Time Scaling Transform
98(1)
2.3.4.3 RRT Variation
99(1)
2.4 Mission Planning
99(31)
2.4.1 Traveling Salesman Problem
101(3)
2.4.2 Replanning or Tactical and Strategical Planning
104(2)
2.4.3 Route Optimization
106(1)
2.4.3.1 Classical Approach
106(2)
2.4.3.2 Dynamic Multi-Resolution Route Optimization
108(3)
2.4.4 Fuzzy Planning
111(1)
2.4.4.1 Fuzzy Decision Tree Cloning of Flight Trajectories
111(3)
2.4.4.2 Fuzzy Logic for Fire-Fighting Aircraft
114(1)
2.4.5 Coverage Problem
115(1)
2.4.5.1 Patrolling Problem
115(2)
2.4.5.2 Routing Problem
117(2)
2.4.5.3 Discrete Stochastic Process for Aircraft Networks
119(2)
2.4.5.4 Sensor Tasking in Multi-Target Search and Tracking Applications
121(4)
2.4.6 Resource Manager for a Team of Autonomous Aircraft
125(1)
2.4.6.1 Routing with Refueling Depots for a Single Aircraft
126(2)
2.4.6.2 Routing with Refueling Depots for Multiple Aircraft
128(2)
2.5 Conclusion
130(1)
Chapter 3 Orienteering and Coverage
131(50)
3.1 Introduction
131(1)
3.2 Operational Research Preliminaries
131(13)
3.2.1 General Vehicle Routing Problem
131(1)
3.2.2 Traveling Salesperson Problem
132(1)
3.2.2.1 Deterministic Traveling Salesperson
133(2)
3.2.2.2 Stochastic Traveling Salesperson
135(2)
3.2.3 Postperson Problem
137(1)
3.2.3.1 Chinese Postperson Problem
137(3)
3.2.3.2 Rural Postperson Problem
140(3)
3.2.4 Knapsack Problem
143(1)
3.3 Orienteering
144(7)
3.3.1 Orienteering Problem Formulation
144(1)
3.3.1.1 Nominal Orienteering Problem
144(2)
3.3.1.2 Robust Orienteering Problem
146(1)
3.3.1.3 UAV Team Orienteering Problem
147(2)
3.3.2 UAV Sensor Selection
149(2)
3.4 Coverage
151(29)
3.4.1 Barrier Coverage
153(1)
3.4.1.1 Barrier Coverage Approach
153(2)
3.4.1.2 Sensor Deployment and Coverage
155(1)
3.4.2 Perimeter Coverage
155(1)
3.4.2.1 Coverage of a Circle
155(2)
3.4.2.2 Dynamic Boundary Coverage
157(1)
3.4.3 Area Coverage
158(1)
3.4.3.1 Preliminaries
158(4)
3.4.3.2 Boustrophedon Cellular Decomposition
162(1)
3.4.3.3 Spiral Path
163(3)
3.4.3.4 Distributed Coverage
166(14)
3.5 Conclusion
180(1)
Chapter 4 Deployment, Patrolling and Foraging
181(50)
4.1 Introduction
181(1)
4.2 Aerial Deployment
181(20)
4.2.1 Deployment Problem
182(1)
4.2.1.1 Deployment Methodology
182(4)
4.2.1.2 Deployment Strategies
186(6)
4.2.2 Mobile Sensor Network
192(1)
4.2.2.1 Aerial Networks
192(3)
4.2.2.2 Visual Coverage
195(5)
4.2.2.3 Wireless Sensor Network
200(1)
4.3 Patrolling
201(14)
4.3.1 Perimeter Patrol
202(4)
4.3.2 Area Cooperative Patrolling
206(1)
4.3.2.1 Multiple Depot Multi-TSP
206(2)
4.3.2.2 Exploration
208(1)
4.3.2.3 Coordination in a Unknown Environment
209(6)
4.4 Foraging
215(14)
4.4.1 Problem Formulation
215(1)
4.4.1.1 Abstract Model
215(1)
4.4.1.2 Continuous Foraging
216(2)
4.4.1.3 Foraging Algorithms
218(3)
4.4.1.4 Anchoring
221(1)
4.4.2 Aerial Manipulation
222(1)
4.4.2.1 Aerial Transportation
222(3)
4.4.2.2 Coupled Dynamics
225(4)
4.5 Conclusion
229(2)
Bibliography 231(20)
Index 251
Yasmina Bestaoui Sebbane (19602018) was a full professor in Automatic and Robotics at the



University of Evry, Val dEssonne (France). She was also the head of the pole Drones of IBISC



(Informatique, Biologie Int“egrative et Syst`emes Complexes), PEDR laboratory in the University of



Evry, and also served as the department head (20062015). She was an Academic Palms recipient



(2017) and led and contributed to many scientific committees and collaborations with other universities



to create new teaching courses. She earned her PhD in Control and Computer Engineering



from “ Ecole Nationale Sup“erieure de M“ecanique, Nantes, France, in 1989 (currently “ Ecole Centrale



de Nantes), and her Research Advisor Qualification (HDR: Habilitation to Direct Research)



in Robotics from the University of Evry, France, in 2000. Her research interests included control,



planning, and decision-making of unmanned systems, particularly unmanned aerial vehicles and



robots.
Permanent link: https://www.kriso.lv/db/9781003026686_pe.html
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