Multi-UAV planning and task allocation:
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 multiagent systems. It covers the team approach as well as deter...
Saved in:
| Main Author: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton
CRC Press
2020
|
| Series: | Chapman & Hall/CRC artificial intelligence and robotics series
|
| Subjects: | |
| Summary: | 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 multiagent 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. Multiagent 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 multiagent systems Both scientists and practitioners in the field of robotics will find this text valuable |
| Physical Description: | ix, 263 Seiten Illustrationen, Diagramme |
| ISBN: | 9781003026686 9780367457822 1003026680 |
Staff View
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV046920427 | ||
| 003 | DE-604 | ||
| 005 | 20201014 | ||
| 007 | t | ||
| 008 | 200930s2020 a||| b||| 00||| eng d | ||
| 020 | |a 9781003026686 |9 978-1-003-02668-6 | ||
| 020 | |a 9780367457822 |9 978-0-367-45782-2 | ||
| 020 | |a 1003026680 |9 1-003-02668-0 | ||
| 035 | |a (OCoLC)1220878899 | ||
| 035 | |a (DE-599)BVBBV046920427 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-573 | ||
| 084 | |a ZQ 6250 |0 (DE-625)158184: |2 rvk | ||
| 100 | 1 | |a Bestaoui Sebbane, Yasmina |d 1960- |e Verfasser |0 (DE-588)1166462471 |4 aut | |
| 245 | 1 | 0 | |a Multi-UAV planning and task allocation |c Yasmina Bestaoui-Sebbane |
| 264 | 1 | |a Boca Raton |b CRC Press |c 2020 | |
| 300 | |a ix, 263 Seiten |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Chapman & Hall/CRC artificial intelligence and robotics series | |
| 505 | 8 | |a Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Author -- Chapter 1 Multi-Aerial-Robot Planning -- 1.1 Introduction -- 1.2 Team Approach -- 1.2.1 Cooperation -- 1.2.2 Cascade-Type Guidance Law -- 1.2.3 Consensus Approach -- 1.2.3.1 Consensus Opinion -- 1.2.3.2 Reachability and Observability -- 1.2.4 Flocking Behavior -- 1.2.4.1 Collective Potential of Flocks -- 1.2.4.2 Distributed Flocking Algorithms -- 1.2.5 Connectivity and Convergence of Formations -- 1.2.5.1 Problem Formulation -- 1.2.5.2 Stability of Formations in Time-Invariant Communication | |
| 505 | 8 | |a 1.3 Deterministic Decision-Making -- 1.3.1 Distributed Receding Horizon Control -- 1.3.2 Conflict Resolution -- 1.3.2.1 Distributed Reactive Collision Avoidance -- 1.3.2.2 Deconfliction Maintenance -- 1.3.3 Artificial Potential -- 1.3.3.1 Velocity Field -- 1.3.3.2 Artificial Potential Field -- 1.3.3.3 Pattern Formation and Reconfigurability -- 1.3.4 Symbolic Planning -- 1.4 Association with Limited Communication -- 1.4.1 Introduction -- 1.4.2 Problem Formulation -- 1.4.2.1 Decentralized Resolution of Inconsistent Association -- 1.4.3 Genetic Algorithms -- 1.4.4 Games Theory Reasoning | |
| 505 | 8 | |a 1.4.4.1 Cooperative Protocol -- 1.4.4.2 Non-Cooperative Protocol -- 1.4.4.3 Leader/Follower Protocol -- 1.5 Multiagent Decision-Making under Uncertainty -- 1.5.1 Decentralized Team Decision Problem -- 1.5.1.1 Bayesian Strategy -- 1.5.1.2 Semi-Modeler Strategy -- 1.5.1.3 Communication Models -- 1.5.2 Algorithms for Optimal Planning -- 1.5.2.1 Multiagent A* (MAA*): A Heuristic Search Algorithm for DEC-POMDP -- 1.5.2.2 Policy Iteration for Infinite Horizon -- 1.5.2.3 Linear-Quadratic Approach -- 1.5.2.4 Decentralized Chance-Constrained Finite Horizon Optimal Control | |
| 505 | 8 | |a 1.5.3 Task Allocation: Optimal Assignment -- 1.5.3.1 Hungarian Algorithm -- 1.5.3.2 Interval Hungarian Algorithm -- 1.5.3.3 Quantifying the Effect of Uncertainty -- 1.5.3.4 Uncertainty Measurement for a Single Utility -- 1.5.4 Distributed Chance-Constrained Task Allocation -- 1.5.4.1 Chance-Constrained Task Allocation -- 1.5.4.2 Distributed Approximation to the Chance-Constrained Task Allocation Problem -- 1.6 Case Studies -- 1.6.1 Reconnaissance Mission -- 1.6.1.1 General Vehicle Routing Problem -- 1.6.1.2 Chinese Postman Problem -- 1.6.1.3 Cluster Algorithm -- 1.6.1.4 The Rural CPP | |
| 505 | 8 | |a 1.6.2 Expanding Grid Coverage -- 1.6.3 Optimization of Perimeter Patrol Operations -- 1.6.3.1 Multiagent Markov Decision Process -- 1.6.3.2 Anytime Error Minimization Search -- 1.6.4 Stochastic Strategies for Surveillance -- 1.6.4.1 Analysis Methods -- 1.6.4.2 Problems in 1D -- 1.6.4.3 Complete Graphs -- 1.7 Conclusions -- Chapter 2 Flight Planning -- 2.1 Introduction -- 2.2 Path and Trajectory Planning -- 2.2.1 Trim Trajectories -- 2.2.2 Trajectory Planning -- 2.2.2.1 Time Optimal Trajectories -- 2.2.2.2 Nonholonomic Motion Planning -- 2.2.3 Path Planning -- 2.2.3.1 B-Spline Formulation | |
| 520 | 3 | |a 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 multiagent 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. Multiagent 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 multiagent systems Both scientists and practitioners in the field of robotics will find this text valuable | |
| 650 | 0 | 7 | |a Mehragentensystem |0 (DE-588)4389058-1 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Mehrrobotersystem |0 (DE-588)4734038-1 |2 gnd |9 rswk-swf |
| 653 | 0 | |a Drone aircraft / Automatic control | |
| 653 | 0 | |a Multiagent systems | |
| 653 | 0 | |a Adaptive control systems | |
| 653 | 0 | |a COMPUTERS / Computer Graphics / Game Programming & Design | |
| 653 | 0 | |a COMPUTERS / Machine Theory | |
| 653 | 0 | |a COMPUTERS / Neural Networks | |
| 653 | 0 | |a Adaptive control systems | |
| 653 | 0 | |a Multiagent systems | |
| 653 | 6 | |a Electronic books | |
| 689 | 0 | 0 | |a Mehrrobotersystem |0 (DE-588)4734038-1 |D s |
| 689 | 0 | 1 | |a Mehragentensystem |0 (DE-588)4389058-1 |D s |
| 689 | 0 | |5 DE-604 | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032329651 | ||
Record in the Search Index
| _version_ | 1804181806997569536 |
|---|---|
| adam_txt | |
| any_adam_object | |
| any_adam_object_boolean | |
| author | Bestaoui Sebbane, Yasmina 1960- |
| author_GND | (DE-588)1166462471 |
| author_facet | Bestaoui Sebbane, Yasmina 1960- |
| author_role | aut |
| author_sort | Bestaoui Sebbane, Yasmina 1960- |
| author_variant | s y b sy syb |
| building | Verbundindex |
| bvnumber | BV046920427 |
| classification_rvk | ZQ 6250 |
| contents | Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Author -- Chapter 1 Multi-Aerial-Robot Planning -- 1.1 Introduction -- 1.2 Team Approach -- 1.2.1 Cooperation -- 1.2.2 Cascade-Type Guidance Law -- 1.2.3 Consensus Approach -- 1.2.3.1 Consensus Opinion -- 1.2.3.2 Reachability and Observability -- 1.2.4 Flocking Behavior -- 1.2.4.1 Collective Potential of Flocks -- 1.2.4.2 Distributed Flocking Algorithms -- 1.2.5 Connectivity and Convergence of Formations -- 1.2.5.1 Problem Formulation -- 1.2.5.2 Stability of Formations in Time-Invariant Communication 1.3 Deterministic Decision-Making -- 1.3.1 Distributed Receding Horizon Control -- 1.3.2 Conflict Resolution -- 1.3.2.1 Distributed Reactive Collision Avoidance -- 1.3.2.2 Deconfliction Maintenance -- 1.3.3 Artificial Potential -- 1.3.3.1 Velocity Field -- 1.3.3.2 Artificial Potential Field -- 1.3.3.3 Pattern Formation and Reconfigurability -- 1.3.4 Symbolic Planning -- 1.4 Association with Limited Communication -- 1.4.1 Introduction -- 1.4.2 Problem Formulation -- 1.4.2.1 Decentralized Resolution of Inconsistent Association -- 1.4.3 Genetic Algorithms -- 1.4.4 Games Theory Reasoning 1.4.4.1 Cooperative Protocol -- 1.4.4.2 Non-Cooperative Protocol -- 1.4.4.3 Leader/Follower Protocol -- 1.5 Multiagent Decision-Making under Uncertainty -- 1.5.1 Decentralized Team Decision Problem -- 1.5.1.1 Bayesian Strategy -- 1.5.1.2 Semi-Modeler Strategy -- 1.5.1.3 Communication Models -- 1.5.2 Algorithms for Optimal Planning -- 1.5.2.1 Multiagent A* (MAA*): A Heuristic Search Algorithm for DEC-POMDP -- 1.5.2.2 Policy Iteration for Infinite Horizon -- 1.5.2.3 Linear-Quadratic Approach -- 1.5.2.4 Decentralized Chance-Constrained Finite Horizon Optimal Control 1.5.3 Task Allocation: Optimal Assignment -- 1.5.3.1 Hungarian Algorithm -- 1.5.3.2 Interval Hungarian Algorithm -- 1.5.3.3 Quantifying the Effect of Uncertainty -- 1.5.3.4 Uncertainty Measurement for a Single Utility -- 1.5.4 Distributed Chance-Constrained Task Allocation -- 1.5.4.1 Chance-Constrained Task Allocation -- 1.5.4.2 Distributed Approximation to the Chance-Constrained Task Allocation Problem -- 1.6 Case Studies -- 1.6.1 Reconnaissance Mission -- 1.6.1.1 General Vehicle Routing Problem -- 1.6.1.2 Chinese Postman Problem -- 1.6.1.3 Cluster Algorithm -- 1.6.1.4 The Rural CPP 1.6.2 Expanding Grid Coverage -- 1.6.3 Optimization of Perimeter Patrol Operations -- 1.6.3.1 Multiagent Markov Decision Process -- 1.6.3.2 Anytime Error Minimization Search -- 1.6.4 Stochastic Strategies for Surveillance -- 1.6.4.1 Analysis Methods -- 1.6.4.2 Problems in 1D -- 1.6.4.3 Complete Graphs -- 1.7 Conclusions -- Chapter 2 Flight Planning -- 2.1 Introduction -- 2.2 Path and Trajectory Planning -- 2.2.1 Trim Trajectories -- 2.2.2 Trajectory Planning -- 2.2.2.1 Time Optimal Trajectories -- 2.2.2.2 Nonholonomic Motion Planning -- 2.2.3 Path Planning -- 2.2.3.1 B-Spline Formulation |
| ctrlnum | (OCoLC)1220878899 (DE-599)BVBBV046920427 |
| discipline | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| discipline_str_mv | Mess-/Steuerungs-/Regelungs-/Automatisierungstechnik / Mechatronik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>05843nam a2200553 c 4500</leader><controlfield tag="001">BV046920427</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20201014 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">200930s2020 a||| b||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781003026686</subfield><subfield code="9">978-1-003-02668-6</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780367457822</subfield><subfield code="9">978-0-367-45782-2</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1003026680</subfield><subfield code="9">1-003-02668-0</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1220878899</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV046920427</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-573</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ZQ 6250</subfield><subfield code="0">(DE-625)158184:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Bestaoui Sebbane, Yasmina</subfield><subfield code="d">1960-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1166462471</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Multi-UAV planning and task allocation</subfield><subfield code="c">Yasmina Bestaoui-Sebbane</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton</subfield><subfield code="b">CRC Press</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">ix, 263 Seiten</subfield><subfield code="b">Illustrationen, Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="490" ind1="0" ind2=" "><subfield code="a">Chapman & Hall/CRC artificial intelligence and robotics series</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Author -- Chapter 1 Multi-Aerial-Robot Planning -- 1.1 Introduction -- 1.2 Team Approach -- 1.2.1 Cooperation -- 1.2.2 Cascade-Type Guidance Law -- 1.2.3 Consensus Approach -- 1.2.3.1 Consensus Opinion -- 1.2.3.2 Reachability and Observability -- 1.2.4 Flocking Behavior -- 1.2.4.1 Collective Potential of Flocks -- 1.2.4.2 Distributed Flocking Algorithms -- 1.2.5 Connectivity and Convergence of Formations -- 1.2.5.1 Problem Formulation -- 1.2.5.2 Stability of Formations in Time-Invariant Communication</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.3 Deterministic Decision-Making -- 1.3.1 Distributed Receding Horizon Control -- 1.3.2 Conflict Resolution -- 1.3.2.1 Distributed Reactive Collision Avoidance -- 1.3.2.2 Deconfliction Maintenance -- 1.3.3 Artificial Potential -- 1.3.3.1 Velocity Field -- 1.3.3.2 Artificial Potential Field -- 1.3.3.3 Pattern Formation and Reconfigurability -- 1.3.4 Symbolic Planning -- 1.4 Association with Limited Communication -- 1.4.1 Introduction -- 1.4.2 Problem Formulation -- 1.4.2.1 Decentralized Resolution of Inconsistent Association -- 1.4.3 Genetic Algorithms -- 1.4.4 Games Theory Reasoning</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.4.4.1 Cooperative Protocol -- 1.4.4.2 Non-Cooperative Protocol -- 1.4.4.3 Leader/Follower Protocol -- 1.5 Multiagent Decision-Making under Uncertainty -- 1.5.1 Decentralized Team Decision Problem -- 1.5.1.1 Bayesian Strategy -- 1.5.1.2 Semi-Modeler Strategy -- 1.5.1.3 Communication Models -- 1.5.2 Algorithms for Optimal Planning -- 1.5.2.1 Multiagent A* (MAA*): A Heuristic Search Algorithm for DEC-POMDP -- 1.5.2.2 Policy Iteration for Infinite Horizon -- 1.5.2.3 Linear-Quadratic Approach -- 1.5.2.4 Decentralized Chance-Constrained Finite Horizon Optimal Control</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.5.3 Task Allocation: Optimal Assignment -- 1.5.3.1 Hungarian Algorithm -- 1.5.3.2 Interval Hungarian Algorithm -- 1.5.3.3 Quantifying the Effect of Uncertainty -- 1.5.3.4 Uncertainty Measurement for a Single Utility -- 1.5.4 Distributed Chance-Constrained Task Allocation -- 1.5.4.1 Chance-Constrained Task Allocation -- 1.5.4.2 Distributed Approximation to the Chance-Constrained Task Allocation Problem -- 1.6 Case Studies -- 1.6.1 Reconnaissance Mission -- 1.6.1.1 General Vehicle Routing Problem -- 1.6.1.2 Chinese Postman Problem -- 1.6.1.3 Cluster Algorithm -- 1.6.1.4 The Rural CPP</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">1.6.2 Expanding Grid Coverage -- 1.6.3 Optimization of Perimeter Patrol Operations -- 1.6.3.1 Multiagent Markov Decision Process -- 1.6.3.2 Anytime Error Minimization Search -- 1.6.4 Stochastic Strategies for Surveillance -- 1.6.4.1 Analysis Methods -- 1.6.4.2 Problems in 1D -- 1.6.4.3 Complete Graphs -- 1.7 Conclusions -- Chapter 2 Flight Planning -- 2.1 Introduction -- 2.2 Path and Trajectory Planning -- 2.2.1 Trim Trajectories -- 2.2.2 Trajectory Planning -- 2.2.2.1 Time Optimal Trajectories -- 2.2.2.2 Nonholonomic Motion Planning -- 2.2.3 Path Planning -- 2.2.3.1 B-Spline Formulation</subfield></datafield><datafield tag="520" ind1="3" ind2=" "><subfield code="a">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 multiagent 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. Multiagent 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 multiagent systems Both scientists and practitioners in the field of robotics will find this text valuable</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Mehragentensystem</subfield><subfield code="0">(DE-588)4389058-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Mehrrobotersystem</subfield><subfield code="0">(DE-588)4734038-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Drone aircraft / Automatic control</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Multiagent systems</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Adaptive control systems</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">COMPUTERS / Computer Graphics / Game Programming & Design</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">COMPUTERS / Machine Theory</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">COMPUTERS / Neural Networks</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Adaptive control systems</subfield></datafield><datafield tag="653" ind1=" " ind2="0"><subfield code="a">Multiagent systems</subfield></datafield><datafield tag="653" ind1=" " ind2="6"><subfield code="a">Electronic books</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Mehrrobotersystem</subfield><subfield code="0">(DE-588)4734038-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Mehragentensystem</subfield><subfield code="0">(DE-588)4389058-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032329651</subfield></datafield></record></collection> |
| id | DE-604.BV046920427 |
| illustrated | Illustrated |
| index_date | 2024-07-03T15:31:06Z |
| indexdate | 2024-07-10T08:57:31Z |
| institution | BVB |
| isbn | 9781003026686 9780367457822 1003026680 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032329651 |
| oclc_num | 1220878899 |
| open_access_boolean | |
| owner | DE-573 |
| owner_facet | DE-573 |
| physical | ix, 263 Seiten Illustrationen, Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | CRC Press |
| record_format | marc |
| series2 | Chapman & Hall/CRC artificial intelligence and robotics series |
| spelling | Bestaoui Sebbane, Yasmina 1960- Verfasser (DE-588)1166462471 aut Multi-UAV planning and task allocation Yasmina Bestaoui-Sebbane Boca Raton CRC Press 2020 ix, 263 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Chapman & Hall/CRC artificial intelligence and robotics series Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Author -- Chapter 1 Multi-Aerial-Robot Planning -- 1.1 Introduction -- 1.2 Team Approach -- 1.2.1 Cooperation -- 1.2.2 Cascade-Type Guidance Law -- 1.2.3 Consensus Approach -- 1.2.3.1 Consensus Opinion -- 1.2.3.2 Reachability and Observability -- 1.2.4 Flocking Behavior -- 1.2.4.1 Collective Potential of Flocks -- 1.2.4.2 Distributed Flocking Algorithms -- 1.2.5 Connectivity and Convergence of Formations -- 1.2.5.1 Problem Formulation -- 1.2.5.2 Stability of Formations in Time-Invariant Communication 1.3 Deterministic Decision-Making -- 1.3.1 Distributed Receding Horizon Control -- 1.3.2 Conflict Resolution -- 1.3.2.1 Distributed Reactive Collision Avoidance -- 1.3.2.2 Deconfliction Maintenance -- 1.3.3 Artificial Potential -- 1.3.3.1 Velocity Field -- 1.3.3.2 Artificial Potential Field -- 1.3.3.3 Pattern Formation and Reconfigurability -- 1.3.4 Symbolic Planning -- 1.4 Association with Limited Communication -- 1.4.1 Introduction -- 1.4.2 Problem Formulation -- 1.4.2.1 Decentralized Resolution of Inconsistent Association -- 1.4.3 Genetic Algorithms -- 1.4.4 Games Theory Reasoning 1.4.4.1 Cooperative Protocol -- 1.4.4.2 Non-Cooperative Protocol -- 1.4.4.3 Leader/Follower Protocol -- 1.5 Multiagent Decision-Making under Uncertainty -- 1.5.1 Decentralized Team Decision Problem -- 1.5.1.1 Bayesian Strategy -- 1.5.1.2 Semi-Modeler Strategy -- 1.5.1.3 Communication Models -- 1.5.2 Algorithms for Optimal Planning -- 1.5.2.1 Multiagent A* (MAA*): A Heuristic Search Algorithm for DEC-POMDP -- 1.5.2.2 Policy Iteration for Infinite Horizon -- 1.5.2.3 Linear-Quadratic Approach -- 1.5.2.4 Decentralized Chance-Constrained Finite Horizon Optimal Control 1.5.3 Task Allocation: Optimal Assignment -- 1.5.3.1 Hungarian Algorithm -- 1.5.3.2 Interval Hungarian Algorithm -- 1.5.3.3 Quantifying the Effect of Uncertainty -- 1.5.3.4 Uncertainty Measurement for a Single Utility -- 1.5.4 Distributed Chance-Constrained Task Allocation -- 1.5.4.1 Chance-Constrained Task Allocation -- 1.5.4.2 Distributed Approximation to the Chance-Constrained Task Allocation Problem -- 1.6 Case Studies -- 1.6.1 Reconnaissance Mission -- 1.6.1.1 General Vehicle Routing Problem -- 1.6.1.2 Chinese Postman Problem -- 1.6.1.3 Cluster Algorithm -- 1.6.1.4 The Rural CPP 1.6.2 Expanding Grid Coverage -- 1.6.3 Optimization of Perimeter Patrol Operations -- 1.6.3.1 Multiagent Markov Decision Process -- 1.6.3.2 Anytime Error Minimization Search -- 1.6.4 Stochastic Strategies for Surveillance -- 1.6.4.1 Analysis Methods -- 1.6.4.2 Problems in 1D -- 1.6.4.3 Complete Graphs -- 1.7 Conclusions -- Chapter 2 Flight Planning -- 2.1 Introduction -- 2.2 Path and Trajectory Planning -- 2.2.1 Trim Trajectories -- 2.2.2 Trajectory Planning -- 2.2.2.1 Time Optimal Trajectories -- 2.2.2.2 Nonholonomic Motion Planning -- 2.2.3 Path Planning -- 2.2.3.1 B-Spline Formulation 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 multiagent 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. Multiagent 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 multiagent systems Both scientists and practitioners in the field of robotics will find this text valuable Mehragentensystem (DE-588)4389058-1 gnd rswk-swf Mehrrobotersystem (DE-588)4734038-1 gnd rswk-swf Drone aircraft / Automatic control Multiagent systems Adaptive control systems COMPUTERS / Computer Graphics / Game Programming & Design COMPUTERS / Machine Theory COMPUTERS / Neural Networks Electronic books Mehrrobotersystem (DE-588)4734038-1 s Mehragentensystem (DE-588)4389058-1 s DE-604 Erscheint auch als Online-Ausgabe |
| spellingShingle | Bestaoui Sebbane, Yasmina 1960- Multi-UAV planning and task allocation Intro -- Half Title -- Series Page -- Title Page -- Copyright Page -- Table of Contents -- Author -- Chapter 1 Multi-Aerial-Robot Planning -- 1.1 Introduction -- 1.2 Team Approach -- 1.2.1 Cooperation -- 1.2.2 Cascade-Type Guidance Law -- 1.2.3 Consensus Approach -- 1.2.3.1 Consensus Opinion -- 1.2.3.2 Reachability and Observability -- 1.2.4 Flocking Behavior -- 1.2.4.1 Collective Potential of Flocks -- 1.2.4.2 Distributed Flocking Algorithms -- 1.2.5 Connectivity and Convergence of Formations -- 1.2.5.1 Problem Formulation -- 1.2.5.2 Stability of Formations in Time-Invariant Communication 1.3 Deterministic Decision-Making -- 1.3.1 Distributed Receding Horizon Control -- 1.3.2 Conflict Resolution -- 1.3.2.1 Distributed Reactive Collision Avoidance -- 1.3.2.2 Deconfliction Maintenance -- 1.3.3 Artificial Potential -- 1.3.3.1 Velocity Field -- 1.3.3.2 Artificial Potential Field -- 1.3.3.3 Pattern Formation and Reconfigurability -- 1.3.4 Symbolic Planning -- 1.4 Association with Limited Communication -- 1.4.1 Introduction -- 1.4.2 Problem Formulation -- 1.4.2.1 Decentralized Resolution of Inconsistent Association -- 1.4.3 Genetic Algorithms -- 1.4.4 Games Theory Reasoning 1.4.4.1 Cooperative Protocol -- 1.4.4.2 Non-Cooperative Protocol -- 1.4.4.3 Leader/Follower Protocol -- 1.5 Multiagent Decision-Making under Uncertainty -- 1.5.1 Decentralized Team Decision Problem -- 1.5.1.1 Bayesian Strategy -- 1.5.1.2 Semi-Modeler Strategy -- 1.5.1.3 Communication Models -- 1.5.2 Algorithms for Optimal Planning -- 1.5.2.1 Multiagent A* (MAA*): A Heuristic Search Algorithm for DEC-POMDP -- 1.5.2.2 Policy Iteration for Infinite Horizon -- 1.5.2.3 Linear-Quadratic Approach -- 1.5.2.4 Decentralized Chance-Constrained Finite Horizon Optimal Control 1.5.3 Task Allocation: Optimal Assignment -- 1.5.3.1 Hungarian Algorithm -- 1.5.3.2 Interval Hungarian Algorithm -- 1.5.3.3 Quantifying the Effect of Uncertainty -- 1.5.3.4 Uncertainty Measurement for a Single Utility -- 1.5.4 Distributed Chance-Constrained Task Allocation -- 1.5.4.1 Chance-Constrained Task Allocation -- 1.5.4.2 Distributed Approximation to the Chance-Constrained Task Allocation Problem -- 1.6 Case Studies -- 1.6.1 Reconnaissance Mission -- 1.6.1.1 General Vehicle Routing Problem -- 1.6.1.2 Chinese Postman Problem -- 1.6.1.3 Cluster Algorithm -- 1.6.1.4 The Rural CPP 1.6.2 Expanding Grid Coverage -- 1.6.3 Optimization of Perimeter Patrol Operations -- 1.6.3.1 Multiagent Markov Decision Process -- 1.6.3.2 Anytime Error Minimization Search -- 1.6.4 Stochastic Strategies for Surveillance -- 1.6.4.1 Analysis Methods -- 1.6.4.2 Problems in 1D -- 1.6.4.3 Complete Graphs -- 1.7 Conclusions -- Chapter 2 Flight Planning -- 2.1 Introduction -- 2.2 Path and Trajectory Planning -- 2.2.1 Trim Trajectories -- 2.2.2 Trajectory Planning -- 2.2.2.1 Time Optimal Trajectories -- 2.2.2.2 Nonholonomic Motion Planning -- 2.2.3 Path Planning -- 2.2.3.1 B-Spline Formulation Mehragentensystem (DE-588)4389058-1 gnd Mehrrobotersystem (DE-588)4734038-1 gnd |
| subject_GND | (DE-588)4389058-1 (DE-588)4734038-1 |
| title | Multi-UAV planning and task allocation |
| title_auth | Multi-UAV planning and task allocation |
| title_exact_search | Multi-UAV planning and task allocation |
| title_exact_search_txtP | Multi-UAV planning and task allocation |
| title_full | Multi-UAV planning and task allocation Yasmina Bestaoui-Sebbane |
| title_fullStr | Multi-UAV planning and task allocation Yasmina Bestaoui-Sebbane |
| title_full_unstemmed | Multi-UAV planning and task allocation Yasmina Bestaoui-Sebbane |
| title_short | Multi-UAV planning and task allocation |
| title_sort | multi uav planning and task allocation |
| topic | Mehragentensystem (DE-588)4389058-1 gnd Mehrrobotersystem (DE-588)4734038-1 gnd |
| topic_facet | Mehragentensystem Mehrrobotersystem |
| work_keys_str_mv | AT bestaouisebbaneyasmina multiuavplanningandtaskallocation |