Overview
- Nominated as an outstanding PhD thesis by Universidad Complutense de Madrid, Spain
- Describes the development of ant colony metaheuristic methods for solving the minimum time search (MTS) problem
- Reports on a real-world application to the ATLANTE UAV
Part of the book series: Springer Theses (Springer Theses)
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Table of contents (7 chapters)
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Front Matter
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Back Matter
Keywords
- UAVs Search Trajectories
- UAV Dynamic Model
- SAVIER Project (Airbus)
- Probabilistic Path Planning
- Probabilistic Search Algorithms
- Minimum Time Search Planner
- Multi UAV Evolutionary Planner
- UAV Trajectory Optimization
- UAV Cardinal Motion Model
- Max-Min Ant System
- ACO in Continuous Domain
- Multi-Stepped ACOR
- Multi-Stepped GA
- Bioinspired metaheuristics
About this book
This book proposes some novel approaches for finding unmanned aerial vehicle trajectories to reach targets with unknown location in minimum time. At first, it reviews probabilistic search algorithms that have been used for dealing with the minimum time search (MTS) problem, and discusses how metaheuristics, and in particular the ant colony optimization algorithm (ACO), can help to find high-quality solutions with low computational time. Then, it describes two ACO-based approaches to solve the discrete MTS problem and the continuous MTS problem, respectively. In turn, it reports on the evaluation of the ACO-based discrete and continuous approaches to the MTS problem in different simulated scenarios, showing that the methods outperform in most all the cases over other state-of-the-art approaches. In the last part of the thesis, the work of integration of the proposed techniques in the ground control station developed by Airbus to control ATLANTE UAV is reported in detail, providing practical insights into the implementation of these methods for real UAVs.
Authors and Affiliations
Bibliographic Information
Book Title: Multi-UAS Minimum Time Search in Dynamic and Uncertain Environments
Authors: Sara Pérez Carabaza
Series Title: Springer Theses
DOI: https://doi.org/10.1007/978-3-030-76559-0
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2021
Hardcover ISBN: 978-3-030-76558-3Published: 01 July 2021
Softcover ISBN: 978-3-030-76561-3Published: 02 July 2022
eBook ISBN: 978-3-030-76559-0Published: 30 June 2021
Series ISSN: 2190-5053
Series E-ISSN: 2190-5061
Edition Number: 1
Number of Pages: XIX, 183
Number of Illustrations: 11 b/w illustrations, 60 illustrations in colour
Topics: Computational Intelligence, Control and Systems Theory, Aerospace Technology and Astronautics