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008			210630s2021 sz | s |||| 0|eng d
020			_a9783030765590
024	7		_a10.1007/978-3-030-76559-0 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQA402.5 _b2021 EB
100			_aPérez Carabaza, Sara _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9681811
245	1	0	_aMulti-UAS Minimum Time Search in Dynamic and Uncertain Environments _cby Sara Pérez Carabaza.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (XIX, 183 páginas) _b71 ilustraciones, 60 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aSpringer Theses Recognizing Outstanding Ph.D. Research _x2190-5061
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aIntroduction -- State of the Art -- Problem Formulation and Optimization Approach -- MTS Algorithms for Cardinal UAV Motion Models.
520	3		_aThis 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.
988			_aSpringer_Robotics_2021
650		7	_2embne _9141162 _aAlgoritmos
776	0	8	_iPrinted edition: _z9783030765583
776	0	8	_iPrinted edition: _z9783030765606
776	0	8	_iPrinted edition: _z9783030765613
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-76559-0 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE