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001440159 019__ $$a1273479671$$a1273670143$$a1287769452$$a1292518134
001440159 020__ $$a9783030779399$$q(electronic bk.)
001440159 020__ $$a3030779394$$q(electronic bk.)
001440159 020__ $$z9783030779382$$q(print)
001440159 020__ $$z3030779386
001440159 0247_ $$a10.1007/978-3-030-77939-9$$2doi
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001440159 049__ $$aISEA
001440159 050_4 $$aTL152.8
001440159 08204 $$a629.04/6$$223
001440159 24500 $$aDeep learning for unmanned systems /$$cAnis Koubaa, Ahmad Taher Azar, editors.
001440159 264_1 $$aCham, Switzerland :$$bSpringer,$$c2021.
001440159 300__ $$a1 online resource (viii, 732 pages) :$$billustrations (some color)
001440159 336__ $$atext$$btxt$$2rdacontent
001440159 337__ $$acomputer$$bc$$2rdamedia
001440159 338__ $$aonline resource$$bcr$$2rdacarrier
001440159 347__ $$atext file
001440159 347__ $$bPDF
001440159 4901_ $$aStudies in computational intelligence,$$x1860-9503 ;$$vvolume 984
001440159 5050_ $$aDeep Learning for Unmanned Autonomous Vehicles: A Comprehensive Review -- Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment -- Reactive Obstacle Avoidance Method for a UAV -- Guaranteed Performances for Learning-Based Control Systems using Robust Control Theory -- A cascaded deep Neural Network for Position Estimation of Industrial Robots -- Managing Deep Learning Uncertainty for Autonomous Systems -- Uncertainty-Aware Autonomous Mobile Robot Navigation with Deep Reinforcement Learning -- Deep Reinforcement Learning for Autonomous Mobile Networks in Micro-Grids -- Reinforcement learning for Autonomous Morphing Control and Cooperative Operations of UAV Cluster -- Image-Based Identification of Animal Breeds Using Deep Learning.
001440159 506__ $$aAccess limited to authorized users.
001440159 520__ $$aThis book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets. In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN). The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science. The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS) The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references. The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques. The book chapters are lucidly illustrated with numerical examples and simulations. The book chapters discuss details of applications and future research areas.
001440159 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 5, 2021).
001440159 650_0 $$aAutomated vehicles$$xControl.
001440159 650_0 $$aAutomated vehicles$$xData processing.
001440159 650_0 $$aMachine learning.
001440159 650_6 $$aVéhicules autonomes$$xLutte contre.
001440159 650_6 $$aVéhicules autonomes$$xInformatique.
001440159 650_6 $$aApprentissage automatique.
001440159 655_7 $$aLlibres electrònics.$$2thub
001440159 655_0 $$aElectronic books.
001440159 7001_ $$aKoubâa, Anis,$$eeditor.
001440159 7001_ $$aAzar, Ahmad Taher,$$eeditor.
001440159 77608 $$iPrint version:$$tDeep learning for unmanned systems.$$dCham, Switzerland : Springer, 2021$$z3030779386$$z9783030779382$$w(OCoLC)1250305180
001440159 830_0 $$aStudies in computational intelligence ;$$vv. 984.$$x1860-9503
001440159 852__ $$bebk
001440159 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-77939-9$$zOnline Access$$91397441.1
001440159 909CO $$ooai:library.usi.edu:1440159$$pGLOBAL_SET
001440159 980__ $$aBIB
001440159 980__ $$aEBOOK
001440159 982__ $$aEbook
001440159 983__ $$aOnline
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