Language:
English
繁體中文
Help
Login
Back
Switch To:
Labeled
|
MARC Mode
|
ISBD
Deep Learning for Unmanned Systems
~
Koubaa, Anis.
Deep Learning for Unmanned Systems
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Deep Learning for Unmanned Systems/ edited by Anis Koubaa, Ahmad Taher Azar.
other author:
Koubaa, Anis.
Description:
VIII, 732 p. 363 illus., 281 illus. in color.online resource. :
Contained By:
Springer Nature eBook
Subject:
Control engineering. -
Online resource:
https://doi.org/10.1007/978-3-030-77939-9
ISBN:
9783030779399
Deep Learning for Unmanned Systems
Deep Learning for Unmanned Systems
[electronic resource] /edited by Anis Koubaa, Ahmad Taher Azar. - 1st ed. 2021. - VIII, 732 p. 363 illus., 281 illus. in color.online resource. - Studies in Computational Intelligence,9841860-9503 ;. - Studies in Computational Intelligence,564.
Deep 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.
This 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.
ISBN: 9783030779399
Standard No.: 10.1007/978-3-030-77939-9doiSubjects--Topical Terms:
1249728
Control engineering.
LC Class. No.: TJ210.2-211.495
Dewey Class. No.: 629.8
Deep Learning for Unmanned Systems
LDR
:04716nam a22004335i 4500
001
1054941
003
DE-He213
005
20211001185304.0
007
cr nn 008mamaa
008
220103s2021 sz | s |||| 0|eng d
020
$a
9783030779399
$9
978-3-030-77939-9
024
7
$a
10.1007/978-3-030-77939-9
$2
doi
035
$a
978-3-030-77939-9
050
4
$a
TJ210.2-211.495
050
4
$a
TJ163.12
072
7
$a
TJFM
$2
bicssc
072
7
$a
TEC004000
$2
bisacsh
072
7
$a
TJFM
$2
thema
072
7
$a
TJFD
$2
thema
082
0 4
$a
629.8
$2
23
245
1 0
$a
Deep Learning for Unmanned Systems
$h
[electronic resource] /
$c
edited by Anis Koubaa, Ahmad Taher Azar.
250
$a
1st ed. 2021.
264
1
$a
Cham :
$b
Springer International Publishing :
$b
Imprint: Springer,
$c
2021.
300
$a
VIII, 732 p. 363 illus., 281 illus. in color.
$b
online resource.
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
347
$a
text file
$b
PDF
$2
rda
490
1
$a
Studies in Computational Intelligence,
$x
1860-9503 ;
$v
984
505
0
$a
Deep 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.
520
$a
This 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.
650
0
$a
Control engineering.
$3
1249728
650
0
$a
Robotics.
$3
561941
650
0
$a
Mechatronics.
$3
559133
650
0
$a
Computational intelligence.
$3
568984
650
0
$a
Engineering—Data processing.
$3
1297966
650
1 4
$a
Control, Robotics, Mechatronics.
$3
768396
650
2 4
$a
Computational Intelligence.
$3
768837
650
2 4
$a
Data Engineering.
$3
1226308
700
1
$a
Koubaa, Anis.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1021160
700
1
$a
Azar, Ahmad Taher.
$4
edt
$4
http://id.loc.gov/vocabulary/relators/edt
$3
1064152
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783030779382
776
0 8
$i
Printed edition:
$z
9783030779405
776
0 8
$i
Printed edition:
$z
9783030779412
830
0
$a
Studies in Computational Intelligence,
$x
1860-949X ;
$v
564
$3
1253640
856
4 0
$u
https://doi.org/10.1007/978-3-030-77939-9
912
$a
ZDB-2-INR
912
$a
ZDB-2-SXIT
950
$a
Intelligent Technologies and Robotics (SpringerNature-42732)
950
$a
Intelligent Technologies and Robotics (R0) (SpringerNature-43728)
based on 0 review(s)
Multimedia
Reviews
Add a review
and share your thoughts with other readers
Export
pickup library
Processing
...
Change password
Login