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Books > Computing & IT > Applications of computing > Artificial intelligence
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Deep Learning for Unmanned Systems (Hardcover, 1st ed. 2021)
Loot Price: R6,307
Discovery Miles 63 070
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Deep Learning for Unmanned Systems (Hardcover, 1st ed. 2021)
Series: Studies in Computational Intelligence, 984
Expected to ship within 10 - 15 working days
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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.
General
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