David Allen Blubaugh - Intelligent Autonomous Drones with Cognitive Deep Learning: Build AI-Enabled Land Drones with the Raspberry Pi 4
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- Book:Intelligent Autonomous Drones with Cognitive Deep Learning: Build AI-Enabled Land Drones with the Raspberry Pi 4
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Intelligent Autonomous Drones with Cognitive Deep Learning: Build AI-Enabled Land Drones with the Raspberry Pi 4: summary, description and annotation
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Youll progress from a list of specifications and requirements, in small and iterative steps, which will then lead to the development of Unified Modeling Language (UML) diagrams based in part to the standards established by for the Robotic Operating System (ROS). The ROS architecture has been used to develop land-based drones. This will serve as a reference model for the software architecture of unmanned systems.
Using this approach youl be able to develop a fully autonomous drone that incorporates object-oriented design and cognitive deep learning systems that adapts to multiple simulation environments. These multiple simulation environments will also allow you to further build public trust in the safety of artificial intelligence within drones and small UAS. Ultimately, youll be able to build a complex system using the standards developed, and create other intelligent systems of similar complexity and capability.
Intelligent Autonomous Drones with Cognitive Deep Learning uniquely addresses both deep learning and cognitive deep learning for developing near autonomous drones.
What Youll Learn
- Examine the necessary specifications and requirements for AI enabled drones for near-real time and near fully autonomous drones
- Look at software and hardware requirements
- Understand unified modeling language (UML) and real-time UML for design
- Study deep learning neural networks for pattern recognition
- Review geo-spatial Information for the development of detailed mission planning within these hostile environments
Who This Book Is For
Primarily for engineers, computer science graduate students, or even a skilled hobbyist. The target readers have the willingness to learn and extend the topic of intelligent autonomous drones. They should have a willingness to explore exciting engineering projects that are limited only by their imagination. As far as the technical requirements are concerned, they must have an intermediate understanding of object-oriented programming and design.
David Allen Blubaugh: author's other books
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The Apress logo.
This Apress imprint is published by the registered company APress Media, LLC, part of Springer Nature.
The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.
Any source code or other supplementary material referenced by the author in this book is available to readers on the Github repository: https://github.com/Apress/Intelligent-Autonomous-Drones-with-Cognitive-Deep-Learning. For more detailed information, please visit http://www.apress.com/source-code.
A photo of one of the male authors.
, PhD, is staff engineer and scientist in the Dayton Engineering Advanced Projects Lab at the Southwest Research Institute. He is a senior leader and defense research and engineering professional with over 25 years of experience in multiple engineering and aviation disciplines and applications. He leads and performs ongoing basic and applied research projects, including the development of third-generation spiking neural networks (SNNs), neuromorphic engineering, and neuromorphic applications that include human autonomy teaming. He has supported the Air Force Research Laboratory Sensors Directorate at Wright-Patterson Air Force Base in Ohio, and at the Air Force Life Cycle Management Center. He is a USAF test pilot with over 5,000 hours total flying time. He has a PhD in neuroscience, MS in aerospace engineering and mathematics, and BS in electrical engineering. Dr. Harbour also teaches at the University of Dayton and Sinclair College.
has an in-depth understanding of the theory behind drone missions and crew resource management. He also has applied experience as a drone pilot/operator who conducted missions as a civilian contractor in both the Iraq and Afghanistan areas of operation.
is a computer science instructor at Wright State University with experience in embedded systems development. He also has developed and worked in various fields within the universe of artificial intelligence.
Imagine this: You are an aspiring engineer and founder of a company called Advanced Technologies & Resources, LLC (ATR) . Your company has just won a multi-million-dollar contract with the Egyptian government and Egyptian Supreme Council of Antiquities. The Egyptian government wants to explore the inner areas of the pyramids . This includes the caverns, wells, and pits found within the Great Pyramids at Giza. However, there is problem! Some of the caverns and wells are prone to sudden cave-ins, and there is the possibility of undetected booby-traps that were set by the pyramid builders themselves back in the year 2553 BC. Furthermore, poisonous gasses, such as carbon monoxide, have been accumulating within the tombs of the pyramids for more than 4,500 years, making these areas dangerous for human exploration.
A still of the pyramids and tombs of Giza in Egypt.
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