Intelligent autonomous drones with cognitive deep learning : build AI-enabled land drones with the Raspberry Pi 4 / David Allen Blubaugh, Steven D. Harbour, Benjamin Sears, Michael J. Findler.
Blubaugh, David Allen.; Harbour, Steven D.; Sears, Benjamin.; Findler, Michael J.
2022
TJ211.495 .B58 2022
Available Online

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Title Intelligent autonomous drones with cognitive deep learning : build AI-enabled land drones with the Raspberry Pi 4 / David Allen Blubaugh, Steven D. Harbour, Benjamin Sears, Michael J. Findler.
Author Blubaugh, David Allen.
ISBN 9781484268032 electronic book
1484268032 electronic book
1484268024
9781484268025
DOI https://doi.org/10.1007/978-1-4842-6803-2
Published New York : Apress, [2022]
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-1-4842-6803-2 doi
Call Number TJ211.495 .B58 2022
Dewey Decimal Classification 629.8/92
Summary What is an artificial intelligence (AI)-enabled drone and what can it do? Are AI-enabled drones better than human-controlled drones? This book will answer these questions and more, and empower you to develop your own AI-enabled drone. You'll 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 you'l 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, you'll 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 You'll 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.
Access Note Access limited to authorized users.
Source of Description Description based on online resource; title from digital title page (viewed on December 09, 2022).
Added Author Harbour, Steven D.
Sears, Benjamin.
Findler, Michael J.
Available in Other Form Print version: 9781484268025
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Chapter 1. Rover Platform Overview. -Chapter 2. AI Rover System Design and Analysis
Chapter 3. Installing Linux and Development Tools
Chapter 4. Building a Simple Virtual Rover
Chapter 5. Adding Sensors to Our Simulation
Chapter 6. Sense and Avoidance
Chapter 7. Navigation, SLAM, and Goals
Chapter 8. OpenCV and Perception
Chapter 9. Reinforced Learning
Chapter 10. Subsumption Cognitive Architecture
Chapter 11. Geospatial Guidance for AI Rover
Chapter 12. Noetic ROS Further Examined and Explained
Chapter 13. Further Considerations
Appendix A: Bayesian Deep Learning
Appendix B: Open AI Gym
Appendix: Introduction to the Future of AI-ML Research.

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