Intelligent Autonomous Drones with Cognitive Deep Learning : Build AI-Enabled Land Drones with the Raspberry Pi 4 / by David Allen Blubaugh, Steven D. Harbour, Benjamin Sears, Michael J. Findler.

Format:

Book

Author/Creator:

Blubaugh, David Allen, author.

Language:

English

Subjects (All):

Makerspaces.

Machine learning.

Maker.

Machine Learning.

Local Subjects:

Maker.

Machine Learning.

Physical Description:

1 online resource (519 pages)

Edition:

1st ed. 2022.

Place of Publication:

Berkeley, CA : Apress : Imprint: Apress, 2022.

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'll 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. You will: 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.

Contents:

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.

Other Format:

Print version: Blubaugh, David Allen Intelligent Autonomous Drones with Cognitive Deep Learning

ISBN:

9781484268032

1484268032

OCLC:

1349567952