Intelligent Autonomous Drones with Cognitive Deep Learning: Build AI-Enabled Land Drones with the Ra
David Allen / Harbour Blubaugh
511 pages, parution le 31/10/2022
Résumé
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.
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.
Chapter 1. Defining the Required Goals, Specifications, and Requirements
Chapter 2. UML Systems for Reliable and Robust AI enabled Self-Driving Drones
Chapter 3. Setting Your Main Virtual Linux System
Chapter 4. Understanding Advanced Anaconda Concepts
Chapter 5. Understanding Drone-Kit for Testing and Programming your Self-Driving Drone
Chapter 6. Understanding, Maintaining, and Controlling the DRIVING Trajectory of the AI Rover Drone
Chapter 7. AI Enabled Rover Drone Vision with the Python OpenCV Library
Chapter 8. Your First Experience with Creating Drone Reinforcement Learning for Self-Driving and Exploring
Chapter 9. AI Enabled Rover Drones with Advanced Deep Learning
Chapter 10. Nature's other Secrets (Uncertainty, Bayesian Deep Learning, and Evolutionary Computing for Rovers)
Chapter 11. Building the Ultimate Cognitive Deep Learning Land-Rover Controller
Chapter 12. AI Drone Verification and Validation with Computer Simulations
Chapter 13. The Critical Need for Geo-Spatial Guidance for AI Rover Drones
Chapter 14. Statistics and Experimental Algorithms for Drone Enhancements
Chapter 15. The Robotic Operating System (ROS) Architecture for AI enabled Land-Based Rover Drones.
Chapter 16. Putting it all together and the Testing Required.
Chapter 17. "It's Alive! It's Alive!" (Facing Ones Very Own Creation)
Chapter 18. Your Creation can be your Best Friend or your Worst Nightmare.
Benjamin Sears has an in-depth understanding of the theory behind drone missions and crew resource management. He also has applied experience as an actual drone pilot/operator who conducted missions as a civilian contractor in both Iraq and Afghanistan areas of operation.
Michael J. Findler is a computer science instructor at Wright State University with experience in working in embedded systems development projects. Mike Findler also has developed and worked on various different fields within the universe of artificial intelligence and will no doubt serve as an excellent source of information during the development of the fore-mentioned manuscript on applications of Cognitive Deep Learning for Autonomous Drones and Drone Missions.
David Allen Blubaugh has a decade of experience in applied engineering projects, embedded systems, design, computer science, and computer engineering.
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Apress |
| Auteur(s) | David Allen / Harbour Blubaugh |
| Parution | 31/10/2022 |
| Nb. de pages | 511 |
| EAN13 | 9781484268025 |
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