Intelligent Autonomous Drones with Cognitive Deep Learning: Build Ai-Enabled Land Drones with the Raspberry Pi 4

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.

什麼是人工智慧（AI）啟用的無人機，它能做什麼？AI啟用的無人機比人控制的無人機更好嗎？本書將回答這些問題，並使您能夠開發自己的AI啟用無人機。

您將逐步從規格和需求清單中進行小而迭代的步驟，然後根據部分基於機器人操作系統（ROS）建立的標準，開發統一建模語言（UML）圖。ROS架構已被用於開發陸地無人機。這將作為無人系統軟體架構的參考模型。

使用這種方法，您將能夠開發一個完全自主的無人機，該無人機結合了面向對象的設計和認知深度學習系統，能夠適應多個模擬環境。這些多個模擬環境還將使您能夠進一步建立公眾對無人機和小型無人系統中人工智慧安全性的信任。最終，您將能夠使用所開發的標準構建一個複雜的系統，並創建具有相似複雜性和能力的其他智能系統。

《具有認知深度學習的智能自主無人機》獨特地涵蓋了深度學習和認知深度學習，用於開發近乎自主的無人機。

您將學到什麼：
- 檢查AI啟用無人機的必要規格和需求，以實現近實時和近全自主的無人機
- 研究軟體和硬體需求
- 理解統一建模語言（UML）和實時UML設計
- 研究用於模式識別的深度學習神經網絡
- 審查地理空間信息，以便在這些惡劣環境中進行詳細的任務規劃

本書適合對象：
主要針對工程師、計算機科學研究生，甚至是熟練的愛好者。目標讀者應該有學習和擴展智能自主無人機主題的意願。他們應該有探索令人興奮的工程項目的意願，這些項目只受他們的想像力限制。就技術要求而言，他們必須對面向對象的編程和設計有中級的理解。

Dr. Stephen Harbour is an experienced technical adviser skilled in artificial intelligence, cognitive engineering, proposal writing, technical writing, research, and command. Harbour is a strong program and project management professional with a Doctor of Philosophy (PhD) focused in Cognitive Science from Northcentral University and teaches at the University of Dayton.

Benjamin Sears has an in depth understanding of the theory behind drone missions and crew resource management but also has applied experience in being an actual drone pilot operator who served with distinction 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 decode of experience in applied engineering projects, embedded systems, design, computer science, and computer engineering.

Dr. Stephen Harbour是一位經驗豐富的技術顧問，擅長人工智慧、認知工程、提案撰寫、技術寫作、研究和指揮。Harbour是一位具有博士學位的強大的程式和專案管理專業人士，專攻認知科學，畢業於Northcentral大學，並在Dayton大學任教。

Benjamin Sears對無人機任務和機組資源管理的理論有深入的了解，同時也具有實際無人機飛行員的應用經驗，在伊拉克和阿富汗地區的任務中表現優異。

Michael J. Findler是Wright State大學的計算機科學講師，擁有在嵌入式系統開發項目中工作的經驗。Mike Findler還在人工智慧的各個領域中進行開發和研究，無疑將在《認知深度學習應用於自主無人機和無人機任務》手稿的開發過程中成為一個優秀的資訊來源。

David Allen Blubaugh在應用工程項目、嵌入式系統、設計、計算機科學和計算機工程方面擁有豐富的經驗。