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· 제목 : Intelligent Autonomous Drones with Cognitive Deep Learning: Build Ai-Enabled Land Drones with the Raspberry Pi 4 (Paperback)
· 분류 : 외국도서 > 컴퓨터 > 하드웨어 > 일반
· ISBN : 9781484268025
· 쪽수 : 511쪽
· 출판일 : 2022-11-01
· 분류 : 외국도서 > 컴퓨터 > 하드웨어 > 일반
· ISBN : 9781484268025
· 쪽수 : 511쪽
· 출판일 : 2022-11-01
목차
Chapter 1. Defining the Required Goals, Specifications, and Requirements
What the Reader will be able to accomplish by reading this chapter:
? Apply simple Statistical tools to analyze specifications
? Appreciate why specifications and requirements are so important.
? Create specifications and requirements for this project.
? Realize the advantages of a created AI enabled land-based rover drone.? Realize the challenges of an AI enabled land-based rover drone.
? Compile a list of all parts and software needed to complete chapter activities.
Chapter 2. UML Systems for Reliable and Robust AI enabled Self-Driving Drones
What the Reader will be able to accomplish by reading this chapter.
? Understand as to what UML and what variants are available.
? Understand the Umbrella UML tool-set for the purpose of this manuscript.
? Review an example of a Subsumption Architecture within UML.
? Understand how UML can be used for machine learning programs.
? Reinforcement Learning.? Deep Reinforcement Learning.
? Cognitive Deep Learning Systems.
? Caveats with using UML for an AI enabled land-based rover drone.
? Review what parts and software tools will be required for this chapter.
Chapter 3. Setting Your Main Virtual Linux System
What the Reader will be able to accomplish by reading this chapter.
? How to use Linux Development Tools
? Why Virtual Linux allows us to have a complete system.
? Why Anaconda and Python are better within Linux.
? Understand examples of Anaconda use with AIRSIM drone simulator.? Understand why testing with different simulators enhances reliability.
? Understand what software tools will be required for this chapter.
Chapter 4. Understanding Advanced Anaconda Concepts
What the Reader will be able to accomplish by reading this chapter.
? Review Examples of Machine Learning Systems with Anaconda
? Review Examples of Anaconda development for Raspberry PI.
? Review Examples of Anaconda use with OpenAI GYM simulator.
? Review Examples of Anaconda use with Gazebo simulator.
? Review Examples of Anaconda use with VREP drone simulator.
? Review Examples of Anaconda use within drone simulators.
? Review Examples of Anaconda use with AIRSIM drone simulator.
? Review Examples of Anaconda for creation of a Ground Control Station.
? Understand why testing with different simulators enhances reliability.
? Understand what software tools will be required for this chapter.
Chapter 5. Understanding Drone-Kit for Testing and Programming your Self-Driving Drone
What the Reader will be able to accomplish by reading this chapter.
? Understand why drone-kit should be used as the first testing platform.
? Understand how to install drone-kit.
? Recognize as to why using Flight Gear as the First Drone Simulator is important
? Review examples of Flight Gear being used for land-based rover drones.
? Understand how Python, Drone-Kit, and Flight Gear can be used for testing.
Chapter 6. Understanding, Maintaining, and Controlling the DRIVING Trajectory of the AI Rover Drone
What the Reader will be able to accomplish by reading this chapter
? Develop the first tracking and trajectory control scripts.
? Review MAVLink protocols for communications
? Review the importance of the autopilot and then utilize the actual Pixhawk Autopilot.
? Develop the first Inertial Navigation System.
Chapter 7. AI Enabled Rover Drone Vision with the Python OpenCV Library
What the Reader will be able to accomplish by reading this chapter
? Develop the first vision routines.
? Integrate vision with deep learning and cognitive processing.
? Understand and develop vision terrain analysis.
? Understand camera and sensor calibration techniques.
? Understand how Multiple View Geometry works.
Chapter 8. Your First Experience with Creating Drone Reinforcement Learning for Self-Driving and Exploring
What the Reader will be able to accomplish by reading this chapter
? Understand Reinforcement Learning.
? Understand OpenAI Gym
? Develop the first drone reinforcement drone agent in OpenAI.
? Develop routines for a land-rover drones.
Chapter 9. AI Enabled Rover Drones with Advanced Deep Learning
What the Reader will be able to accomplish by reading this chapter? Why deep and convolution neural networks are important for robotics.
? How to correctly design Deep Learning networks for real-time response.
Chapter 10. Nature's other Secrets (Uncertainty, Bayesian Deep Learning, and Evolutionary Computing for Rovers)
What the Reader will be able to accomplish by reading this chapter
? How evolution can create a smarter drone.
? How Bayesian analysis can make sense of evolution.
? How to evolve the perfect brain-controller.
Chapter 11. Building the Ultimate Cognitive Deep Learning Land-Rover Controller
What the Reader will be able to accomplish by reading this chapter? Understand why Cognitive computing is superior to deep learning.
? Understand the concerns for higher-level artificial intelligence.
? Develop and verify the first drone cognitive architectures within simulators.
? Why the Pixhawk autopilot with acts as the “brain-stem” of these drones.
? Wrap the cognitive controller within both the Subsumption Architecture and ROS.
Chapter 12. AI Drone Verification and Validation with Computer Simulations
What the Reader will be able to accomplish by reading this chapter
? Develop computer simulations to build trust for these drones.
? Find and locate software flaws in these drones before it is too late.
? Verify and Validate these drones with all the fore-mentioned simulators.
Chapter 13. The Critical Need for Geo-Spatial Guidance for AI Rover Drones
What the Reader will be able to accomplish by reading this chapter
? Understand GIS applied to Land drones.
? Incorporating GIS into AI and drone navigation awareness.
? Understand and develop the first navigational GIS routines within these drones.
Chapter 14. Statistics and Experimental Algorithms for Drone Enhancements
What the Reader will be able to accomplish by reading this chapter
? Use the power of statistics to find ever more ways to improve our drones.
? Develop statistical tests for simulations.
? Find and understand the sources of design uncertainty for Land Rover drones.
? Develop incremental improvements to our Rover drones.
Chapter 15. The Robotic Operating System (ROS) Architecture for AI enabled Land-Based Rover Drones.
What the Reader will be able to accomplish by reading this chapter
? Understand why the ROS architecture is important.
? Develop the ROS architecture for Drones.
? How to incorporate everything we have thus built into the ROS.
? Maintain trust within our sophisticated drones.
Chapter 16. Putting it all together and the Testing Required.
What the Reader will be able to accomplish by reading this chapter
? Develop the actual physical Raspberry PI based Rover Drone.
? Understand the difference between your simulated drone and the real-world drone.
? Understand the potential use of CLUSHAT for cluster computing.
? Understand and use off-processing with Intel’s neural sticks.
Chapter 17. “It’s Alive! It’s Alive!” (Facing Ones Very Own Creation)
What the Reader will be able to accomplish by reading this chapter
? Understand as to how they relate to reliability.
? How to investigate and locate the cause of an accident with a land-based drone. Why sense-and-avoid can fail.
Chapter 18. Your Creation can be your Best Friend or your Worst Nightmare.
What the Reader will be able to accomplish by reading this chapter
? Understand the future of drones and artificial intelligence.
? Understand how drones can build a better tomorrow.
