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Path Planning for Vehicles Operating in Uncertain 2D-environments presents a survey that includes several path planning methods developed using fuzzy logic, grapho-analytical search, neural networks, and neural-like structures, procedures of genetic search, and unstable motion modes.
- Presents a survey of accounting limitations imposed by vehicle dynamics
- Proposes modified and new original methods, including neural networking, grapho-analytical, and nature-inspired
- Gives tools for a novice researcher to select a method that would suit their needs or help to synthesize new hybrid methods
Scientists working in the area of system analysis, robotics and control systems; Vehicle control systems designers; Developers of control systems and algorithms for robotic complexes; post-graduate and graduate students studying robotics, control systems, mechatronics and preparing their theses
Chapter One. Position-Path Control of a Vehicle
- 1.1. Motion-Control Systems Problems Analysis
- 1.2. Mathematical Models of Motion
- 1.3. Motion Path Planning
- 1.4. Algorithms of Position-Path Control
- 1.5. Requirements of Path Planners
- 1.6. Summary
Chapter Two. Neural Networking Path Planning Based on Neural-Like Structures
- 2.1. Bionic Approach to Building a Neural Network–Based Vehicle Path Planner in 2D Space
- 2.2. Synthesis of Neural Networking Planner as a Part of Position-Path Control System. Task Statement
- 2.3. Development of the Basic Method of Determining the Vehicle's Motion Direction under the Conditions of Uncertainty
- 2.4. Bionic Method of Neural-Networking Path Search
- 2.5. Convolutional Neural Networks1
- 2.6. Summary
Chapter Three. Vehicles Fuzzy Control Under the Conditions of Uncertainty
- 3.1. Types of Uncertainties
- 3.2. Applications of Fuzzy Logic in Vehicles Control
- 3.3. Vehicle's Path Planning
- 3.4. Development of the Vehicle's Behavioral Model Using Fuzzy-Logic Apparatus
- 3.5. Vehicle Motion Control Principles
- 3.6. Summary
Chapter Four. Genetic Algorithms Path Planning
- 4.1. Generalized Planning Algorithm
- 4.2. Graph Formation
- 4.3. Development of Genetic Algorithms for Planning
- 4.4. Modeling Results of Using Genetic Algorithms for Path Finding
- 4.5. Imitation Modeling Results for Path Planning With Mapping
- 4.6. Summary
Chapter Five. Graphic-Analytical Approaches to Vehicle's Motion Planning
- 5.1. Potential-Field Method in Vehicles Control
- 5.2. Application of Voronoi Diagrams to Path Planning
- 5.3. Vehicle Motion Planning Accounting for the Vehicle's Inertia
- 5.4. Summary
Chapter Six. Motion Planning and Control Using Bionic Approaches Based on Unstable Modes
- 6.1. Non-Formalized Environments With Point Obstacles
- 6.2. Non-Formalized Environments With Complicated Obstacles
- 6.3. Coordinated Application of Unstable Modes and Virtual Point for Obstacle Avoidance
- 6.4. Summary
- No. of pages:
- © Butterworth-Heinemann 2017
- 1st February 2017
- Paperback ISBN:
- eBook ISBN:
Director of Research and Development Institute of Robotics and Control Systems of Southern Federal University. Head of the Robotics and Intelligent Systems Laboratory
PhD, Associate Professor of Automatic Control Systems Department at Southern Federal University, Russia
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