Path Planning for Vehicles Operating in Uncertain 2D Environments

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