Cargando…
Path Planning for Vehicles Operating in Uncertain 2D environments.
"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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Saint Louis :
Elsevier Science,
2017.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Path Planning for Vehicles Operating in Uncertain 2D Environments; Path Planning for Vehicles Operating in Uncertain 2D Environments; Copyright; Contents; List of Contributors; Acknowledgment; Abbreviations; Introduction; One
- Position-Path Control of a Vehicle; 1.1 MOTION-CONTROL SYSTEMS PROBLEMS ANALYSIS; 1.2 MATHEMATICAL MODELS OF MOTION; 1.2.1 Vehicle's Mathematical Model; 1.2.2 Mathematical Model of a Wheeled Vehicle; 1.2.3 Description of Vehicle Functioning Test Scenes; 1.3 MOTION PATH PLANNING; 1.4 ALGORITHMS OF POSITION-PATH CONTROL; 1.5 REQUIREMENTS OF PATH PLANNERS.
- 1.6 SUMMARYREFERENCES; Two
- Neural Networking Path Planning Based on Neural-Like Structures; 2.1 BIONIC APPROACH TO BUILDING A NEURAL NETWORK-BASED VEHICLE PATH PLANNER IN2D 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; 2.3.1 Task Statement; 2.3.2 Method Development on a Classical Basis; 2.3.3 Method Modeling Results in MatLab; 2.3.4 Implementation of DVH Method in Formal Neurons.
- 2.3.5 Modeling of DVH-NN Planner in MatLab2.4 BIONIC METHOD OF NEURAL-NETWORKING PATH SEARCH; 2.4.1 Planning Task Formalization for the Considered Method; 2.4.2 Development of the Method and of Neural-like Structures Implementing It; 2.4.2.1 DEFINITION OF A FORMAL NEURON AS A THRESHOLD ELEMENT; 2.4.2.1.1 Development of a Neural Networking System of Afferent Synthesis; 2.4.2.1.2 Development of the Decision-Making Unit; 2.4.2.1.3 Development of the Neural Networking Planner; 2.4.3 Method Modeling in MatLab.
- 2.5 CONVOLUTIONAL NEURAL NETWORKS11THE WORK OF M.U. SIROTENKO [100] WAS USED IN WRITING OF THIS SECTION. 2.5.1 Path Planner Structure; 2.5.2 Methods of Visual Planning and Obstacle Avoidance; 2.5.3 Local Motions Planner; 2.5.4 Motion Planner Modeling; 2.5.5 Simplified Version of a Neural Networking Motion Planner Based on a Convolutional Neural Network; 2.6 SUMMARY; REFERENCES; Three
- Vehicles Fuzzy Control Under the Conditions of Uncertainty; 3.1 TYPES OF UNCERTAINTIES; 3.2 APPLICATIONS OF FUZZY LOGIC IN VEHICLES CONTROL; 3.2.1 Formalization of Environmental Description.
- 3.2.2 Formalization of the Vehicle's BehaviorCLASSIFICATION MODEL; MODEL FOR CALCULATING THE TRUTH DEGREE OF THE FUZZY PRODUCTION RULES; SITUATIONAL MODEL; 3.3 VEHICLE'S PATH PLANNING; 3.4 DEVELOPMENT OF THE VEHICLE'S BEHAVIORAL MODEL USING FUZZY-LOGIC APPARATUS; 3.4.1 Analysis of the Motion Paths Formation Principles; 3.4.2 Analysis of Methods of Vehicle's Behavior Coordination; 3.5 VEHICLE MOTION CONTROL PRINCIPLES; 3.5.1 General Motion Control Principle; 3.5.2 Behavior Formation General Principle; 3.5.3 Goal-Reaching Behavior; 3.5.4 Obstacle-Avoidance Behavior.