Lighter than Air Robots : Guidance and Control of Autonomous Airships
An aerial robot is a system capable of sustained flight with no direct human control and able to perform a specific task. A lighter than air robot is an aerial robot that relies on the static lift to balance its own weight. It can also be defined as a lighter than air unmanned aerial vehicle or an u...
Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Livre numérique |
| Lenguaje: | Anglais |
| Publicado: |
Dordrecht :
Springer Netherlands
2012.
Cham : Springer Nature |
| Colección: | Intelligent Systems, Control and Automation: Science and Engineering
58 |
| Materias: | |
| Acceso en línea: | Accès sur la plateforme de l'éditeur Accès sur la plateforme Istex Accès Université d'Orléans Accès INSA CVL |
| Nota: |
Archives Springer e-books (Licence nationale) Archives Springer e-books (Licence nationale) |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Lighter than air robots, Guidance and control of autonomous airships, Yasminia Bestaoui Sebbane, Dordrecht, Springer, 2012, 1 vol. (XVII-251 p.), Intelligent systems, control and automation, 978-94-007-2662-8 |
Tabla de Contenidos:
- 1 Introduction 1.1 Aerial robotics 1.2 Outline of the book 2 Modeling 2.1 Introduction 2.2 Kinematics 2.2.1 Euler angles 2.2.2 Euler parameters 2.3 Dynamics 2.3.1 Mass Characteristics 2.3.2 6 DOF Dynamics : Newton-Euler Approach 2.3.3 6 DOF Dynamics : Lagrange Approach 2.3.4 Translational Dynamics . 2.4 Aerology Characteristics 2.4.1 Wind Profile 2.4.2 Down burst 2.5 Conclusions 3 Mission Planning 3.1 Introduction 3.2 Flight Planning 3.3 Motion Planning Algorithms Review 3.3.1 Overall Problem description 3.3.2 Problem Types 3.4 Planning with differential constraints 3.4.1 Roadmap algorithm 3.4.2 Artificial Potential Methods 3.4.3 Sampling based trajectory planning 3.4.4 Decoupled Trajectory Planning 3.4.5 The Finite State Motion Model: The Maneuver Automaton 3.4.6 Mathematical Programming 3.4.7 Receding Horizon Control 3.4.8 Reactive Planning 3.4.9 Probabilistic Roadmap Methods: PRM 3.4.10 Rapidly Expanding Random Tree (RRT) 3.4.11 Guided Expansive Search Trees 3.5 Planning with Uncertain Winds 3.5.1 Receding Horizon Approach 3.5.2 Markov Decision Process Approach 3.5.3 Chance constrained predictive control under stochastic uncertainty 3.6 Planning in Strong Winds 3.7 Task Assignment 3.8 Conclusions 4.1 Introduction 4.2 Trajectory Generation in Hover 4.2.1 Trim Trajectories 4.2.2 Under-actuation at Hover 4.3 Lateral planning in cruising flight 4.3.1 Lateral dynamics of the lighter than air robot 4.3.2 Time Optimal Extremals 4.4 Zermelo Navigation Problem 4.4.1 Navigation equation