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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Dordrecht :
Springer Netherlands : Imprint: Springer,
2012.
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| Edición: | 1st ed. 2012. |
| Colección: | Intelligent Systems, Control and Automation: Science and Engineering,
58 |
| Temas: | |
| Acceso en línea: | Texto Completo |
MARC
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| 100 | 1 | |a Bestaoui Sebbane, Yasmina. |e author. |4 aut |4 http://id.loc.gov/vocabulary/relators/aut | |
| 245 | 1 | 0 | |a Lighter than Air Robots |h [electronic resource] : |b Guidance and Control of Autonomous Airships / |c by Yasmina Bestaoui Sebbane. |
| 250 | |a 1st ed. 2012. | ||
| 264 | 1 | |a Dordrecht : |b Springer Netherlands : |b Imprint: Springer, |c 2012. | |
| 300 | |a XVIII, 254 p. |b online resource. | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 347 | |a text file |b PDF |2 rda | ||
| 490 | 1 | |a Intelligent Systems, Control and Automation: Science and Engineering, |x 2213-8994 ; |v 58 | |
| 505 | 0 | |a 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 -- 4.4.2 One particular solution -- 4.5 3D Trajectory design with wind -- 4.5.1 Determination of the Reference Controls -- 4.5.2 Accessibility and Controllability -- 4.5.3 Motion Planning when wind can be neglected -- 4.5.4 Determination of the Minimum Energy Trajectories -- 4.5.5 Determination of Time Optimal Trajectories -- 4.6 Parametric Curves -- 4.6.1 Cartesian polynomials -- 4.6.2 Trim Flight Paths -- 4.6.3 Non Trim Flight Paths -- 4.6.4 Maneuvers between two different trims -- 4.6.5 Frenet -Serret Approach -- 4.6.6 Pythagorean Hodograph -- 4.6.7 h3 Splines -- 4.7 Conclusions -- 5 Control -- 5.1 Introduction -- 5.2 Linear Control -- 5.2.1 Linear Formulation in Cruising flight -- 5.2.2 Flying and Handling Qualities -- 5.2.3 Classical Linear Control -- 5.2.4 Linear Robust Control -- 5.3 Nonlinear Control -- 5.3.1 Dynamic Inversion -- 5.3.2 Trajectory Tracking in a High Constant Altitude Flight -- 5.3.3 Variable Structure Robust Control -- 5.3.4 Back stepping controller design -- 5.3.5 Line tracking by path curvature and torsion -- 5.3.6 Intelligent Control -- 5.4 System Health Management -- 5.4.1 Health Monitoring -- 5.4.2 Diagnosis, Response to systems failure -- 5.5 Conclusions -- 6 General Conclusions -- 7 References -- References -- A Current Projects -- A.1 Introduction -- A.2 Artic Airship -- A.2.1 Vehicle Description -- A.2.2 Weight, mass distribution and balance -- A.2.3 Modeling and identification -- A.2.4 Aerodynamics -- A.2.5 Localization and positioning -- A.2.6 Navigation and Path Planner -- A.2.7 Feeding the path planner with realistic wind information -- A.2.8 Data processing and transmission -- A.2.9 Airship Piloting and Response to wind disturbances -- A.2.10 Loading and unloading lifts -- A.2.11 Diagnosis, Response to systems failure -- A.2.12 Flight dynamics simulator -- A.2.13 Small scale delta-wing quad-rotor airship -- A.2.14 Ground handling -- A.3 Bridge Monitoring -- A.4 Monitoring of high voltage power networks -- A.4.1 Current market for inspection of electrical networks -- A.4.2 Project Goals -- A.5 FAA Recommendations -- A.6 Indoor Lighter Than Air Robot : A Differential Geometry Modeling Approach -- Index. | |
| 520 | |a 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 unmanned airship with sufficient autonomy. Lighter than air systems are particularly appealing since the energy to keep them airborne is small. They are increasingly considered for various tasks such as monitoring, surveillance, advertising, freight carrier, transportation. This book familiarizes readers with a hierarchical decoupled planning and control strategy that has been proven efficient through research. It is made up of a hierarchy of modules with well defined functions operating at a variety of rates, linked together from top to bottom. The outer loop, closed periodically, consists of a discrete search that produces a set of waypoints leading to the goal while avoiding obstacles and weighed regions. The second level smoothes this set so that the generated paths are feasible given the vehicle's velocity and accelerations limits. The third level generates flyable, timed trajectories and the last one is the tracking controller that attempts to minimize the error between the robot measured trajectory and the reference trajectory. This hierarchy is reflected in the structure and content of the book. Topics treated are: Modelling, Flight Planning, Trajectory Design and Control. Finally, some actual projects are described in the appendix. This volume will prove useful for researchers and practitioners working in Robotics and Automation, Aerospace Technology, Control and Artificial Intelligence. | ||
| 650 | 0 | |a Control engineering. | |
| 650 | 0 | |a Robotics. | |
| 650 | 0 | |a Automation. | |
| 650 | 0 | |a Computer networks . | |
| 650 | 0 | |a Aerospace engineering. | |
| 650 | 0 | |a Astronautics. | |
| 650 | 1 | 4 | |a Control, Robotics, Automation. |
| 650 | 2 | 4 | |a Computer Communication Networks. |
| 650 | 2 | 4 | |a Aerospace Technology and Astronautics. |
| 710 | 2 | |a SpringerLink (Online service) | |
| 773 | 0 | |t Springer Nature eBook | |
| 776 | 0 | 8 | |i Printed edition: |z 9789400726628 |
| 776 | 0 | 8 | |i Printed edition: |z 9789400797567 |
| 776 | 0 | 8 | |i Printed edition: |z 9789400726642 |
| 830 | 0 | |a Intelligent Systems, Control and Automation: Science and Engineering, |x 2213-8994 ; |v 58 | |
| 856 | 4 | 0 | |u https://doi.uam.elogim.com/10.1007/978-94-007-2663-5 |z Texto Completo |
| 912 | |a ZDB-2-ENG | ||
| 912 | |a ZDB-2-SXE | ||
| 950 | |a Engineering (SpringerNature-11647) | ||
| 950 | |a Engineering (R0) (SpringerNature-43712) | ||