Trajectory Optimisation via Reinforcement Learning: Safe Autonomous Rendezvous, Proximity Operation, and Docking for under-actuated CubeSat via Reinforcement Learning - Couverture souple

Paris, Matthieu

9786204718385: Trajectory Optimisation via Reinforcement Learning: Safe Autonomous Rendezvous, Proximity Operation, and Docking for under-actuated CubeSat via Reinforcement Learning

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ISBN 10 : 620471838X ISBN 13 : 9786204718385

Editeur : LAP LAMBERT Academic Publishing, 2021

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From the emergence of commercial applications such as on-demand imagery and global internet service to the necessity of satellite servicing and active space debris removal, the level of complexity in mission design has skyrocketed. All these different applications have directed the evolution of the technology toward the need for autonomous spacecraft that can operate independently of human control. As such, artificial intelligence has rapidly emerged as being a promising field allowing greater robotic autonomy and innovative decision making. While new autonomous techniques have enabled faster and larger numbers of spacecraft operations, there is still a valid concern for the safety of the missions during proximity manoeuvres.This Master's thesis investigates the use of the Reinforcement Learning algorithm Proximal Policy Optimization for achieving a planar Autonomous Rendezvous, Proximity Operation, and Docking manoeuvre with an under-actuated CubeSat. Together with the safety considerations, the different control objectives throughout the three phases reflect the complexity necessary for safe and efficient operations.

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2021
Langue: anglais
ISBN 10: 620471838X
ISBN 13: 9786204718385
Reliure: Broch�
Nombre de pages: 80
Coordonn�es du fabricant: non disponible
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Trajectory Optimisation via Reinforcement Learning

Matthieu Paris

Edit� par LAP LAMBERT Academic Publishing Nov 2021, 2021

ISBN 10 : 620471838X ISBN 13 : 9786204718385

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -From the emergence of commercial applications such as on-demand imagery and global internet service to the necessity of satellite servicing and active space debris removal, the level of complexity in mission design has skyrocketed. All these different applications have directed the evolution of the technology toward the need for autonomous spacecraft that can operate independently of human control. As such, artificial intelligence has rapidly emerged as being a promising field allowing greater robotic autonomy and innovative decision making. While new autonomous techniques have enabled faster and larger numbers of spacecraft operations, there is still a valid concern for the safety of the missions during proximity manoeuvres.This Master's thesis investigates the use of the Reinforcement Learning algorithm Proximal Policy Optimization for achieving a planar Autonomous Rendezvous, Proximity Operation, and Docking manoeuvre with an under-actuated CubeSat. Together with the safety considerations, the different control objectives throughout the three phases reflect the complexity necessary for safe and efficient operations. 80 pp. Englisch. N� de r�f. du vendeur 9786204718385

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Trajectory Optimisation via Reinforcement Learning

Matthieu Paris

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ISBN 10 : 620471838X ISBN 13 : 9786204718385

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Paris MatthieuMatthieu Paris is a French engineer graduated from Ecole Centrale de Nantes (France) and Politecnico di Milano (Italy). He built a strong engineering culture through his academic and professional experiences. This thesi. N� de r�f. du vendeur 535629517

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Trajectory Optimisation via Reinforcement Learning

Matthieu Paris

Edit� par LAP LAMBERT Academic Publishing Nov 2021, 2021

ISBN 10 : 620471838X ISBN 13 : 9786204718385

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -From the emergence of commercial applications such as on-demand imagery and global internet service to the necessity of satellite servicing and active space debris removal, the level of complexity in mission design has skyrocketed. All these different applications have directed the evolution of the technology toward the need for autonomous spacecraft that can operate independently of human control. As such, artificial intelligence has rapidly emerged as being a promising field allowing greater robotic autonomy and innovative decision making. While new autonomous techniques have enabled faster and larger numbers of spacecraft operations, there is still a valid concern for the safety of the missions during proximity manoeuvres.This Master's thesis investigates the use of the Reinforcement Learning algorithm Proximal Policy Optimization for achieving a planar Autonomous Rendezvous, Proximity Operation, and Docking manoeuvre with an under-actuated CubeSat. Together with the safety considerations, the different control objectives throughout the three phases reflect the complexity necessary for safe and efficient operations.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 80 pp. Englisch. N� de r�f. du vendeur 9786204718385

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Trajectory Optimisation via Reinforcement Learning : Safe Autonomous Rendezvous, Proximity Operation, and Docking for under-actuated CubeSat via Reinforcement Learning

Matthieu Paris

Edit� par LAP LAMBERT Academic Publishing, 2021

ISBN 10 : 620471838X ISBN 13 : 9786204718385

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - From the emergence of commercial applications such as on-demand imagery and global internet service to the necessity of satellite servicing and active space debris removal, the level of complexity in mission design has skyrocketed. All these different applications have directed the evolution of the technology toward the need for autonomous spacecraft that can operate independently of human control. As such, artificial intelligence has rapidly emerged as being a promising field allowing greater robotic autonomy and innovative decision making. While new autonomous techniques have enabled faster and larger numbers of spacecraft operations, there is still a valid concern for the safety of the missions during proximity manoeuvres.This Master's thesis investigates the use of the Reinforcement Learning algorithm Proximal Policy Optimization for achieving a planar Autonomous Rendezvous, Proximity Operation, and Docking manoeuvre with an under-actuated CubeSat. Together with the safety considerations, the different control objectives throughout the three phases reflect the complexity necessary for safe and efficient operations. N� de r�f. du vendeur 9786204718385

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Trajectory Optimisation via Reinforcement Learning | Safe Autonomous Rendezvous, Proximity Operation, and Docking for under-actuated CubeSat via Reinforcement Learning

Matthieu Paris

Edit� par LAP LAMBERT Academic Publishing, 2021

ISBN 10 : 620471838X ISBN 13 : 9786204718385

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Taschenbuch. Etat : Neu. Trajectory Optimisation via Reinforcement Learning | Safe Autonomous Rendezvous, Proximity Operation, and Docking for under-actuated CubeSat via Reinforcement Learning | Matthieu Paris | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204718385 | Verantwortliche Person f�r die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabr�ck, mail[at]preigu[dot]de | Anbieter: preigu. N� de r�f. du vendeur 120915005

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