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Reasoning with Uncertainty in Robotics - Leo Dorst; Michiel van Lambalgen; Frans Voorbraak
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Leo Dorst; Michiel van Lambalgen; Frans Voorbraak:

Reasoning with Uncertainty in Robotics - neues Buch

ISBN: 9783540685067

This book presents the refereed proceedings of the International Workshop on Reasoning with Uncertainty in Robotics, RUR'95, held in Amsterdam, The Netherlands, in December 1995. The book… Mehr…

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Reasoning with Uncertainty in Robotics - Leo Dorst; Michiel van Lambalgen; Frans Voorbraak
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Leo Dorst; Michiel van Lambalgen; Frans Voorbraak:

Reasoning with Uncertainty in Robotics - neues Buch

ISBN: 9783540685067

Engineering; Robotics and Automation; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Pattern Recognition Autonome Mobile Agenten, Autonomous Mobile Agents, Bay… Mehr…

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Reasoning with Uncertainty in Robotics - Leo Dorst; Michiel van Lambalgen; Frans Voorbraak
Bestellen
bei Springer.com
€ 59,49
Bestellen
Leo Dorst; Michiel van Lambalgen; Frans Voorbraak:
Reasoning with Uncertainty in Robotics - neues Buch

ISBN: 9783540685067

Engineering; Robotics and Automation; Artificial Intelligence (incl. Robotics); Control, Robotics, Mechatronics; Pattern Recognition Autonome Mobile Agenten, Autonomous Mobile Agents, Bay… Mehr…

  - Versandkosten:zzgl. Versandkosten.

Details zum Buch

Detailangaben zum Buch - Reasoning with Uncertainty in Robotics


EAN (ISBN-13): 9783540685067
Herausgeber: Springer Science+Business Media

Buch in der Datenbank seit 2017-02-17T19:21:59+01:00 (Berlin)
Detailseite zuletzt geändert am 2021-08-30T16:56:05+02:00 (Berlin)
ISBN/EAN: 9783540685067

ISBN - alternative Schreibweisen:
978-3-540-68506-7


Daten vom Verlag:

Autor/in: Leo Dorst; Michiel van Lambalgen; Frans Voorbraak
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Reasoning with Uncertainty in Robotics - International Workshop, RUR '95, Amsterdam, The Netherlands, December 4-6, 1995. Proceedings
Verlag: Springer; Springer Berlin
395 Seiten
Erscheinungsjahr: 2006-01-21
Berlin; Heidelberg; DE
Gedruckt / Hergestellt in Deutschland.
Sprache: Englisch
69,99 € (DE)
77,00 € (AT)
93,50 CHF (CH)
Available

EA; E107; eBook; Nonbooks, PBS / Technik/Maschinenbau, Fertigungstechnik; Robotik; Verstehen; Autonome Mobile Agenten; Autonomous Mobile Agents; Bayes-Entscheidungstheorie; Bayesian Decision Theory; Navigation; Processing; Robot Navigation; Roboternavigation; Sensorenbasierte Bewegungsplanung; Unsicheres Schließen; decision theory; image processing; mobile robot; object recognition; robot; C; Robotics and Automation; Artificial Intelligence; Control, Robotics, Mechatronics; Pattern Recognition; Control, Robotics, Automation; Artificial Intelligence; Automated Pattern Recognition; Computer Science; Künstliche Intelligenz; Regelungstechnik; Elektronische Geräte und Materialien; Mustererkennung; BC

Mathematical foundations of navigation and perception for an autonomous mobile robot.- Reasoning with uncertainty in AI.- Robot navigation: Integrating perception, environmental constraints and task execution within a probabilistic framework.- Uncertainty reasoning in object recognition by image processing.- Partially observable markov decision processes for artificial intelligence.- An evidential approach to probabilistic map-building.- Belief formation by constructing models.- Causal relevance.- The robot control strategy in a domain with dynamical obstacles.- Reasoning about noisy sensors (and effectors) in the situation calculus.- Recursive total least squares: An alternative to using the discrete kalman filter in robot navigation.- A sensor-based motion planner for mobile robot navigation with uncertainty.- Knowledge considerations in robotics.- Neural network applications in sensor fusion for an autonomous mobile robot.- Structuring uncertain knowledge with hierarchical bayesian networks.- Uncertainty treatment in a surface filling mobile robot.- Probabilistic map learning: Necessity and difficulties.- Robot navigation with markov models: A framework for path planning and learning with limited computational resources.- A refined method for occupancy grid interpretation.- Sensor planning with bayesian decision theory.- Perception-based self-localization using fuzzy locations.

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