Nonlinear Kalman Filtering for Force-Controlled Robot Tasks

AvTine Lefebvre,Herman Bruyninckx

Inbunden, Engelska, 2005

Del i serien Springer Tracts in Advanced Robotics

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Häftad

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Beskrivning

This monograph focuses on how to achieve more robot autonomy by means of reliable processing skills. "Nonlinear Kalman Filtering for Force-Controlled Robot Tasks " discusses the latest developments in the areas of contact modeling, nonlinear parameter estimation and task plan optimization for improved estimation accuracy. Kalman filtering techniques are applied to identify the contact state based on force sensing between a grasped object and the environment. The potential of this work is to be found not only for industrial robot operation in space, sub-sea or nuclear scenarios, but also for service robots operating in unstructured environments co-habited by humans where autonomous compliant tasks require active sensing.

Produktinformation

Utgivningsdatum:2005-09-20
Mått:155 x 235 x undefined mm
Format:Inbunden
Språk:Engelska
Serie:Springer Tracts in Advanced Robotics
Antal sidor:266
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783540280231

Utforska kategorier

Elektronik och kommunikationer inom Naturvetenskap och teknik

Recensioner i media

From the reviews: "The present book provides the latest developments in the area of contact modeling, nonlinear parameter estimation and parameter estimation and parameter estimation improvement accuracy of robots. ... The book includes an up-to-date bibliography and a lot of numerical examples. The Appendices provide various theoretical complements which are of high interest for researchers working in robotics. The graphics is excellent and the final list of symbols and abbreviations are very useful. The book is an excellent contribution in the area of robotics ... ." (Dumitru Stanomir, Zentralblatt MATH, Vol. 1120 (22), 2007)

Innehållsförteckning

Introduction.- Literature Survey: Autonomous Compliant Motion.- Literature Survey: Bayesian Probability Theory.- Kalman Filters for Nonlinear Systems.- The Non-Minimal State Kalman Filter.- Contact Modelling.- Geometrical Parameter Estimation and CF Recognition.- Experiment: A Cube-In-Corner Assembly.- Task Planning with Active Sensing.- General Conclusions.