Buch, Englisch, 266 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 435 g
Buch, Englisch, 266 Seiten, Previously published in hardcover, Format (B × H): 155 mm x 235 mm, Gewicht: 435 g
Reihe: Springer Tracts in Advanced Robotics
ISBN: 978-3-642-06629-0
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Technische Wissenschaften Elektronik | Nachrichtentechnik Elektronik Robotik
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Fertigungstechnik, Automatisierung
- Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik Regelungstechnik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik Zuverlässigkeitstechnik
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
Weitere Infos & Material
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.
