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Model predictive motion control based on generalized dynamical movement primitives
Örebro University, School of Science and Technology. (AASS)
INRIA St Ismier, Rhône-Alpes, France .
2014 (English)In: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 77, no 1, p. 17-35Article in journal (Refereed) Published
Abstract [en]

In this work, experimental data is used toestimate the free parameters of dynamical systemsintended to model motion profiles for a robotic system.The corresponding regression problem is formedas a constrained non-linear least squares problem.In our method, motions are generated via embeddedoptimization by combining dynamical movementprimitives in a locally optimal way at each time step.Based on this concept, we introduce a model predictivecontrol scheme which allows generalization overmultiple encoded behaviors depending on the currentposition in the state space, while leveraging the abilityto explicitly account for state constraints to the fulfillmentof additional tasks such as obstacle avoidance.We present a numerical evaluation of our approachand a preliminary verification by generating graspingmotions for the anthropomorphic Shadow Robothand/arm platform.

Place, publisher, year, edition, pages
Springer, 2014. Vol. 77, no 1, p. 17-35
Keywords [en]
Motion Control, Model Predictive Control, Motion Planning, Imitation Learning, Grasping
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-39630DOI: 10.1007/s10846-014-0100-3ISI: 000346172800004Scopus ID: 2-s2.0-84916891980OAI: oai:DiVA.org:oru-39630DiVA, id: diva2:771327
Note

Funding Agencies:

HANDLE - European Community ICT-231640

ROBLOG - European Community ICT-270350

Available from: 2014-12-12 Created: 2014-12-12 Last updated: 2018-01-11Bibliographically approved

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Krug, Robert

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