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Model predictive motion control based on generalized dynamical movement primitives
Örebro universitet, Institutionen för naturvetenskap och teknik. (AASS)
INRIA St Ismier, Rhône-Alpes, France .
2014 (engelsk)Inngår i: Journal of Intelligent and Robotic Systems, ISSN 0921-0296, E-ISSN 1573-0409, Vol. 77, nr 1, s. 17-35Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Springer, 2014. Vol. 77, nr 1, s. 17-35
Emneord [en]
Motion Control, Model Predictive Control, Motion Planning, Imitation Learning, Grasping
HSV kategori
Forskningsprogram
Datavetenskap
Identifikatorer
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
Merknad

Funding Agencies:

HANDLE - European Community ICT-231640

ROBLOG - European Community ICT-270350

Tilgjengelig fra: 2014-12-12 Laget: 2014-12-12 Sist oppdatert: 2018-01-11bibliografisk kontrollert

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Krug, Robert

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