On-line real-time physics-based predictive motion control with balance recovery
In this paper, we present an on‐line real‐time physics‐based approach to motion control with contact repositioning based on a low‐dimensional dynamics model using example motion data. Our approach first generates a reference motion in run time according to an on‐line user request by transforming an...
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Published in | Computer graphics forum Vol. 33; no. 2; pp. 245 - 254 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Blackwell Publishing Ltd
01.05.2014
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Subjects | |
Online Access | Get full text |
ISSN | 0167-7055 1467-8659 |
DOI | 10.1111/cgf.12323 |
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Summary: | In this paper, we present an on‐line real‐time physics‐based approach to motion control with contact repositioning based on a low‐dimensional dynamics model using example motion data. Our approach first generates a reference motion in run time according to an on‐line user request by transforming an example motion extracted from a motion library. Guided by the reference motion, it repeatedly generates an optimal control policy for a small time window one at a time for a sequence of partially overlapping windows, each covering a couple of footsteps of the reference motion, which supports an on‐line performance. On top of this, our system dynamics and problem formulation allow to derive closed‐form derivative functions by exploiting the low‐dimensional dynamics model together with example motion data. These derivative functions and their sparse structures facilitate a real‐time performance. Our approach also allows contact foot repositioning so as to robustly respond to an external perturbation or an environmental change as well as to perform locomotion tasks such as stepping on stones effectively. |
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Bibliography: | istex:11DFDDC024466A81A43F9C5880BCA6A36B68126C ArticleID:CGF12323 ark:/67375/WNG-WX165Z4F-C Supporting Information SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 |
ISSN: | 0167-7055 1467-8659 |
DOI: | 10.1111/cgf.12323 |