Convex Optimization Algorithms for Active Balancing of Humanoid Robots

We show that a large class of active balancing problems for legged robots can be framed as a second-order cone programming (SOCP) problem, a convex optimization problem for which efficient and numerically robust algorithms exist. We describe this general SOCP balancing framework, show that several e...

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Veröffentlicht in:IEEE transactions on robotics Jg. 23; H. 4; S. 817 - 822
Hauptverfasser: Juyong Park, Jaeyoung Haan, Park, F.C.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.08.2007
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Acceleration
Accelerometers
Active balancing
Algorithms
Applied sciences
Automation
Computer science; control theory; systems
Computer simulation
Control theory. Systems
convex optimization
Electrical engineering
Error correction
Exact sciences and technology
Feedback control
Humanoid
Humanoid robots
Intelligent robots
Leg
Legged locomotion
legged robot
Mathematical models
Optimization
Programming
Robot programming
Robotics
Robots
Robustness
second-ordercone programming (SOCP)
Strategy
Walking
Balancing
Moving robot
Modeling
Convex programming
Optimization
Robotics
Humanoid robot
ISSN:1552-3098, 1941-0468
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Beschreibung
Zusammenfassung:We show that a large class of active balancing problems for legged robots can be framed as a second-order cone programming (SOCP) problem, a convex optimization problem for which efficient and numerically robust algorithms exist. We describe this general SOCP balancing framework, show that several existing optimization-based balancing strategies reduce to special cases of this more general formulation, and investigate the computational performance of our SOCP algorithms through simulation studies involving a humanoid model.
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ISSN:1552-3098
1941-0468
DOI:10.1109/TRO.2007.900639