A task-priority based framework for multiple tasks in highly redundant robots

Authors:
Jae Won Jeong;Pyung Hun Chang
Affiliations:
Korea Advanced Institute of Science and Technology, Deajeon, Republic of Korea;Korea Advanced Institute of Science and Technology, Deajeon, Republic of Korea
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

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Abstract

A task-priority based framework for multiple tasks of highly redundant robots was derived using the Lagrangian multiplier method. The framework was proved to prioritize a generic number of tasks without algorithmic problems - so called an algorithmic singularity and an algorithmic error. The computational efficiency of the framework excels other conventional task-priority strategies. The efficiency and efficacy of the framework was demonstrated theoretically and experimentally through comparative study.