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The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.

A general framework for managing multiple tasks in highly redundant robotic systems

Siciliano, B., Slotine, J.-J.E.·1991·Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments

Abstract

The exploitation of kinematic redundancies in robotic systems may provide more dexterity and versatility in the execution of complex tasks. When functional constraint tasks are imposed in addition to the end-effector tasks, a task priority strategy is advisable. The authors propose a general framework for managing multiple tasks in highly redundant systems. In particular, they derive joint velocity and acceleration solutions which can be used as reference input trajectories to suitable model-based controllers. They also develop a recursive implementation, and discuss the occurrence of singularities in the Jacobian associated with the generic task. Two case studies illustrate the effectiveness of the algorithm on a snake-like robot.>

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Metadata

Authors
Siciliano, B., & Slotine, J.-J. E.
Venue
Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments
Publisher
IEEE
Published
1991
Pages
1211-1216 vol.2
OpenAlex
W2072763216
Cited by
608
References
16
FWCI
1.00
Type
conference paper
Language
en
Open access
closed

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CoChat. (2026). Citation check: A general framework for managing multiple tasks in highly redundant robotic systems. https://app.cochat.ai/papers/10.1109/icar.1991.240390
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Siciliano, B., & Slotine, J.-J. E. (1991). A general framework for managing multiple tasks in highly redundant robotic systems. Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments. https://doi.org/10.1109/icar.1991.240390

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.