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Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]

Yim, Mark, Shen, Wei-min, Salemi, Behnam, Rus, Daniela, Moll, Mark, Lipson, Hod, Klavins, Eric, Chirikjian, Gregory S.·2007·IEEE Robotics & Automation Magazine

Abstract

The field of modular self-reconfigurable robotic systems addresses the design, fabrication, motion planning, and control of autonomous kinematic machines with variable morphology. Modular self-reconfigurable systems have the promise of making significant technological advances to the field of robotics in general. Their promise of high versatility, high value, and high robustness may lead to a radical change in automation. Currently, a number of researchers have been addressing many of the challenges. While some progress has been made, it is clear that many challenges still exist. By illustrating several of the outstanding issues as grand challenges that have been collaboratively written by a large number of researchers in this field, this article has shown several of the key directions for the future of this growing field

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Metadata

Authors
Yim, M., Shen, W.-M., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E., & Chirikjian, G. S.
Venue
IEEE Robotics & Automation Magazine
Published
2007-03
Volume
14
Issue
1
Pages
43-52
ISSN
  • 1070-9932 (ISSN-L)
  • 1558-223X
OpenAlex
W2149841830
Cited by
1,035
References
0
FWCI
92.08
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]. https://app.cochat.ai/papers/10.1109/mra.2007.339623
The paper
Yim, M., Shen, W.-M., Salemi, B., Rus, D., Moll, M., Lipson, H., Klavins, E., & Chirikjian, G. S. (2007). Modular Self-Reconfigurable Robot Systems [Grand Challenges of Robotics]. IEEE Robotics & Automation Magazine. https://doi.org/10.1109/mra.2007.339623

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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.