Data driven models of legged locomotion

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https://www.spiedigitallibrary.org/conference-proceedings-of-spie/9467/94671V/Data-driven-models-of-legged-locomotion/10.1117/12.2178007.full

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https://doi.org/10.1117/12.2178007
 http://hdl.handle.net/11603/37052

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UMBC Mathematics and Statistics Department

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https://orcid.org/0000-0002-2662-6760

Date

2015-05-22

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conference papers and proceedings
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Citation of Original Publication

Revzen, Shai, and Matthew Kvalheim. “Data Driven Models of Legged Locomotion.” In Micro- and Nanotechnology Sensors, Systems, and Applications VII, 9467:315–22. SPIE, 2015. https://doi.org/10.1117/12.2178007.

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©(2015) Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

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Abstract

Legged locomotion is a challenging regime both for experimental analysis and for robot design. From biology, we know that legged animals can perform spectacular feats which our machines can only surpass on some specially controlled surfaces such as roads. We present a concise review of the theoretical underpinnings of Data Driven Floquet Analysis (DDFA), an approach for empirical modeling of rhythmic dynamical systems. We provide a review of recent and classical results which justify its use in the analysis of legged systems.