Data driven models of legged locomotion

Shai Revzen; Matthew Kvalheim

doi:10.1117/12.2178007

22 May 2015 Data driven models of legged locomotion

Shai Revzen, Matthew Kvalheim

Proceedings Volume 9467, Micro- and Nanotechnology Sensors, Systems, and Applications VII; 94671V (2015) https://doi.org/10.1117/12.2178007
Event: SPIE Defense + Security, 2015, Baltimore, MD, United States

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.

Citation Download Citation

Shai Revzen and Matthew Kvalheim "Data driven models of legged locomotion", Proc. SPIE 9467, Micro- and Nanotechnology Sensors, Systems, and Applications VII, 94671V (22 May 2015); https://doi.org/10.1117/12.2178007

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