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Kinematic modeling of a RHex-type robot using a neural network
Author(s): Mario Harper; James Pace; Nikhil Gupta; Camilo Ordonez; Emmanuel G. Collins
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Paper Abstract

Motion planning for legged machines such as RHex-type robots is far less developed than motion planning for wheeled vehicles. One of the main reasons for this is the lack of kinematic and dynamic models for such platforms. Physics based models are difficult to develop for legged robots due to the difficulty of modeling the robot-terrain interaction and their overall complexity. This paper presents a data driven approach in developing a kinematic model for the X-RHex Lite (XRL) platform. The methodology utilizes a feed-forward neural network to relate gait parameters to vehicle velocities.

Paper Details

Date Published: 5 May 2017
PDF: 9 pages
Proc. SPIE 10195, Unmanned Systems Technology XIX, 1019507 (5 May 2017); doi: 10.1117/12.2262894
Show Author Affiliations
Mario Harper, Florida State Univ. (United States)
James Pace, Florida State Univ. (United States)
Nikhil Gupta, Florida State Univ. (United States)
Camilo Ordonez, Florida State Univ. (United States)
Emmanuel G. Collins, Florida State Univ. (United States)


Published in SPIE Proceedings Vol. 10195:
Unmanned Systems Technology XIX
Robert E. Karlsen; Douglas W. Gage; Charles M. Shoemaker; Hoa G. Nguyen, Editor(s)

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