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.
Mario Harper, James Pace, Nikhil Gupta, Camilo Ordonez, and Emmanuel G. Collins, "Kinematic modeling of a RHex-type robot using a neural network," Proc. SPIE 10195, Unmanned Systems Technology XIX, 1019507 (Presented at SPIE Defense + Security: April 11, 2017; Published: 5 May 2017); https://doi.org/10.1117/12.2262894.
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