Gait Generation Through a Feature Based Linear Periodic Function
By considering locomotion as a set of coordinated oscillations, a method for generating a wide variety of periodic linear gait
trajectories is proposed. The shape of the generated trajectory can be defined as a set of features such as symmetry, skewness,
signal width, duality and squareness, along with amplitude, offset, phase and frequency parameters. Taking previously proven
nonlinear bipedal gait trajectories as reference, a set of linear approximates are modeled, and is tested on a simulated humanoid
robot. Then, gait trajectories for producing stable and faster bipedal gait on the same humanoid robot are learned using Genetic
Algorithm, through a bottom-up approach.
A. Ranganath and L. Moren, “Gait generation through a feature based linear periodic function”, in Mediterranean
Conference on Control and Automation (MED), Torremolinos, Spain, Jun 2015.