Alternative formulations for optimization-based human gait planning

Abstract

Simulating human motion is a complex problem due to redundancy of the human musculoskeletal system. The concept of task-based dynamic motion prediction using single-or multi-objective optimization techniques provides a viable approach for predicting dynamic gait motions of digital humans, subjected to basic physical and kinematical constraints. The task-based motion prediction is in fact a numerical optimal control problem. Alternative formulations for simulation of human gait motion are possible and can be solved by modern nonlinear optimization methods. Different ways to discretize the equations of motion are presented, namely finite difference, and Hermite and B-spline interpolations. The advantages and disadvantages of different formulations are discussed. Since the human gait simulation utilizes gradient-based optimization techniques, analytical gradients of objective and constraint functions are provided. A skeletal model for the lower body having 18 degrees of freedom is used to demonstrate the formulations, and is solved by a large-scale sparse nonlinear programming solver.