Offline and online trajectory generation with sequential physical constraints

This paper presents a method to generate motion from human motion for a humanoid robot with physical limits. The method focuses on representing constraints for angle, collision, velocity, and dynamic force as B-spline coefficients. The constraints can be applied for offline optimization and online filtering. For optimization, the objective function is responsible for mimicking human trainers, while constraints are used to transform motion within the limit of the capabilities of the humanoid robot. An iterative soft-constraint paradigm is used to enhance the quality of offline velocity and force constraints. To refine precision, in regions of high-frequency motion not adequately modeled by an initial splining, B-spline is extensible into a hierarchy so that optimization that meets global criteria can be performed locally. For filtering, consideration is given to online B-spline decomposition and how to adapt the constraint functions as filters. The proposed method will be shown to outperform existing methods for space-time tasks with physical limits.