Generation of humanoid robot motions with physical constraints using hierarchical B-spline

From recorded human motion, trajectory optimization of a humanoid robot with physical limits being the key constraint is presented. The optimization's objective function preserves the salient characteristics of the original motion, while constraints are used to transform the motion to the limit of the capabilities of the humanoid robot. It is shown that using constraints to limit generated trajectories to physically realizable motion ensures that limits are being met more precisely than by reducing them using a standard objective function. The use of wavelets vs. B-spline is compared, and it is shown that B-spline data representation has pronounced advantages. Furthermore, in regions of high-frequency motion not adequately modeled by an initial splining, B-spline is extensible into a hierarchy so that optimizations can be performed locally which still meet global criteria. It is shown how to use B-spline coefficients in angle-, velocity-, acceleration-, and dynamic force-constraints. To detect trajectory subsections with excessive error in need of control point adjustment and local re-optimization, not only is a traditional error detector used, but also a B-spline density detector is also presented. Generated motions were tested using our simulation program and the robot HRP-2.