Deterministic sampling methods for spheres and SO(3)

This paper addresses the problem of generating uniform deterministic samples over the spheres and the three-dimensional rotation group, SO(3). The target applications include motion planning, optimization, and verification problems in robotics and in related areas, such as graphics, control theory and computational biology. We introduce an infinite sequence of samples that is shown to achieve: 1) low-dispersion, which aids in the development of resolution complete algorithms, 2) lattice structure, which allows easy neighbor identification that is comparable to what is obtained for a grid in R^d, and 3) incremental quality, which is similar to that obtained by random sampling. The sequence is demonstrated in a sampling-based motion planning algorithm.