Probabilistic roadmap approaches (PRMs) have been successfully applied in motion planning of robots with many degrees of freedom. Narrow passages create significant difficulty for these planners. We do not propose a new sampling strategy; our main contribution is to replace random sampling with deterministic sampling. This work can be viewed as a complementary study to importance sampling. Our experimental results show that the deterministic variants of the PRM offer performance advantages in comparison to the original PRM.