  Sampling based probabilistic roadmap planners (PRM) have been successful in
motion planning of robots with higher degrees of freedom, but may fail to
capture the connectivity of the configuration space in scenarios with a
critical narrow passage. In this paper, we show a novel technique based on Levy
Flights to generate key samples in the narrow regions of configuration space,
which, when combined with a PRM, improves the completeness of the planner. The
technique substantially improves sample quality at the expense of a minimal
additional computation, when compared with pure random walk based methods,
however, still outperforms state of the art random bridge building method, in
terms of number of collision calls, computational overhead and sample quality.
The method is robust to the changes in the parameters related to the structure
of the narrow passage, thus giving an additional generality. A number of 2D &
3D motion planning simulations are presented which shows the effectiveness of
the method.
