A new hybrid sampling strategy for PRM planners - To address narrow passages problem

The probabilistic path planner (PPP) is a general planning scheme that yields fast robot path planners for a wide variety of problems, involving high degree of freedom articulated robots, non holonomic robots, and multiple robots. This paper presents a new probabilistic approach for finding paths through narrow passages. Our probabilistic planner follows the general framework of probabilistic roadmap (PRM), but to increase sample density in difficult areas like narrow passages, we define two sampling constraints in order to get much more points than a classic PRM gets in such areas. We simulate our planner in 2D environments and the simulations results shows good performance for our planner.