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the rapidly exploring random trees (RRTs) have generated a highly successful single query planner which solved difficult problems in many applications of motion planning in recent years. Even though RRT works well on many problems, they have weaknesses in environments that handle complicated geometries. Sampling narrow passages in a robot’s free configuration space remains a challenge for RRT planners indeed; the geometry of a narrow passage affects significantly the exploration property of the RRT when the sampling domain is not well adapted for the problem. In this paper we characterize the weaknesses of the RRT planners and propose a general framework to improve their behaviours in difficult environments. We simulate and test our new planner on mobile robots in many difficult static environments which are completely known, simulations show significant improvements over existing RRT based planner to reliably capture the narrow passages areas in the configuration space.

A NEW PROBABILISTIC PATH PLANNER - For Mobile Robots Comparison with the Basic RRT Planner