Density Avoided Sampling: An Intelligent Sampling Technique for Rapidly-Exploring Random Trees

This paper proposes a new sampling procedure for rapidly-exploring random trees (RRT). In traditional path planning methods, sampling procedure is carried out by neglecting the configuration of environment. Hence useless samples tend to be generated; which will result in a waste of computational resources without any considerable improvement in the results. The sampling method proposed in this paper is based on the way that branches of plants grow in limited spaces. This method yields impressive improvements in the uniformity of expansion and the speed of space exploration for path planning queries in highly constrained environments wherein the visible Voronoi regions are largely affected by dense obstacles.