Triangular Geometrized Sampling Heuristics for Fast Optimal Motion Planning

摘要: Rapidly-exploring Random Tree (RRT)-based algorithms have become increasingly popular due to their lower computational complexity as compared with other path planning algorithms. The recently presented RRT* motion algorithm improves upon the original RRT by providing optimal solutions. While determines an initial collision-free fairly quickly, guarantees almost certain convergence optimal, obstacle-free from start goal points for any given geometrical environment. However, main limitations of include its slow processing rate and high memory consumption, large number iterations required calculating path. In order overcome these limitations, we present another improvement, i.e, Triangular Geometerized-RRT* (TG-RRT*) algorithm, which utilizes triangular methods improve performance in terms time a decreased an...