F-RRT*: An improved path planning algorithm with improved initial solution and convergence rate

Abstract

During the last decades, sampling-based algorithms have been used to solve the problem of motion planning. RRT*, as an optimal variant of RRT, provides asymptotic optimality. However, the slow convergence rate and costly initial solution make it inefficient. To overcome these limitations, this paper proposes a modified RRT* algorithm, F-RRT*, which generates a better initial solution and converges faster than RRT*. F-RRT* optimizes the cost of paths by creating a parent node for the random point, instead of selecting it among the existing vertices. The creation process can be divided into two steps, the FindReachest, and CreatNode procedures, which require few calculations, and the triangle inequality is used repeatedly throughout the process, thus, resulting in paths with higher performance than those of RRT*. Since the algorithm proposed in this paper is a tree extending algorithm, its performance can be further enhanced when combined with other sampling strategies. The advantages of the proposed algorithm in the initial solution and fast convergence rate are demonstrated by comparing with RRT*, RRT*-Smart, and Q-RRT* through numerical simulations in this paper.