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

•A tree-extending algorithm for reducing the solution cost is presented.•Generating nodes essential to the optimal path with smaller samples.•Searching for ancestors of the nearest vertex instead of nodes around the random point.•Connecting the random point to the furthest vertex of the tree it can...

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Vydáno v:Expert systems with applications Ročník 184; s. 115457
Hlavní autoři: Liao, Bin, Wan, Fangyi, Hua, Yi, Ma, Ruirui, Zhu, Shenrui, Qing, Xinlin
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.12.2021
Elsevier BV
Témata:
Algorithms
Apexes
Convergence
Motion planning
Optimal path planning
Optimization
Path planning
Rapidly-exploring random tree (RRT)
Sampling
Sampling-based algorithms
Path planning
Rapidly-exploring random tree (RRT)
Optimal path planning
Sampling-based algorithms
ISSN:0957-4174, 1873-6793
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Shrnutí:•A tree-extending algorithm for reducing the solution cost is presented.•Generating nodes essential to the optimal path with smaller samples.•Searching for ancestors of the nearest vertex instead of nodes around the random point.•Connecting the random point to the furthest vertex of the tree it can reach. 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.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2021.115457