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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| Veröffentlicht in: | Expert systems with applications Jg. 184; S. 115457 |
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| Format: | Journal Article |
| Sprache: | Englisch |
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Elsevier Ltd
01.12.2021
Elsevier BV |
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| ISSN: | 0957-4174, 1873-6793 |
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| Abstract | •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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| AbstractList | •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. 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. |
| ArticleNumber | 115457 |
| Author | Wan, Fangyi Qing, Xinlin Hua, Yi Zhu, Shenrui Liao, Bin Ma, Ruirui |
| Author_xml | – sequence: 1 givenname: Bin surname: Liao fullname: Liao, Bin email: liaobin@mail.nwpu.edu.cn organization: School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China – sequence: 2 givenname: Fangyi surname: Wan fullname: Wan, Fangyi email: fwan@nwpu.edu.cn organization: School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China – sequence: 3 givenname: Yi surname: Hua fullname: Hua, Yi email: yihua0826@163.com organization: School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China – sequence: 4 givenname: Ruirui surname: Ma fullname: Ma, Ruirui email: maruirui@mail.nwpu.edu.cn organization: School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China – sequence: 5 givenname: Shenrui surname: Zhu fullname: Zhu, Shenrui email: zsravarhm@163.com organization: School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China – sequence: 6 givenname: Xinlin surname: Qing fullname: Qing, Xinlin email: xinlinqing@xmu.edu.cn organization: School of Aeronautics, Northwestern Polytechnical University, Xi’an 710072, China |
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| Cites_doi | 10.1109/21.148404 10.1177/027836402320556458 10.1016/j.eswa.2019.01.032 10.1016/j.artint.2007.11.009 10.1016/j.eswa.2020.113425 10.1016/j.cad.2009.12.007 10.1109/TITS.2015.2498841 10.1109/TSSC.1968.300136 10.1016/j.artint.2003.12.001 10.1007/s10514-015-9518-0 10.1109/70.127236 10.1177/0278364911406761 |
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| Snippet | •A tree-extending algorithm for reducing the solution cost is presented.•Generating nodes essential to the optimal path with smaller samples.•Searching for... 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... |
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| SubjectTerms | Algorithms Apexes Convergence Motion planning Optimal path planning Optimization Path planning Rapidly-exploring random tree (RRT) Sampling Sampling-based algorithms |
| Title | F-RRT: An improved path planning algorithm with improved initial solution and convergence rate |
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