2022
DOI: 10.1007/s10514-022-10044-x
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AEB-RRT*: an adaptive extension bidirectional RRT* algorithm

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Cited by 20 publications
(13 citation statements)
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“…Gammell et al [5] Informed RRT algorithm Jr et al [6] Kang et al [17] RRT-Connect algorithm Karaman et al [7] RRT-Connect algorithm Wang et al [16] NRRT* algorithm Wu et al [12] Fast-RRT algorithm Wang et al [13] Adaptive extended bidirectional RRT* algorithm Jeong et al [14] Quick-RRT* algorithm Mashayekhi et al [15] Hybrid RRT algorithm Gan et al [18] Bg-RRT algorithm Song et al [19] Bi-RRT algorithm Luan et al [20] Dynamic variable sampling area RRT algorithm Zhou et al [22] Bidirectional RRT* algorithm Swarm intelligence based algorithm Xu et al [8] Genetic algorithm Yu et al [9] Particle swarm algorithm Dorigo et al [10] Wang et al [26] Zhang et al [28] Li et al [31] Yang et al [32] Li et al [36] Li et al [38] Ant colony algorithm Wang et al [11] Artificial fish swarm algorithm Chen et al [25] PNACO algorithm Li et al [27] Jump evolutionary ant colony optimization algorithm Shi et al [29] Genetic ant colony…”
Section: Rrt Algorithmmentioning
confidence: 99%
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“…Gammell et al [5] Informed RRT algorithm Jr et al [6] Kang et al [17] RRT-Connect algorithm Karaman et al [7] RRT-Connect algorithm Wang et al [16] NRRT* algorithm Wu et al [12] Fast-RRT algorithm Wang et al [13] Adaptive extended bidirectional RRT* algorithm Jeong et al [14] Quick-RRT* algorithm Mashayekhi et al [15] Hybrid RRT algorithm Gan et al [18] Bg-RRT algorithm Song et al [19] Bi-RRT algorithm Luan et al [20] Dynamic variable sampling area RRT algorithm Zhou et al [22] Bidirectional RRT* algorithm Swarm intelligence based algorithm Xu et al [8] Genetic algorithm Yu et al [9] Particle swarm algorithm Dorigo et al [10] Wang et al [26] Zhang et al [28] Li et al [31] Yang et al [32] Li et al [36] Li et al [38] Ant colony algorithm Wang et al [11] Artificial fish swarm algorithm Chen et al [25] PNACO algorithm Li et al [27] Jump evolutionary ant colony optimization algorithm Shi et al [29] Genetic ant colony…”
Section: Rrt Algorithmmentioning
confidence: 99%
“…Wu et al [12] proposed a Fast-RRT algorithm combined with the random steering strategy to improve the search performance in narrow aisle scenarios. Wang et al [13]proposed an adaptive extended bidirectional RRT* (AEB-RRT*) algorithm with high efficiency and good robustness for complex environments such as concave, convex, narrow aisle, maze, and multiple obstacles. Jeong I et al [14] proposed the Quick-RRT* algorithm, which has a better initial solution and a faster rate of convergence than the RRT* algorithm.…”
Section: Introductionmentioning
confidence: 99%
“…The experimental results show that the improved RRT algorithm can eliminate the redundant bifurcations of the growing tree, reduce the number of sampling times, and greatly improve the growth efficiency compared with the traditional RRT, RRT* and B-RRT*. Other application scenarios for B-RRT or B-RRT* include ice navigation [ 51 ], arc welding robot [ 52 ], the autonomous flight of UAV [ 53 ], robot path planning in constrained environment [ 48 , 54 ], redundant manipulators [ 8 , 55 ], autonomous parking [ 56 ], lunar rover [ 57 ], litchi-picking robot [ 58 ].…”
Section: Overview Of Rrt-based Algorithm Improvementsmentioning
confidence: 99%
“…Sampling-based planning methods [18][19][20][21][22] are preferred for solving obstacle constraints in high-dimensional configuration spaces due to their adaptability. However, when faced with multiple task constraints, sampling planning can present challenges.…”
Section: Introductionmentioning
confidence: 99%