AEB-RRT*: an adaptive extension bidirectional RRT* algorithm

Wang, Xuewu; Wei, Jianbin; Zhou, Xin; Xia, Zelong; Gu, Xingsheng

doi:10.1007/s10514-022-10044-x

Cited by 20 publications

(13 citation statements)

References 35 publications

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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%

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Toward Optimization of AGV Path Planning: An RRT^*-ACO Algorithm

Wang,

Li,

Bai

et al. 2024

IEEE Access

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Automated guided vehicle (AGV) smart parking provides a new solution to solve the problem of urban parking difficulties. In the AGV parking lot, whether the AGV running path is reasonable affects the transportation efficiency of the entire parking lot. RRT algorithm and ant colony algorithm can achieve good path planning effect for AGV. However, the use of uniformly distributed initial pheromones can easily lead to blind search and local optimization in the early stage of the algorithm, resulting in a decrease in AGV efficiency. In this paper, an AGV based on the fast-scaling random treeant colony algorithm (RRT*-ACO) is proposed. The path planning method uses the fast search mechanism of RRT* algorithm and the positive feedback advantage of ant colony algorithm to combine the two algorithms and add them to the RRT* algorithm. The two-way search, heuristic dynamic sampling and dynamic step size strategies are used to accelerate the search speed of the RRT* algorithm, quickly generate an initial path, and then deploy different concentrations of initial pheromones according to the initial path to guide the ant colony algorithm search. Then, the silo excavation search strategy is added to the ant colony algorithm, which improves the pheromone update rule and reduces the number of iterations of the ant colony algorithm. Finally, several simulation experiments are carried out on the algorithm in maps of different scales, and the comparative experimental results show that the RRT*-ACO algorithm has fast convergence speed, better global search ability, and the path planning efficiency is significantly higher than that of traditional ant colony algorithm and ant colony system, and has better robustness in large-scale maps.

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Section: Rrt Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Toward Optimization of AGV Path Planning: An RRT^*-ACO Algorithm

Wang,

Li,

Bai

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…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%

Recent advances in Rapidly-exploring random tree: A review

2024

Heliyon

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“…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%

An Informed-Bi-Quick RRT* Algorithm Based on Offline Sampling: Motion Planning Considering Multiple Constraints for a Dual-Arm Cooperative System

Zhang,

Liu,

Qin

et al. 2024

Actuators

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Aiming to address problems such as low sampling success rate and long computation time in the motion planning of a dual-arm cooperative system with multiple constraints, this paper proposes an Informed-Bi-Quick RRT* algorithm based on offline sampling. First, in the process of pre-sampling, the new algorithm relaxes the approximation of constrained manifolds by introducing the idea of incremental construction, and it incorporates the stochastic gradient descent method to replace global random sampling with local random sampling, which enriches the data set and shortens the offline sampling time of the data set. Second, the new algorithm improves the original Quick-RRT* algorithm by combining the two-tree idea and the multi-target bias expansion strategy, and it improves the adaptability of the algorithm to different obstacle environments. In addition, the loosely constrained motion and tightly constrained motion in the two-arm cooperative system are analyzed, and the adaptive planning of the two-arm trajectory in different motions is described in detail. In this paper, the two-arm cooperative model constructed with UR5 and UR10 robot arms is studied, and the ability of the proposed algorithm to deal with multiple constraints is verified by simulating assembly and handling tasks. The experimental results show that compared with other methods, the proposed algorithm has obvious advantages in path quality and planning efficiency.

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AEB-RRT: an adaptive extension bidirectional RRT algorithm

Cited by 20 publications

References 35 publications

Toward Optimization of AGV Path Planning: An RRT^*-ACO Algorithm

Toward Optimization of AGV Path Planning: An RRT^*-ACO Algorithm

Recent advances in Rapidly-exploring random tree: A review

An Informed-Bi-Quick RRT* Algorithm Based on Offline Sampling: Motion Planning Considering Multiple Constraints for a Dual-Arm Cooperative System

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AEB-RRT*: an adaptive extension bidirectional RRT* algorithm

Cited by 20 publications

References 35 publications

Toward Optimization of AGV Path Planning: An RRT*-ACO Algorithm

Toward Optimization of AGV Path Planning: An RRT*-ACO Algorithm

Recent advances in Rapidly-exploring random tree: A review

An Informed-Bi-Quick RRT* Algorithm Based on Offline Sampling: Motion Planning Considering Multiple Constraints for a Dual-Arm Cooperative System

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AEB-RRT: an adaptive extension bidirectional RRT algorithm

Toward Optimization of AGV Path Planning: An RRT^*-ACO Algorithm

Toward Optimization of AGV Path Planning: An RRT^*-ACO Algorithm