典型文献
An Adaptive Rapidly-Exploring Random Tree
文献摘要:
Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning (MP) problems, in which rapidly-exploring random tree (RRT) and the faster bidirectional RRT (named RRT-Connect) algorithms have achieved good results in many planning tasks. However, sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages. Therefore, several algorithms have been proposed to overcome these drawbacks. As one of the improved algorithms, Rapidly-exploring random vines (RRV) can achieve better results, but it may perform worse in cluttered environments and has a certain environmental selectivity. In this paper, we present a new improved planning method based on RRT-Connect and RRV, named adaptive RRT-Connect (ARRT-Connect), which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments. The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.
文献关键词:
中图分类号:
作者姓名:
Binghui Li;Badong Chen
作者机构:
Institute of Artificial Intelligence and Robotics,Xi'an Jiaotong University,Xi'an 710049,China
文献出处:
引用格式:
[1]Binghui Li;Badong Chen-.An Adaptive Rapidly-Exploring Random Tree)[J].自动化学报(英文版),2022(02):283-294
A类:
vines,cluttered,ARRT
B类:
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AB值:
0.550224
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