Improved rapidly-exploring random tree path planning algorithm

SUN Feng-cai; ZHANG Ya-nan; SHI Xu-hua

doi:10.13873/j.1000-9787(2017)09-0129-03

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Improved rapidly-exploring random tree path planning algorithm

更新时间：2025-09-11

- Improved rapidly-exploring random tree path planning algorithm
- Vol. 36, Issue 9, Pages: 129-131+135(2017)
- 作者机构：
  
  宁波大学信息科学与工程学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-9787(2017)09-0129-03
  CLC： TP242
- Published：2017
- 稿件说明：
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[1]孙丰财,张亚楠,史旭华.改进的快速扩展随机树路径规划算法[J].传感器与微系统,2017,36(09):129-131+135.

SUN Feng-cai, ZHANG Ya-nan, SHI Xu-hua. Improved rapidly-exploring random tree path planning algorithm[J]. 2017, 36(9): 129-131+135.
[1]孙丰财,张亚楠,史旭华.改进的快速扩展随机树路径规划算法[J].传感器与微系统,2017,36(09):129-131+135. DOI： 10.13873/j.1000-9787(2017)09-0129-03.

SUN Feng-cai, ZHANG Ya-nan, SHI Xu-hua. Improved rapidly-exploring random tree path planning algorithm[J]. 2017, 36(9): 129-131+135. DOI： 10.13873/j.1000-9787(2017)09-0129-03.

摘要

针对快速扩展随机树（RRT）路径规划算法缺乏稳定性和偏离最优解的问题

提出了一基于RRT的偏向性路径搜索算法（m-RRT）。m-RRT采用生成随机点向量组的形式对随机点选取策略进行了优化

改善快速扩展随机树的不确定性

减少不必要的扩展

而加快向目标位置搜索的速度

且得到的路径优于RRT算法的结果。通过其在二维平面路径规划和三维机械臂路径规划的测试

表明其具有一定的应用价值。

Abstract

Because the rapidly-exploring random tree（ RRT） path planning algorithm is unstable and not optimal

propose a biased path search strategy which is called m-RRT. By generating a random point vectors to optimize the strategy of random points selection

the uncertainty of RRT searching can be improved

meanwhile

the expansion of searching tree can also be reduced. So

it can improve the searching speed to the destination

and the path got by m-RRT is better than that by RRT. Through the two-dimensional path planning and three-dimensional manipulator path planning

the results show that it has certain application value.

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references

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