基于改进RRT*FN算法的机器人路径规划
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作者单位:

湖南大学电气与信息工程学院

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中图分类号:

TP242

基金项目:

国家自然科学基金项目(面上项目,重点项目,重大项目)


Robot Path Planning Based on Improved RRT*FN Algorithm
Author:
Affiliation:

HUNAN UNIVERSITY COLLEGE OF ELECTRICAL AND INFORMATION ENGINEERING

Fund Project:

The National Natural Science Foundation of China (General Program, Key Program, Major Research Plan)

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    摘要:

    针对固定节点数的渐进最优快速扩展随机数算法(Rapidly-exploring Random Trees Star Fixed Nodes,RRT*FN) 精度低、收敛到最优值速度慢等问题, 本文提出了一种改进的RRT*FN 路径规划算法, 并用于解决二维静态环境下的移动机器人全局路径规划问题. 首先, 改进算法使用与RRT*FN 算法相同的均匀采样方法进行路径搜索. 当改进算法搜索到一条初始路径时, 则在之后的路径规划中使用启发式采样方法. 在之后的每次迭代中,改进算法在椭圆子集采样方法与路径点邻近区域采样方法中随机选择一种作为当前采样方法. 然后, 当树中的总节点数达到预设值时, 则对树中的叶子结点采用加权方法进行删除. 通过给予采样区域内的叶子结点更高的权重, 从而将采样区域外的叶子结点以更高概率删除. 因此得以保留树中的高性能节点, 提高了算法性能. 最后, 仿真实验验证了本文改进算法的有效性.

    Abstract:

    Aiming at the problems of low accuracy and slow convergence of RRT*FN(Rapidly-exploring Random Trees Star Fixed Nodes) algorithm, this paper proposes an improved RRT*FN path planning algorithm, which is used to solve the global path planning problem of mobile robots in a two-dimensional static environment. First, the improved algorithm uses the same uniform sampling method as the RRT*FN algorithm for path planning. When the improved algorithm gets an initial path, heuristic sampling method will be used in subsequent path searching. In each next iteration, the improved algorithm randomly selects a method from the ellipse subset sampling method and the path point neighboring area sampling method as the current sampling method. Then, when the total number of nodes in the tree reaches the preset value, the leaf nodes in the tree are deleted using a weighted method. By giving higher weight to the leaf nodes in the sampling area, the leaf nodes outside the sampling area are deleted with a higher probability. Therefore, high-performance nodes in the tree can be retained, and algorithm performance is improved. Finally, the simulation experiments verify the effectiveness of the improved algorithm.

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历史
  • 收稿日期:2019-12-07
  • 最后修改日期:2021-01-27
  • 录用日期:2020-04-03
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