东北大学学报(自然科学版) ›› 2025, Vol. 46 ›› Issue (3): 12-19.DOI: 10.12068/j.issn.1005-3026.2025.20239047

• 信息与控制 • 上一篇    下一篇

改进PSO-PH-RRT*算法在智能车路径规划中的应用

蒋启龙(), 许健   

  1. 西南交通大学 电气工程学院,四川 成都 611756
  • 收稿日期:2023-09-12 出版日期:2025-03-15 发布日期:2025-05-29
  • 通讯作者: 蒋启龙
  • 作者简介:蒋启龙(1969—),男,四川南充人,西南交通大学教授.
  • 基金资助:
    国家自然科学基金资助项目(52277166)

Application of Improved PSO-PH-RRT* Algorithm in Intelligent Vehicle Path Planning

Qi-long JIANG(), Jian XU   

  1. School of Electrical Engineering,Southwest Jiaotong University,Chengdu 611756,China.
  • Received:2023-09-12 Online:2025-03-15 Published:2025-05-29
  • Contact: Qi-long JIANG
  • About author:JIANG Qi-long, E-mail: double_long@126.com

摘要:

在机器人控制、智能车自主导航等应用场景中,路径规划需要考虑到环境中的障碍物、地形等因素.针对路径规划中快速拓展随机树(RRT)算法拓展目标方向盲目、效率较低的问题,提出了基于粒子群算法优化的均匀概率快速拓展随机树(PSO-PH-RRT*)算法.该算法在基于均匀概率的快速拓展随机树(PH-RRT*)算法的基础上,利用粒子群算法更新方向概率作为随机树节点的速度方向,从而改善了节点的位置更新策略,并将节点到目标向量的距离和轨迹平滑度作为粒子群算法的适应度函数.最后在多种障碍环境下进行仿真.结果表明,PSO-PH-RRT*算法能大大减少迭代时间成本,同时改善路径长度和平滑度.

关键词: 路径规划, RRT算法, 改进粒子群优化算法, 目标向量, 代价函数, 适应度函数

Abstract:

In application scenarios like robot control and autonomous navigation of intelligent vehicle, path planning needs to account for factors including obstacles and terrain. To address the issues of directionless expansion target and low efficiency in rapidly-exploring random tree (RRT) algorithm in path planning, a particle swarm optimization for probabilistically homogeneous rapidly-exploring random tree (PSO-PH-RRT*) algorithm is proposed. This algorithm base on the probabilistically homogeneous rapidly-exploring random tree (PH-RRT*) algorithm by using the particle swarm optimization algorithm to update the probability of direction as the velocity direction for random tree nodes, thereby improving the node position update strategy. It also uses the distance between the node and the target vector, along with trajectory smoothness, as the fitness function in the particle swarm optimization algorithm. Finally, simulations across various scenarios demonstrate that the PSO-PH-RRT* algorithm can significantly reduce iteration time costs while improving path length and smoothness.

Key words: path planning, RRT algorithm, improved particle swarm optimization algorithm, target vector, cost function, fitness function

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