武汉理工大学学报(交通科学与工程版)2024,Vol.48Issue(2) :403-408.DOI:10.3963/j.issn.2095-3844.2024.02.035

一种面向动态场景的无人艇路径规划方法

A Path Planning Method for Unmanned Surface Vehicles Oriented to Dynamic Scenes

何正伟 徐小本 汪成立
武汉理工大学学报(交通科学与工程版)2024,Vol.48Issue(2) :403-408.DOI:10.3963/j.issn.2095-3844.2024.02.035

一种面向动态场景的无人艇路径规划方法

A Path Planning Method for Unmanned Surface Vehicles Oriented to Dynamic Scenes

何正伟 1徐小本 1汪成立2
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作者信息

  • 1. 武汉理工大学航运学院 武汉 430063
  • 2. 浙江省交通运输科学研究院 杭州 311305
  • 折叠

摘要

文中提出了一种融合时空机制的改进Q学习算法(itegrated spatio-temporal mechanism Q-learning,ISTM).根据动态场景中障碍物时空变化特性,建立动、静态障碍物混合的动态环境;通过引入时空机制构建动态奖惩函数,根据障碍物动态变化情况展开路径搜索,提高无人艇对障碍物状态变化的感知能力;建立动态探索机制,通过自适应调整贪婪因子,提升无人艇在动态场景中的探索效率.结果表明:基于ISTM算法的路径规划收敛时间更短,稳定性更高,所规划出的路径更短.

Abstract

An improved Q-learning algorithm(ISTM)integrating spatio-temporal mechanism was pro-posed.According to the temporal and spatial variation characteristics of obstacles in the dynamic scene,a dynamic environment with mixed dynamic and static obstacles was established.The dynamic reward and punishment function was constructed by introducing the space-time mechanism,and the path search was carried out according to the dynamic change of obstacles,so as to improve the percep-tion ability of unmanned boats to the change of obstacle state.The dynamic exploration mechanism was established,and the greedy factor was adjusted adaptively to improve the exploration efficiency of unmanned boats in dynamic scenes.The results show that the path planning based on ISTM algorithm has shorter convergence time,higher stability and shorter planned path.

关键词

路径规划/强化学习/动态环境/时空机制/无人艇

Key words

path planning/reinforcement learning/dynamic environment/spatiotemporal mechanism/unmanned surface vehicle

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基金项目

浙江省科学技术厅重点研究计划(2021C01010)

浙江省科学技术厅重点研究计划(ZJJKY2021-DY-016)

湖北省重点研究计划项目(2023BAB013)

出版年

2024
武汉理工大学学报(交通科学与工程版)
武汉理工大学

武汉理工大学学报(交通科学与工程版)

CSTPCD
影响因子:0.462
ISSN:2095-3844
参考文献量2
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