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2025年04期
自动化技术

基于强化学习的改进RRT~*路径规划

Improved RRT~* Path Planning Based on Reinforcement Learning

张艳珠;侯亢钧;陈勇;李婷雪;李妍; ZHANG Yanzhu;HOU Kangjun;CHEN Yong;LI Tingxue;LI Yan;

针对RRT~*算法在路径规划中面临搜索效率不高、易于陷入局部最优等问题,提出一种结合强化学习的Q-RRT~*算法。该算法将Q-Learning算法和RRT~*算法相融合,首先引入转角偏向策略增强路径搜索时的导向作用、减少无效节点的生成,提升算法的搜索效率;其次通过动R搜索算法动态地调整搜索半径,进一步优化路径的质量和冗余节点的产生;最后对生成的路径使用三次B样条插值法和冗余节点删除法进一步优化路径质量。在二维和三维环境下的仿真实验结果表明,改进后的Q-RRT~*算法和RRT、RRT~*和RL-RRT算法相比,路径规划时长平均快39.7%,迭代次数平均减低27.9%,路径长度平均缩短16.3%。

In order to solve the problems of low search efficiency and tendency to fall into local optimum in RRT~* path planning, a Q-RRT~* algorithm combined with reinforcement learning was proposed, which fused the Q-Learning algorithm and the RRT~* algorithm.Firstly, the corner bias strategy was introduced to enhance the guiding effect of path search, reduce the generation of invalid nodes, and improve the search efficiency of the algorithm.Secondly, the search radius was dynamically adjusted by the dynamic R search algorithm to further optimize the quality of the path and the generation of redundant nodes.Finally the cubic B-spline interpolation method and the redundant node deletion method were used to further optimize the path quality of the generated path.Simulation results in 2D and 3D environments show that the improved Q-RRT~* algorithm is 39.7%faster on average, 27.9%less iteration and 16.3%shorter in path length than RRT,RRT~* and RL-RRT algorithms.

2025 年 04 期 v.44 ; 辽宁省教育厅高等学校基本科研项目(LJKZ0245)
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基于文本图表示学习的人格分类方法

Personality Classification Method Based on Text Graph for the Representation of Learning

刘猛;范摇珊;刘芳;张德育;贡胜男; LIU Meng;FAN Yaoshan;LIU Fang;ZHANG Deyu;GONG Shengnan;

针对网络用户的传统人格分类方法提取文本语义特征不充分、分类准确率低的问题,提出一种基于文本图表示学习的人格分类方法。该方法利用自然语言处理技术,并结合深度学习和图网络模型,设计一种自适应图卷积网络(adaptive graph convolutional network, ADGCN),通过自适应调整机制优化节点表示,平衡了节点特征的局部与全局信息。在Kaggle数据集上的测试实验表明,F1分数最高为80%,且平均F1分数达到71.14%,比传统机器学习方法和预训练模型BERT提高近20%,展现了模型计算效率上的优越性。

To solve the problems of insufficient text semantic features and low classification accuracy of traditional personality classification methods for network users, a personality classification method based on text graph representation learning is proposed.This method uses natural language processing technology, combined with deep learning and graph network model, to build a new type of network user personality classification model, and designs an adaptive graph convolutional network(ADGCN).The node representation is optimized by an adaptive adjustment mechanism, which balances the local and global information of node features.Experiments on the Kaggle dataset show that the F1 score is up to 80%,and the average F1 score reaches 71.14%,which is nearly 20% higher than the traditional machine learning method and BERT pre-training model, showing the superiority of the model's computational efficiency.

2025 年 04 期 v.44 ; 辽宁省教育厅高等学校基本科研重点项目(LJ212410144013); 沈阳市自然科学基金项目(22-315-6-10); 沈阳市中青年科技创新人才支持计划项目(RC210280)
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期刊名称: 沈阳理工大学学报Journal of Shenyang Ligong University
创办日期: 1982年
主管单位: 辽宁省教育厅
主办单位: 沈阳理工大学

出版单位:《沈阳理工大学学报》编辑部
刊期: 双月刊
电话: 024-24686097
Email: sgxb6097@sylu.edu.cn
国内统一刊号(CN): CN 21-1594/T
国际标准刊号(ISSN):ISSN 1003-1251

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