系统仿真学报 ›› 2024, Vol. 36 ›› Issue (8): 1969-1981.doi: 10.16182/j.issn1004731x.joss.23-0779

• 论文 • 上一篇    

动态路网环境下的路径优化算法研究

解鑫1, 胡小兵2, 周航3   

  1. 1.中国民航大学 电子信息与自动化学院, 天津 300300
    2.中国民航大学 安全科学与工程学院, 天津 300300
    3.中国民航大学 中欧航空工程师学院, 天津 300300
  • 收稿日期:2023-06-28 修回日期:2023-08-22 出版日期:2024-08-15 发布日期:2024-08-19
  • 通讯作者: 胡小兵
  • 第一作者简介:解鑫(1999-),男,硕士生,研究方向为人工智能。
  • 基金资助:
    国家自然科学基金(62201577)

Research on Path Optimization Algorithm in Dynamic Routing Environment

Xie Xin1, Hu Xiaobing2, Zhou Hang3   

  1. 1.College of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, China
    2.College of Safety Science & Engineering, Civil Aviation University of China, Tianjin 300300, China
    3.Sino-European Institute of Aviation Engineering, Civil Aviation University of China, Tianjin 300300, China
  • Received:2023-06-28 Revised:2023-08-22 Online:2024-08-15 Published:2024-08-19
  • Contact: Hu Xiaobing

摘要:

为解决真实动态路网环境下,静态路径优化(static path optimization,SPO)方法和传统动态路径优化(dynamic path optimization,DPO)方法由于频繁实时优化计算,规划路径过程中容易出现绕路、折返、计算复杂度高等问题,提出基于涟漪扩散算法(ripple-spreading algorithm,RSA)的重启协同进化路径优化(restart co-evolutionary path optimization,RCEPO) 方法 。将路径优化过程与路网环境的动态变化过程相结合,提升了路径优化效果。仅当路网环境的动态变化超出预测范围时才进行路径的重新优化计算,降低了计算复杂度。实验结果表明:在动态路网环境下,该方法的实际行进轨迹长度和行进时间相较于传统DPO方法分别缩短了17%和12%。有效解决了真实动态路网环境下路径优化问题。并且通过机器狗实验,验证了该方法的实用性和有效性。

关键词: 路径优化, 动态环境, 协同进化, 涟漪扩散算法:不确定性

Abstract:

In a real dynamic routing environment, static path optimization (SPO) and traditional dynamic path optimization (DPO) tend to encounter issues such as detours, reversals and high computational complexity due to frequent real-time optimization calculation. To address these problems, a novel restart co-evolutionary path optimization (RCEPO) method based on the ripple-spreading algorithm (RSA) is proposed. This method integrates the path optimization process with the dynamic changes of the routing network environment to enhance the effectiveness of path optimization. Moreover, the path re-optimization calculation is performed only when the dynamic changes in the routing environment exceed the predicted range, thereby reducing computational complexity. Experimental results demonstrate that the actual travel path length and the actual travel time of this method are shortened by 17% and 12%, respectively, compared with the traditional DPO method under the dynamic routing network environment. It can effectively solve the path optimization problem under the real dynamic routing network environment. The feasibility and effectiveness of this approach are validated through experiments conducted with a robot dog.

Key words: path optimization, co-evolutionary, dynamic environment, ripple-spreading algorithm, uncertainty

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