一种动态窗口法和人工势场法融合的AGV路径规划算法
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TM911

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国家能源集团科技创新项目(GJNY-21-57);浙江省自然科学基金(LTGN24E050001)


A Path Planning Algorithm for AGV Combining the Improved Dynamic Window Approach and Artificial Potential Field Method
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    摘要:

    针对原始动态窗口法(dynamic window approach, DWA)在路径规划中存在的振荡现象,设计了一种改进的DWA路径规划算法,该算法融合了人工势场法。首先,提升了DWA算法的安全性约束,并将原始DWA中的线性障碍物距离评价函数改进为人工势场法中的非线性障碍物势场函数。其次,将改进的DWA与梯度下降法的平滑A*路径相结合,以解决传统算法全局规划能力不足的问题。最后,通过仿真实验和实物实验验证了算法的可行性。在仿真实验中,与原始算法相比,本文提出的改进算法在设计的障碍物场景中减少了9.84%的路径长度,运行时间缩短了31.71%,平滑度提升了6.49%。同时,与相关文献的结果相比,本文在不同场景下均有所提升。在自动导引车实物实验中,路径长度减少了10.76%,运行时间缩短了13.09%。因此,改进的DWA算法能够生成更平滑的路径、更短的路径长度和更短的运行时间。

    Abstract:

    Considering the oscillation phenomenon of the original dynamic window approach (DWA) in path planning, an improved DWA path planning algorithm is designed, which is integrated with the artificial potential field method. Firstly, the safety constraint of the DWA algorithm is improved, and the linear obstacle distance evaluation function in the original DWA is improved to the nonlinear obstacle potential field function in the artificial potential field method. Secondly, the improved DWA is combined with the smooth A* path of the gradient descent method to solve the problem of poor global planning of the traditional algorithm. Finally, the feasibility of the algorithm is verified by simulation experiments and physical experiments. In the simulation experiments, compared with the original algorithm, the improved algorithm in this paper reduces the path of the designed obstacle scene by 9.84%, reduces the running time by 31.71%, and improves the smoothness by 6.49%. Meanwhile, compared with the results of related literatures, the results of this paper have been improved to different degrees in different scenarios. In the physical experiments of automated guided vehicle, the path length is reduced by 10.76% and the elapsed time is reduced by 13.09%. Therefore, the improved DWA generates better path smoothness, shorter path length and shorter elapsed time.

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李玉清,梁忠楠,赵衍昭,等. 一种动态窗口法和人工势场法融合的AGV路径规划算法[J]. 科学技术与工程, 2025, 25(14): 5924-5933.
Li Yuqing, Liang Zhongnan, Zhao Yanzhao, et al. A Path Planning Algorithm for AGV Combining the Improved Dynamic Window Approach and Artificial Potential Field Method[J]. Science Technology and Engineering,2025,25(14):5924-5933.

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历史
  • 收稿日期:2024-07-17
  • 最后修改日期:2025-03-01
  • 录用日期:2024-08-31
  • 在线发布日期: 2025-05-22
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