一种改进的A*路径规划算法研究及其Qt实现
An Improved A* Path Planning Algorithm and Its Qt Implementation
DOI: 10.12677/AIRR.2023.122015, PDF,  被引量    科研立项经费支持
作者: 耿 飚, 宋丽华, 吴爱燕:北方工业大学信息学院,北京
关键词: QtA*算法启发式搜索UOS路径规划Qt A* Algorithm Heuristic Search UOS Path Planning
摘要: 路径规划算法是人工智能技术中的关键技术之一,常用的A*算法可以在大多数情况下以较快的速度生成路径,但考虑到不同工作环境中最优路径与运行效率不可兼得,为提高现有A*算法的使用灵活度,本文提出了一种加权A*算法,改进了算法的运行时间和搜索准确度。改进后的算法在Qt5.12.9和UOS操作系统环境下进行了实现和测试,结果表明求出最短路径的概率为100%,平均运行时间与传统A*算法相比缩短了12.3%。与标准的A*相关算法相比,提出的改进算法能够缩短规划时间,缩小算法搜索空间,提高算法在路径规划问题中的适用性,提高了不同环境路径生成的效率和平滑性,为运行载体在复杂背景下的精确控制提供了新思路。
Abstract: Path planning algorithm is one of the key technologies in artificial intelligence technology, common A* algorithm can generate road route quickly under most circumstances. Given that it can not achieve the best road route and working efficiency in different working environments at the same time, this paper proposes a weighted A* algorithm, which shortens the running time and enhances search accuracy of the algorithm. The improved algorithm is implemented and tested under Qt5. 12.9 and UOS environment, the results show that the probability of finding the shortest path is 100%, and the average running time is shortened by 12.3%. Compared with the standard A* algorithm, the weighted A* algorithm can shorten the planning time, reduce the search space of the algorithm, and improve the applicability of the algorithm in path planning problems. It improves the efficiency and smoothness of path generation in different environments, and provides a new idea for the precise control of operating vehicles in complex backgrounds.
文章引用:耿飚, 宋丽华, 吴爱燕. 一种改进的A*路径规划算法研究及其Qt实现[J]. 人工智能与机器人研究, 2023, 12(2): 115-125. https://doi.org/10.12677/AIRR.2023.122015

参考文献

[1] Zhang, H., Hong, W. and Chen, M. (2019) A Path Planning Strategy for Intelligent Sweeping Robots. 2019 IEEE International Conference on Mechatronics and Automation (ICMA), Tianjin, China, 4-7 August 2019, 11-15. [Google Scholar] [CrossRef
[2] Xue, X., Cao, X., Zhang, X., Wang, C., Ma, H. and Fan, H. (2020) Electromagnetic Wave Attenuation Mechanism and Distribution Strategy for Coal Mine Rescue Robot under the Typical Obstacle Environment. Radio Science, 55. [Google Scholar] [CrossRef
[3] Deng, S., Cai, H., Li, K., Cheng, Y., Ni, Y. and Wang, Y. (2018) The Design and Analysis of a Light Explosive Ordnance Disposal Manipulator. 2018 2nd International Conference on Robotics and Automation Sciences (ICRAS), Wuhan, China, 23-25 June 2018, 1-5. [Google Scholar] [CrossRef
[4] Guo, P. and Deng, W. (2019) Design and Implementation of Intelligent Medical Customer Service Robot Based on Deep Learning. 2019 16th International Computer Conference on Wavelet Active Media Technology and Information Processing, Chengdu, China, 14-15 December 2019, 37-40. [Google Scholar] [CrossRef
[5] Wu, Q., Chen, Z.Y., Wang, L., et al. (2020) Real-Time Dynamic Path Planning of Mobile Robots: A Novel Hybrid Heuristic Optimization Algorithm. Sensors, 20, 188 p. [Google Scholar] [CrossRef] [PubMed]
[6] Pan, Z., Liu, S. and Fu, W.N. (2017) A Review of Visual Moving Target Tracking. Multimedia Tools and Applications, 76, 16989-17018. [Google Scholar] [CrossRef
[7] Fan, X.J., Sun, M.M., Lin, Z.H., et al. (2018) Automated Noncontact Micromanipulation Using Magnetic Swimming Microrobots. IEEE Transactions on Nanotechnology, 17, 666-669. [Google Scholar] [CrossRef
[8] Salehizadeh, M. and Diller, E.D. (2021) Path Planning and Tracking for an Underactuated Two-Microrobot System. IEEE Robotics and Automation Letters, 6, 2674-2681. [Google Scholar] [CrossRef
[9] Huang, C.Y., Xu, T.T., Liu, J., et al. (2019) Visual Servoing of Miniature Magnetic Film Swimming Robots for 3D Arbitrary Path Following. IEEE Robotics and Automation Letters, 4, 4185-4191. [Google Scholar] [CrossRef
[10] 宋丽君, 周紫瑜, 李云龙, 侯佳杰, 何星. 改进Q-Learning的路径规划算法研究[J/OL]. 小型微型计算机系统: 1-8.
https://kns.cnki.net/kcms/detail/21.1106.tp.20230218.2208.008.html, 2023-02-21.
[11] 张柏鑫, 杨毅镔, 朱华中, 等. 基于深度强化学习的移动机器人动态路径规划算法[J]. 计算机测量与控制, 2023, 31(1): 153-159+166. [Google Scholar] [CrossRef
[12] 向红标, 杨大虎, 杨璐, 等. 复杂环境下磁弹性微型游泳机器人的路径规划与识别跟踪[J]. 机械工程学报, 59(5): 89-99. [Google Scholar] [CrossRef