| 摘要: |
| 目的 以智慧校园为背景，针对现有校园复杂环境无人车路径规划技术存在的规划效率低、拐弯角度大、鲁棒性差等问题，提出一种融合改进A*算法和模糊控制动态窗口法(Dynamic Window Approach，DWA)的校园复杂环境无人车路径规划方法，以降低校园安全事故风险。方法 首先，量化环境障碍物信息，将其引入A*算法启发函数，采用关键节点选取策略，优化全局路径节点，利用Clamped-B样条曲线对路径进行平滑处理。其次，设计改进模糊控制DWA，将全局路径关键节点作为DWA局部目标点，对目标点进行动态设置，设计融合改进A*算法和改进模糊控制DWA算法的融合路径规划方法，实现复杂环境下校园无人车的路径规划。最后，在未知校园静态和动态2种环境下进行校园无人车实车实验。结果 改进A*+DWA路径规划方法的实时避障性能优于其他2种方法，规划的路径符合全局路径，路径轨迹偏差较小，平均偏差仅±0.109 m，有效减小无人车拐弯角度，规划的路径安全性更好、效率更高，保证了全局最优的同时生成的轨迹更加平滑。结论 融合改进A*算法和模糊控制DWA算法的校园复杂环境无人车路径规划方法可保证校园无人车运行的可靠性，满足智慧校园建设需求。 |
| 关键词: 智慧校园 校园无人车 全局路径规划 局部路径规划 校园复杂环境 |
| DOI：10.19554/j.cnki.1001-3563.2025.07.024 |
| 分类号: |
| 基金项目:国家自然科学基金面上项目(51974229);国家重点研发计划重点专项(2018YFB1703402，2018YFC0808203);陕西省科技创新团队(2021TD-27) |
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| Unmanned Vehicle Path Planning Method in Complex Campus Environment from the Perspective of Smart Campus |
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YANG Zhi, ZHANG Chuanwei
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(College of Mechanical Engineering, Xi'an University of Science and Technology, Xi'an 710054, China)
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| Abstract: |
| Focusing on the problem that the existing unmanned vehicle path planning technology in complex campus environment is faced with low planning efficiency, large turning angle and poor robustness under the background of smart campus, the work aims to propose an unmanned vehicle path planning method which integrates improved A algorithm and fuzzy control DWA for the complex campus environment. Firstly, the environment obstacle information was quantified which was introduced into the A algorithm heuristic function. The global path node was optimized by selecting key nodes, and the path was smoothed by Clamped-B spline curve. Secondly, the improved fuzzy control DWA was designed, and the key nodes of global path were taken as local target points of DWA, which were dynamically set. The integrated path planning method of improved A and improved fuzzy control DWA was designed to realize the path planning of unmanned vehicles in complex campus environment. Finally, the campus unmanned vehicle experiment was carried out under the static and dynamic unknown campus environment. The improved A + DWA path planning method had better real-time obstacle avoidance performance than the other two methods, and the planned path conformed to the global path. The path trajectory deviation was small, and the average deviation was only±0.109 m, which effectively reduced the turning angle of the unmanned vehicle. The planned path was safer and more efficient, which ensured the global optimum and the smoother generated trajectory. The path planning method of unmanned vehicles in complex campus environment, which integrates improved A algorithm and fuzzy control DWA, can ensure the reliability of unmanned vehicles in campus operation and meet the needs of smart campus construction. |
| Key words: smart campus campus unmanned vehicle global path planning local path planning complex campus environment |
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