Acta Geodaetica et Cartographica Sinica ›› 2025, Vol. 54 ›› Issue (6): 1139-1151.doi: 10.11947/j.AGCS.2025.20240478

• Cartography and Geoinformation • Previous Articles     Next Articles

Time optimal path planning method based on Gaussian mixture regression and improved A* algorithm

Ruixin ZHANG1(), Qing XU1(), Zheng LÜ1, Guo ZHANG2, Xia CHU3, Xiang CHENG4   

  1. 1.Institute of Surveying and Maping, Information Engineering University, Zhengzhou 450001, China
    2.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    3.Troops 61206, Beijing 100043, China
    4.Troops 66069, Luoyang 471000, China
  • Received:2024-11-26 Revised:2025-05-06 Online:2025-07-14 Published:2025-07-14
  • Contact: Qing XU E-mail:1185269992@qq.com;xq1982_no.1@163.com
  • About author:ZHANG Ruixin (2000—), male, postgraduate, majors in vehicle path planning. E-mail: 1185269992@qq.com
  • Supported by:
    The National Natural Science Foundation of China(42101455)

Abstract:

Path planning plays an important role in emergency rescue and emergency rescue. In these scenarios, vehicles are often able to get faster routes through a combination of off-road and on-road routes. Therefore, a time optimal path planning method based on Gaussian mixture regression and improved A* algorithm is proposed. First, a time optimal traffic cost model is constructed using the A* algorithm combined with a vehicle speed coefficient, accounting for various factors affecting vehicle traffic, including road conditions. Second, the Gaussian mixture model is employed to collect trajectory information for the proposed rescue route. Combined with Gaussian mixture regression, this model constrains the search radius of the A* algorithm, enhancing its search efficiency. Finally, experimental verification is conducted using data from Dengfeng, Henan province. The results show that compared with the four algorithms of 2D A*, 3D A*, 2D time optimal A* and improved time optimal A* without Gaussian mixture regression constraints, the proposed algorithm reduces the path passage time by 2.02% to 32.31%, decreases code running time by 38.76% to 83.6%, and reduces node traversal by 38.69% to 79.77%. When compared to the recommended path from AutoNavi, the proposed algorithm shortens the path distance by 6.86% to 9.53% and reduces passage time by 8.41% to 17.22%.

Key words: path planning with combination of road and non-road routes, toll cost modeling, optimal timing, Gaussian mixed regression, A* algorithm

CLC Number: