App. Res. of Comp. Accepted Paper
Vol. 43, 2026 No. 11

Fast marching tree algorithm based on area-guided sampling

Xu Zhengyu1,2
Jia Xiaolin1,2
Gu Yajun1,2
Yuan Lei1,2
1. School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China
2. Mobile Internet of Things and Radio Frequency Identification Technology Key Laboratory of Mianyang (MIOT&RFID), Mianyang 621010, China
DOI: 10.19734/j.issn.1001-3695.2026.03.0065

Abstract

This paper proposes an Area-Guided Sampling Fast Marching Tree (AGS-FMT*) algorithm to address the critical limitations of the standard Fast Marching Tree (FMT*) algorithm in scenarios with a small number of sampling points: low path planning success rate, difficulty traversing narrow passages, excessive inflection points in generated paths, and long path lengths. The algorithm first calculates the environmental obstacle density to determine the number of sampling points required for pre-training, and marks key sampling areas for path planning in the map through multiple rounds of pre-training. It then adopts a key area-guided sampling strategy and introduces a target point offset mechanism for sampling points falling into key areas to improve sampling effectiveness in critical regions. During the path tree expansion phase, it incorporates a smoothness cost to comprehensively select nodes with smoother direction changes for connection. Finally, it applies a path pruning strategy to further eliminate redundant nodes and optimize the number of path inflection points. Simulation experiments demonstrate that under the condition of 10 sampling points, the proposed algorithm achieved a 62% improvement in path planning success rate, a 61.67% reduction in the number of inflection points, and shortened the path length to 48.54 m compared with the standard FMT* algorithm in Map 1 (random obstacle map) ; in Map 2 (narrow passage map) , it delivered a 36% increase in success rate, a 10.91% decrease in inflection points, and reduced the path length to 49.96 m.

Foundation Support

国家自然科学基金面上项目(61471306)
四川省自然科学基金项目(2022NSFSC0548)
四川省重点研发计划项目(2020YFS0360)
四川省教育厅教改项目(JG2021-1414)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2026.03.0065
Publish at: Application Research of Computers Accepted Paper, Vol. 43, 2026 No. 11

Publish History

[2026-07-02] Accepted Paper

Cite This Article

徐正宇, 贾小林, 顾娅军, 等. 基于重点区域采样的快速行进树路径规划算法 [J]. 计算机应用研究, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0065. (Xu Zhengyu, Jia Xiaolin, Gu Yajun, et al. Fast marching tree algorithm based on area-guided sampling [J]. Application Research of Computers, 2026, 43 (11). (2026-07-31). https://doi.org/10.19734/j.issn.1001-3695.2026.03.0065. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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