• Chinese Core Journal
  • Chinese Science Citation Database (CSCD) Source journal
  • Journal of Citation Report of Chinese S&T Journals (Core Edition)
GAO Ruitao, ZI Le, HU Lian, et al. Methods and experiments of farm road layer construction and farm machinery transfer path planning[J]. Journal of South China Agricultural University, 2025, 46(2): 256-264. DOI: 10.7671/j.issn.1001-411X.202405001
Citation: GAO Ruitao, ZI Le, HU Lian, et al. Methods and experiments of farm road layer construction and farm machinery transfer path planning[J]. Journal of South China Agricultural University, 2025, 46(2): 256-264. DOI: 10.7671/j.issn.1001-411X.202405001

Methods and experiments of farm road layer construction and farm machinery transfer path planning

More Information
  • Received Date: April 30, 2024
  • Available Online: January 24, 2025
  • Published Date: November 21, 2024
  • Objetive 

    A path planning method for unmanned farm machinery transfer is proposed to address the problems of relying on manual driving or manual management and planning, which are time-consuming, labor-intensive, and do not meet the requirements of unmanned applications.

    Method 

    ArcGIS was used to construct farm road layers and networks, and simulation experiments were conducted. A Dijkstra bidirectional search transfer path planning algorithm based on graph theory was developed, single and bidirectional searches were simulated using Python. A transfer path planning system based on a web platform was built.

    Result 

    In the simulation of the road network, the distances traveled by agricultural machinery from the hangar to the field, from the field to the field, and from the field to the hangar at a speed of 0.7 m/s were 241.57, 74.46 and 75.66 m respectively, with corresponding time of 345.10, 106.37 and 108.09 s. The single and bidirectional search time of Dijkstra’s algorithm were 0.632 and 0.216 s respectively, and the computational efficiency of bidirectional search was improved by 65.82% compared to unidirectional search. The transfer path planning system for agricultural machinery based on the web platform conducted real vehicle road tests at a speed of 0.7 m/s from the hangar to the field, from the field to the field, and from the field to the hangar. The absolute arithmetic mean difference between the sampling points of the path and the actual path of the agricultural machinery was less than 0.1 m, which met the transfer requirements of unmanned farm agricultural machinery. Compared to manual marking, the path planning efficiency of the transfer path planning system was higher.

    Conclusion 

    The constructed farm road layer, road network and transfer path planning system meet the road transfer needs of unmanned farm machinery. The research results can provide the technical support for the transfer path of agricultural machinery in unmanned farms.

  • [1]
    罗锡文, 廖娟, 胡炼, 等. 我国智能农机的研究进展与无人农场的实践[J]. 华南农业大学学报, 2021, 42(6): 8-17. doi: 10.7671/j.issn.1001-411X.202108040
    [2]
    罗锡文. 农场是数字农业的实现途径之一[J]. 大数据时代, 2021(10): 13-19.
    [3]
    车刚, 陈正发, 秦泗君, 等. 建三江水稻智慧农场技术创新与应用[J]. 现代农业装备, 2023, 44(3): 77-80. doi: 10.3969/j.issn.1673-2154.2023.03.012
    [4]
    罗锡文, 胡炼, 何杰, 等. 中国大田无人农场关键技术研究与建设实践[J]. 农业工程学报, 2024, 40(1): 1-16. doi: 10.11975/j.issn.1002-6819.202312126
    [5]
    吴晓明, 邢廷炎, 钱建平, 等. 面向车辆监控的LBS地图可视化技术研究[J]. 地理与地理信息科学, 2016, 32(1): 100-104. doi: 10.3969/j.issn.1672-0504.2016.01.019
    [6]
    张攀, 刘经南. 通用化高精地图数据模型[J]. 测绘学报, 2021, 50(11): 1432-1446. doi: 10.11947/j.AGCS.2021.20210254
    [7]
    路鹏伟. 车路网协同下的电动汽车充电路径规划研究[D]. 秦皇岛: 燕山大学, 2021.
    [8]
    王艳东, 何国雄, 吴晨琛, 等. 基于匹配置信度的路网几何特征融合方法研究[J]. 测绘与空间地理信息, 2024, 47(1): 9-12. doi: 10.3969/j.issn.1672-5867.2024.01.004
    [9]
    陈亮. 道路网格网模式识别的关键指标设计[J]. 测绘与空间地理信息, 2024, 47(1): 193-195. doi: 10.3969/j.issn.1672-5867.2024.01.056
    [10]
    周筝, 龙华, 李帅, 等. 多因素下基于路网拓扑的电动汽车充电路径规划策略[J]. 四川大学学报(自然科学版), 2024, 61(1): 229-238.
    [11]
    李宏伟, 宋玉峰, 李帅兵, 等. 基于ArcGIS路网结构与交通拥挤度分析的电动汽车充电负荷预测方法[J/OL]. 电网技术, [2024-01-25]. https://doi.org/10.13335/j.1000-3673.pst.2023.1420.
    [12]
    刘纪平, 张用川, 徐胜华, 等. 一种顾及道路复杂度的增量路网构建方法[J]. 测绘学报, 2019, 48(4): 480-488. doi: 10.11947/j.AGCS.2019.20180419
    [13]
    温廷新, 李可昕, 赵琳琳, 等. 时变路网下电动汽车冷链配送路径规划研究[J]. 大连理工大学学报, 2022, 62(6): 641-649. doi: 10.7511/dllgxb202206012
    [14]
    LIU C, YIN H, SUN Y, et al. A grade identification method of critical node in urban road network based on multi-attribute evaluation correction[J]. Applied Sciences, 2022, 12(2): 813. doi: 10.3390/app12020813
    [15]
    ZHANG Y, LI X, ZHANG Q. Road topology refinement via a multi-conditional generative adversarial network[J]. Sensors, 2019, 19(5): 1162. doi: 10.3390/s19051162
    [16]
    CHEN C, YE Z, HU F, et al. Vehicle trajectory-clustering method based on road-network-sensitive features[J]. Journal of Intelligent & Fuzzy Systems, 2021, 41: 2357-2375.
    [17]
    周旦, 孙家煜, 顾国斌, 等. 基于轨迹数据的出租车司机寻客路径优化方法[J]. 重庆交通大学学报(自然科学版), 2024, 43(1): 83-90.
    [18]
    汪胡青, 孙知信. 基于Dijkstra算法的多约束多播路由算法的研究[J]. 计算机技术与发展, 2011, 21(12): 5-8. doi: 10.3969/j.issn.1673-629X.2011.12.002
    [19]
    QING G, ZHENG Z, YUE X. Path-planning of automated guided vehicle based on improved Dijkstra algorithm[C]//2017 29th Chinese Control and Decision Conference (CCDC). IEEE, 2017: 7138-7143.
    [20]
    SEDEÑO-NODA A, COLEBROOK M. A biobjective Dijkstra algorithm[J]. European Journal of Operational Research, 2019, 276(1): 106-118. doi: 10.1016/j.ejor.2019.01.007
    [21]
    MAKARIYE N. Towards shortest path computation using Dijkstra algorithm[C]//2017 International Conference on IoT and Application (ICIOT). IEEE, 2017: 1-3.
    [22]
    FAN D K, SHI P. Improvement of Dijkstra’s algorithm and its application in route planning[C]//2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery. IEEE, 2010, 4: 1901-1904.
    [23]
    NOTO M, SATO H. A method for the shortest path search by extended Dijkstra algorithm[C]//Smc 2000 Conference Proceedings. 2000 IEEE International Conference on Systems, Man and Cybernetics. IEEE, 2000, 3: 2316-2320.
    [24]
    阎逸飞. 基于缓冲区的道路中心线提取方法与应用[J]. 地理空间信息, 2023, 21(11): 36-38. doi: 10.3969/j.issn.1672-4623.2023.11.009
    [25]
    王小龙, 张黎明, 闫浩文, 等. 利用哈尔变换和高斯随机数进行矢量空间数据坐标加密[J]. 武汉大学学报(信息科学版), 2022, 47(11): 1946-1955.
    [26]
    陆文琦, 谷远利, 李萌, 等. 基于预处理的点到点最短路径计算方法[J]. 山东科学, 2018, 31(2): 64-71. doi: 10.3976/j.issn.1002-4026.2018.02.011
  • Related Articles

    [1]LIU Guohai, WAN Yalian, SHEN Yue, LIU Hui, HE Siwei, ZHANG Yafei. Complete coverage path planning of irregular convex field for the high clearance unmanned sprayer based on improved particle swarm optimizer algorithm[J]. Journal of South China Agricultural University, 2025, 46(3): 390-398. DOI: 10.7671/j.issn.1001-411X.202409017
    [2]WANG Pei, ZENG Sixiao, HE Jie. Simulation and verification for obstacle avoidance path tracking of unmanned agricultural machinery[J]. Journal of South China Agricultural University, 2024, 45(3): 416-426. DOI: 10.7671/j.issn.1001-411X.202308002
    [3]ZHANG Yali, MO Zhenjie, TIAN Haoxin, LAN Yubin, WANG Linlin. Path planning algorithm of agricultural robot based on improved APF-FMT*[J]. Journal of South China Agricultural University, 2024, 45(3): 408-415. DOI: 10.7671/j.issn.1001-411X.202305030
    [4]XIONG Chunyuan, XIONG Juntao, YANG Zhengang, HU Wenxin. Path planning method for citrus picking manipulator based on deep reinforcement learning[J]. Journal of South China Agricultural University, 2023, 44(3): 473-483. DOI: 10.7671/j.issn.1001-411X.202206024
    [5]YANG Chen, CHEN Jiyang, HU Qingsong, ZHANG Zheng, NIU Fengjie. Path planning of unmanned vehicle based on multi-objective PSO-ACO fusion algorithm[J]. Journal of South China Agricultural University, 2023, 44(1): 65-73. DOI: 10.7671/j.issn.1001-411X.202205005
    [6]LUO Xiwen, LIAO Juan, HU Lian, ZHOU Zhiyan, ZHANG Zhigang, ZANG Ying, WANG Pei, HE Jie. Research progress of intelligent agricultural machinery and practice of unmanned farm in China[J]. Journal of South China Agricultural University, 2021, 42(6): 8-17. DOI: 10.7671/j.issn.1001-411X.202108040
    [7]MA Quankun, ZHANG Yanfei, GONG Jinliang. Traversal path planning of agricultural robot based on memory simulated annealing and A* algorithm[J]. Journal of South China Agricultural University, 2020, 41(4): 127-132. DOI: 10.7671/j.issn.1001-411X.201911022
    [8]LIU Yufeng, JI Changying, TIAN Guangzhao, GU Baoxing, WEI Jiansheng, CHEN Kai. Obstacle avoidance path planning for autonomous navigation agricultural machinery[J]. Journal of South China Agricultural University, 2020, 41(2): 117-125. DOI: 10.7671/j.issn.1001-411X.201909010
    [9]PENG Xiaodong, LAN Yubin, HU Jie, OUYANG Fan, XIAO Kehui, GAO Zhizheng. Turning mode and whole region-coverage path planning and optimization of agricultural small UAV[J]. Journal of South China Agricultural University, 2019, 40(2): 111-117. DOI: 10.7671/j.issn.1001-411X.201805011
    [10]ZHANG Xinxin, XUE Jinlin. Human-machine cooperative path planning of an agricultural mobile robot based on a cloud model[J]. Journal of South China Agricultural University, 2017, 38(6): 105-111. DOI: 10.7671/j.issn.1001-411X.2017.06.016

Catalog

    Article views (74) PDF downloads (39) Cited by()

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return