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Integration of geometric-based path tracking controller and its application in agricultural machinery automatic navigation
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Xinyu CUI1, Bingbo CUI1, 2, *, Zhen MA1, 2, Yi HAN1, Jianxin ZHANG1, Xinhua WEI1, 2
Journal of Intelligent Agricultural Mechanization | 2023, 4(3) : 24 - 31
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Journal of Intelligent Agricultural Mechanization | 2023, 4(3): 24-31
Integration of geometric-based path tracking controller and its application in agricultural machinery automatic navigation
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Xinyu CUI1, Bingbo CUI1, 2, *, Zhen MA1, 2, Yi HAN1, Jianxin ZHANG1, Xinhua WEI1, 2
Affiliations
  • 1.College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
  • 2.Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China
Published: 2023-08-15 doi: 10.12398/j.issn.2096-7217.2023.03.003
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Agricultural machinery autonomous navigation is an effective approach to achieve precision agriculture, and high-precision path tracking control is a critical assurance for the reliability of intelligent agricultural machinery autonomous operations. To improve the adaptability of path tracking algorithms under different path curvatures and initial error conditions, a geometric path tracking combined algorithm based on adaptive switching of two geometric path tracking methods was proposed. The steady-state errors and convergence characteristics of the pure pursuit (PP) and Stanley model in U-shaped path tracking were analyzed, and their optimal geometric path tracking parameters were respectively set at a constant velocity. A path tracking method switching logic was designed based on tracking errors and path curvature as input, and a boustrophedon path was used to test the geometric path tracking combined algorithm. A mobile cart platform was built to verify the effectiveness of the proposed algorithm. The tracking results showed that when the initial error is 2.5 meters, the Stanley model had a faster convergence rate than the PP algorithm. When operating at speeds of 1.0 m/s for straight paths and 0.7 m/s for curved paths, the PP and Stanley model exhibited similar tracking errors for straight segments, but when the curvature changed abruptly, the maximum tracking errors for the PP and Stanley model were 12.0 cm and 11.0 cm respectively. The path tracking combined algorithm effectively reduced the tracking error during curvature changes, with a maximum tracking error of 9.0 cm under the same speed configuration for the reciprocating shuttle path, representing a reduction of 25.0% and 18.2% compared to the PP and Stanley model respectively. The integration of geometric-based path tracking algorithm reduced the computational load and complexity of online optimization for forward distance and gain coefficient, and thus effectively improved the adaptability of path tracking algorithms for agricultural machinery under complex soil condition.

intelligent agricultural equipment  /  automatic navigation of agricultural machinery  /  geometric-based path tracking  /  Stanley model  /  pure pursuit algorithm
崔鑫宇, 崔冰波, 马振, 韩逸, 张建鑫, 魏新华. 几何路径跟踪组合算法及其农业机械自动导航应用. 智能化农业装备学报(中英文), 2023 , 4 (3) : 24 -31 . DOI: 10.12398/j.issn.2096-7217.2023.03.003
Xinyu CUI, Bingbo CUI, Zhen MA, Yi HAN, Jianxin ZHANG, Xinhua WEI. Integration of geometric-based path tracking controller and its application in agricultural machinery automatic navigation[J]. Journal of Intelligent Agricultural Mechanization, 2023 , 4 (3) : 24 -31 . DOI: 10.12398/j.issn.2096-7217.2023.03.003
Year 2023 volume 4 Issue 3
43
13
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Article Info
doi: 10.12398/j.issn.2096-7217.2023.03.003
  • Receive Date:2023-06-03
  • Online Date:2026-11-05
  • Published:2023-08-15
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  • Received:2023-06-03
  • Revised:2023-08-03
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    1.College of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
    2.Key Laboratory of Modern Agricultural Equipment and Technology of Ministry of Education, Jiangsu University, Zhenjiang 212013, China
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表12种不同金属材料的力学参数
科
Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)
属
Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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