Integration of geometric-based path tracking controller and its application in agricultural machinery automatic navigation

doi:10.12398/j.issn.2096-7217.2023.03.003

Abstract

Abstract:

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.

Key words: intelligent agricultural equipment, automatic navigation of agricultural machinery, geometric-based path tracking, Stanley model, pure pursuit algorithm

CLC Number:

CUI Xinyu, CUI Bingbo, MA Zhen, HAN Yi, ZHANG Jianxin, WEI Xinhua. 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.

Figures/Tables 17

Figure 1 Platform for automatic navigation system verification

Figure 2 Flowchart of automatic navigation for mobile vehicle

Figure 3 Schematic of vehicle bicycle model

Figure 4 Schematic of pure pursuit model

Figure 5 Schematic of Stanley model

Figure 6 Schematic of boustrophedon path

Figure 7 Path tracking result of Stanley model

Figure 8 Path tracking result of pure pursuit model

Table 1 Result of geometric-based path tracking algorithms

	PP L_d=0.85 m	PP L_d=1.30 m	Stanley K=0.65	Stanley K=0.75
最大偏差/cm	6.0	10.0	6.0	8.0
平均偏差/cm	-1.9	-4.7	-1.7	-2.8
标准差/cm	1.2	1.7	2.0	2.2

Figure 9 Flowchart of integration path tracking algorithm

Figure 10 Path tracking result of different algorithms

Table 2 U-shaped path tracking results of different algorithms

	Stanley	PP	Stanley+PP
最大偏差/cm	8.0	10.0	7.0
平均偏差/cm	-2.8	-4.7	-2.0
标准差/cm	2.2	1.7	2.2
上线距离/m	5.9	9.6	5.7

Figure 11 Error frequency histogram of integration path tracking for U-shaped path

Figure 13 Path tracking result of boustrophedon path

Figure 14 Lateral error of boustrophedon path tracking

Table 3 Result of different algorithms for boustrophedon path tracking

	Stanley	PP	Stanley+PP
最大偏差/cm	11.0	12.0	9.0
平均偏差/cm	-3.8	-5.3	-2.4
标准差/cm	2.3	1.3	2.3
上线距离/m	6.5	10.4	6.1

Figure 15 Error frequency histogram of integration path tracking for boustrophedon path

References 20

1	白学峰，常江雪，滕兆丽，等. 我国智能农业拖拉机关键技术研究进展［J］. 智能化农业装备学报（中英文）， 2022， 3（2）： 10-21.
	BAI Xuefeng， CHANG Jiangxue， TENG Zhaoli， et al. Research progress on key technologies of intelligent agricultural tractors in China ［J］. Journal of Intelligent Agricultural Mechanization （in Chinese and English）， 2022， 3（2）： 10-21.
2	张隆梅，刘岗微，齐彦栋，等. 农业机械无人驾驶系统关键技术研究进展（英文）［J］. 智能化农业装备学报（中英文）， 2022， 3（1）： 27-36.
	ZHANG Longmei， LIU Gangwei， QI Yandong， et al. Research progress on key technologies of agricultural machinery unmanned driving system ［J］. Journal of Intelligent Agricultural Mechanization （in Chinese and English）， 2022， 3（1）： 27-36
3	谢斌，武仲斌，毛恩荣. 农业拖拉机关键技术发展现状与展望［J］. 农业机械学报， 2018， 49（8）： 1-17.
	XIE Bin， WU Zhongbin， MAO Enrong. Development and prospect of key technologies on agricultural tractor ［J］. Transactions of the Chinese Society for Agricultural Machinery， 2018， 49（8）： 1-17.
4	ZHAO Jingjuan， YANG Yanping， ZHENG Huaiguo， et al. Global agricultural robotics research and development trend forecasts ［J］. Journal of Intelligent Agricultural Mechanization （in Chinese and English）， 2021， 2（1）： 28-35.
5	张漫，季宇寒，李世超，等. 农业机械导航技术研究进展［J］. 农业机械学报， 2020， 51（4）： 1-18.
	ZHANG Man， JI Yuhan， LI Shichao， et al. Research progress of agricultural machinery navigation technology ［J］. Transactions of the Chinese Society for Agricultural Machinery， 2020， 51（4）： 1-18.
6	崔冰波，魏新华，吴抒航，等. 基于动态路径搜索的农机自动驾驶软件系统研制［J］. 农机化研究， 2022， 44（10）： 228-232.
	CUI Bingbo， WEI Xinhua， WU Shuhang， et al. Development of agricultural machinery self-driving software system based on dynamic path search ［J］. Journal of Agricultural Mechanization Research， 2022， 44（10）： 228-232.
7	熊璐，杨兴，卓桂荣，等. 无人驾驶车辆的运动控制发展现状综述［J］. 机械工程学报， 2020， 56（10）： 127-143.
	XIONG Lu， YANG Xing， ZHUO Guirong， et al. Review on motion control of autonomous vehicles ［J］. Journal of Mechanical Engineering， 2020， 56（10）： 127-143.
8	李逃昌，胡静涛，高雷，等. 一种与行驶速度无关的农机路径跟踪方法［J］. 农业机械学报， 2014， 45（2）： 59-65.
	LI Taochang， HU Jingtao， GAO Lei， et al. Agricultural machine path tracking method irrelevant to travel speeds ［J］. Transactions of the Chinese Society for Agricultural Machinery， 2014， 45（2）： 59-65.
9	胡静涛，高雷，白晓平，等. 农业机械自动导航技术研究进展［J］. 农业工程学报， 2015， 31（10）： 1-10.
	HU Jingtao， GAO Lei， BAI Xiaoping， et al. Review of research on automatic guidance of agricultural vehicles ［J］. Transactions of the Chinese Society of Agricultural Engineering， 2015， 31（10）： 1-10.
10	TANG X H， PEI Z， YIN S， et al. Practical design and implementation of an autonomous surface vessel prototype： navigation and control ［J］. International Journal of Advanced Robotic Systems， 2020： 1-14.
11	YAO L J， PITLA S K， ZHAO C， et al. An improved fuzzy logic control method for path tracking of an autonomous vehicle ［J］. Transactions of the ASABE， 2020， 63（6）： 1895-1904.
12	张华强，王国栋，吕云飞，等. 基于改进纯追踪模型的农机路径跟踪算法研究［J］. 农业机械学报， 2020， 51（9）：18-25.
	ZHANG Huaqiang， WANG Guodong， LU Yunfei， et al. Agricultural machinery automatic navigation control system based on improved pure tracking model ［J］. Transactions of the Chinese Society for Agricultural Machinery， 2020， 51（9）： 18-25.
13	WANG R， LI Y， FAN J， et al. A novel pure pursuit algorithm for autonomous vehicles based on salp swarm algorithm and velocity controller ［J］. IEEE Access， 2020， 8： 166525-166540.
14	姚立健， Pitla Santosh K，杨自栋，等. 基于超宽带无线定位的农业设施内移动平台路径跟踪研究［J］. 农业工程学报， 2019， 35（2）： 17-24.
	YAO Lijian， Pitla SANTOSH K， YANG Zidong， et al. Path tracking of mobile platform in agricultural facilities based on ultra wideband wireless positioning ［J］. Transactions of the CSAE， 2019， 35（2）： 17-24.
15	COULTER R C. Implementation of the pure pursuit path tracking algorithm ［R］. Carnegie Mellon University， Pittsburgh， Pennsylvania， 1992.
16	肖世德，江海锋，杜金兰，等. 基于两阶段纯追踪模型的农用车辆路径跟踪算法研究［J］. 农业机械学报， 2023， 54（4）： 439-446.
	XIAO Shide， JIANG Haifeng， DU Jinlan， et al Path tracking algorithm of agricultural vehicle based on two stages pure tracking model ［J］. Transactions of the Chinese Society for Agricultural Machinery， 2014， 45（2）： 59-65.
17	WANG L， LIU M. Path tracking control for autonomous harvesting robots based on improved double arc path planning algorithm ［J］. Journal of Intelligent & Robotic Systems， 2020， 100（3-4）： 899-909.
18	THRUN S， MONTEMERLO M， DAHLKAMP H， et al. Stanley： The robot that won the DARPA grand challenge ［J］. Journal of Field Robotics， 2006， 23（9）： 661-692.
19	崔冰波，孙宇，吉峰，等. 基于模糊Stanley模型的农机全田块路径跟踪算法研究［J］. 农业机械学报， 2022， 53（12）： 43-48， 88.
	CUI Bingbo， SUN Yu， JI Feng， et al. Study on whole field path tracking of agricultural machinery based on fuzzy Stanley model ［J］. Transactions of the Chinese Society for Agricultural Machinery， 2022， 53（12）： 43-48.
20	王晟睿. 基于改进鲸鱼优化算法的Stanley算法研究［D］. 长春：吉林大学，2022.
	WANG Shengrui. Research on Stanley algorithm based on improved whale optimization algorithm ［D］. Changchun： Jilin University， 2022.

[1]	Bai Xuefeng, Chang Jiangxue, Teng Zhaoli, Lu Zhixiong. Research progress on key technologies of intelligent agricultural tractors in China [J]. Journal of Intelligent Agricultural Mechanization (in Chinese and English), 2022, 3(2): 10-21.
[2]	Wang Zhuoxuan, Wang Lihui, Xu Ninghui, Li Yong, Shi Jiachen. Research on the control strategy of sideslip and hysteresis of intelligent harvesting robot [J]. Journal of Intelligent Agricultural Mechanization (in Chinese and English), 2022, 3(2): 37-44.