Research on an Intelligent Agricultural Machinery Unmanned Driving System

Haoling Ren (), Jiangdong Wu, Tianliang Lin, Yu Yao and Chang Liu
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Haoling Ren: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
Jiangdong Wu: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
Tianliang Lin: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China
Yu Yao: Mechatronic Engineering with the School of Beihang University, Beijing 102206, China
Chang Liu: College of Mechanical Engineering and Automation, Huaqiao University, Xiamen 361021, China

Agriculture, 2023, vol. 13, issue 10, 1-19

Abstract: Intelligent agricultural machinery refers to machinery that can independently complete tasks in the field, which has great significance for the transformation of agricultural modernization. However, most of the existing research on intelligent agricultural machinery is limited to unilateral research on positioning, planning, and control, and has not organically combined the three to form a fully functional intelligent agricultural machinery system. Based on this, this article has developed an intelligent agricultural machinery system that integrates positioning, planning, and control. In response to the problem of large positioning errors in the large range of plane anchoring longitude and latitude, this article integrates geographic factors such as ellipsoid ratio, long and short axis radius, and altitude into coordinate transformation, and combines RTK/INS integrated inertial navigation to achieve precise positioning of the entire vehicle over a large range. In response to the problem that existing full-coverage path planning algorithms only focus on job coverage as the optimization objective and cannot achieve path optimization, this paper proposes a multi-objective function-coupled full-coverage path planning algorithm that integrates three optimization objectives: job coverage, job path length, and job path quantity. This algorithm achieves optimal path planning while ensuring job coverage. As the existing pure pursuit algorithm is not suitable for the motion control of tracked mobile machinery, this paper reconstructs the existing pure pursuit algorithm based on the Kinematics characteristics of tracked mobile machinery, and adds a linear interpolation module, so that the actual tracking path points of motion control are always ideal tracking path points, effectively improving the motion control accuracy and control stability. Finally, the feasibility of the intelligent agricultural machinery system was demonstrated through corresponding simulation and actual vehicle experiments. This intelligent agricultural machinery system can cooperate with various operating tools and independently complete the vast majority of agricultural production activities.

Keywords: intelligent agricultural machinery; unmanned driving; vehicle positioning; full-coverage path planning; motion control (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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