Safflower Picking Trajectory Planning Strategy Based on an Ant Colony Genetic Fusion Algorithm

Hui Guo (), Zhaoxin Qiu, Guomin Gao, Tianlun Wu, Haiyang Chen and Xiang Wang
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Hui Guo: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Zhaoxin Qiu: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Guomin Gao: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Tianlun Wu: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Haiyang Chen: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China
Xiang Wang: College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China

Agriculture, 2024, vol. 14, issue 4, 1-17

Abstract: In order to solve the problem of the low pickup efficiency of the robotic arm when harvesting safflower filaments, we established a pickup trajectory cycle and an improved velocity profile model for the harvest of safflower filaments according to the growth characteristics of safflower. Bezier curves were utilized to optimize the picking trajectory, mitigating the abrupt changes produced by the delta mechanism during operation. Furthermore, to overcome the slow convergence speed and the tendency of the ant colony algorithm to fall into local optima, a safflower harvesting trajectory planning method based on an ant colony genetic algorithm is proposed. This method includes enhancements through an adaptive adjustment mechanism, pheromone limitation, and the integration of optimized parameters from genetic algorithms. An optimization model with working time as the objective function was established in the MATLAB environment, and simulation experiments were conducted to optimize the trajectory using the designed ant colony genetic algorithm. The simulation results show that, compared to the basic ant colony algorithm, the path length with the ant colony genetic algorithm is reduced by 1.33% to 7.85%, and its convergence stability significantly surpasses that of the basic ant colony algorithm. Field tests demonstrate that, while maintaining an S-curve velocity, the ant colony genetic algorithm reduces the harvesting time by 28.25% to 35.18% compared to random harvesting and by 6.34% to 6.81% compared to the basic ant colony algorithm, significantly enhancing the picking efficiency of the safflower-harvesting robotic arm.

Keywords: safflower harvesting; ant colony algorithm; parallel robotic arms; path planning (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: 2024
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