Trajectory planning of the robotic arm of oil tea fruit picking vehicles based on the golden jackal optimization algorithm

PENG Bo; LI Lijun; GAO Zicheng; LUO Hong; LIAO Kai; XIAO Shihui

doi:10.16578/j.issn.1004.2539.2025.12.014

Abstract：[Objective]Aiming at the problems of low efficiency and high labor intensity in manual picking of oil tea fruits and in order to realize the automation of the picking process,an oil tea fruit picking vehicle was designed.[Methods]Firstly,the robotic arm of the oil tea fruit picking vehicle was taken as the research object,and the Denavit-Hartenberg method was used to model it,followed by forward and inverse kinematic analysis.Then,the Matlab Robotics Toolbox was used to solve the limit workspace of the end effector gripper in Cartesian space using the Monte Carlo method.Due to the complex and varied application scenarios of traditional harvesting robotic arms,there are problems with trajectory planning,such as uneven joint curves and excessively long running times.An improved golden jackal optimization(IGJO)algorithm was proposed,which combined the particle swarm optimization algorithm with the golden jackal optimization(GJO)algorithm to improve the search strategy.Under the constraint of the velocity,acceleration and jerk of the robotic arm,the 5-5-5 polynomial interpolation function was introduced,and the golden jackal optimization algorithm of the fusion particle swarm optimization algorithm was combined to optimize the trajectory of the robotic arm.[Results]The simulation and test show that compared with the original golden jackal optimization algorithm,the IGJO algorithm reduces the total running time of the robotic arm by 17.8%,and the joint curve,velocity curve and acceleration curve of the robotic arm in joint space are smooth and stable.The high positioning accuracy proves that the IGJO algorithm is effective for the time-optimal trajectory planning of the robotic arm of the oil tea fruit picking vehicle.