关键词: 气振盘式, 机械臂, 深度强化学习, 奖励函数

Abstract: It is essential to improve the work efficiency of the vacuumvibration seeding assembly line by dynamically changing the established plan of the manipulator. According to the working characteristics of the existing vacuumvibration seeder, a motion planning method of the vacuumvibration seeding manipulator based on deep reinforcement learning was proposed. A Markov model was established by using the perception and action space. A reward function of process action matching was designed in combining the position and posture of the suction plate and the target action state, which guided the suction plate to move to the upper area of the assembly line and made the suction plate following the seedling tray to row seeds. V-REP was used to rebuild the seeding environment, and the manipulator action set reinforcement learning framework was established. The improved proximal strategy optimization algorithm could accelerate the convergence speed. Simulation experiments indicated that the absolute value of position error of the suction plate on X, Y, and Z axis was less than 2.5, 3.0, and 1.1 mm respectively. The absolute value of deflection angle error on each axis was less than 1.20°, 1.14°, and 1.28°. The maximum distance error was 4.5 mm. The average sowing period was 5.1 s. All the indexes of the method meet the design requirements, which can provide a basis for improving the efficiency of the vacuumvibration seeding assembly line.

Key words: vacuumvibration, manipulator, deep reinforcement learning, reward function