Research on the Optimization of Action Execution Efficiency of Picking Robot Based on Machine Learning

In order to improve the execution efficiency and control precision of the action of the picking robot, it introduced the machine learning algorithm into the design of the control system of the picking robot. And it designed the machine learning method of the picking robot by using the mechanical learning and inductive learning, and the learning process is optimized by using the PID feedback regulation, thus it obtained the robot with higher efficiency with removing the action execution system. Taking the picking amount of fruits in the same time as the task, it carried out the simulation of the number of fruits picked by robots with different machine learning methods. The simulation results showed that more fruits can be picked in the same time by adopting the mechanical and inductive learning method and PID feedback adjustment method, which can effectively improve the execution efficiency of the picking robot action.