Research on path planning of tea picking manipulator based on improved DQN

In order to solve the problems of long picking paths, low efficiency and low picking quality caused by old leaves, stems and other interferences in the picking process of famous tea leaves, an improved deep reinforcement learning method based on target recognition is proposed. After the image target is pre-processed, the HIS color model is used to obtain target objects of different depths, the picking position of the shoot is obtained through the setting of parameter channels, the shape characteristics of the picking object are analyzed, and the speed, angular velocity, and distance error are used as the guiding factors of the reward function to realize the improvement of deep reinforcement learning. The path planning design of the picking process is accomplished by establishing objective functions, objective networks, and empirical recovery to achieve intensive training of the planned paths. Gazebo simulation platform is used to carry out reinforcement learning training of picking path, simulate obstacles to achieve the optimization of picking path, complete the verification of the planning algorithm, and get with the increase of training times, the improved deep reinforcement learning method is effective for picking path optimization, the localization cutting accuracy is controlled within 0.005 m, and the efficiency of path optimization is improved by 3.6%.