Abstract
The present application relates to the technical field of manipulators and provides an autonomous operation decision-making method of a picking manipulator. The autonomous operation decision-making method of a picking manipulator includes: acquiring sample images of fruits and branches and constructing a plurality of virtual scenes, where each virtual scene includes a picking manipulator model, a fruit model, and a branch model; in the virtual scene, determining azimuths of a target picking point and a target picking plane of an end effector model as parameters and inputting the parameters to a reward function; performing reinforcement learning training on the picking manipulator in the plurality of virtual scenes according to the reward function to determine an optimal picking action function; and controlling the picking manipulator to execute a picking task in an actual environment according to the optimal picking action function.