Method for achieving robot multi-axis-hole assembling through deep reinforcement learning

Abstract

The invention relates to a method for achieving robot multi-axis-hole assembling through deep reinforcement learning, and belongs to the technical field of robot assembling. The method comprises the steps that in the training process, common experience data and expert experience data which are produced through a traditional fuzzy force control method and a deep reinforcement learning network on the basis of a simulation model are added into an experience data set, experience data are randomly drawn from the experience data set to train the deep reinforcement learning network, and therefore theassembling action of the network can rapidly achieve the assembling level of the traditional fuzzy control method and can exceed the assembling effect of the traditional fuzzy control method if training continues to be carried out. The deep reinforcement learning network trained by the simulation model is directly used for a practical robot multi-axis-hole assembling task. The experience data produced by the simulation model are used for training, the problem that practical assembling environment cannot provide enough training data is solved, and meanwhile the training cost is reduced.

Description

technical field [0001] The invention relates to a method for realizing multi-axis hole assembly of a robot by using deep reinforcement learning, and belongs to the technical field of robot assembly. Background technique [0002] Under the trend of rapid development of intelligent manufacturing, robotic automatic assembly technology has a huge market demand, and has been more and more used in various assembly fields. Aiming at the large number of multi-axis hole assembly tasks that exist in the industry at present, the most used one is the one proposed by Zhang Kuangen of the Robotics and Automation Research Office of the Manufacturing Institute of the Department of Mechanical Engineering, Tsinghua University in his paper "Forcecontrol for a rigid dual peg-in-hole assembly" Based on the method of fuzzy force control, this method is based on the detailed analysis of the contact state of the shaft hole, and the contact force model under different states can be established, and ...

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Problems solved by technology

However, with this method, as the assembly model becomes more and more complex, such as when the assembly task of multi-axis holes needs to be completed at the same time, the contact state will increase exponentially and the jamming model cannot be accurately analyzed, which leads to the current situation. The traditional fuzzy control method is difficult to achieve stable control, and its versatility to different assembly environments is poor and its generalization ability is poor

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