Generation Approach of Human-Robot Cooperative Assembly Strategy Based on Transfer Learning

In current small batch and customized production mode,the products change rapidly and the personal demand increases sharply.Human-robot cooperation combining the advantages of human and robot is an effective way to solve the complex assembly.However,the poor reusability of historical assembly knowledge reduces the adaptability of assembly system to different tasks.For cross-domain strategy transfer,we propose a human-robot cooperative assembly(HRCA)framework which consists of three main modules:expression of HRCA strategy,transferring of HRCA strategy,and adaptive planning of motion path.Based on the analysis of subject capability and component properties,the HRCA strategy suitable for specific tasks is designed.Then the reinforcement learning is established to optimize the parameters of target encoder for feature extraction.After classification and segmentation,the actor-critic model is built to realize the adaptive path planning with progressive neural network.Finally,the proposed framework is verified to adapt to the multi-variety environment,for example,power lithium batteries.