Assembly Control Parameter Learning for Complex Robotic Assembly Processes

Recently robotic technology has been advanced rapidly. There are many robotic applications in manufacturing environments to replace human workers. However there are many unsolved problems in robotic automation. One issue is how to optimize a robotic manufacturing process. To face this challenge, this paper proposes a robot learning method to optimize process control parameters. The system performance including cycle and First Time Through rate can be optimized. Experimental platforms have been developed and experimental results demonstrate the proposed control parameter learning method is very effective compared to other existing methods. Hence the proposed method will make industrial robots more intelligent to meet the modern manufacturing demands in Industry 4.0.