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Robot learning for complex manufacturing process

机译：用于复杂制造过程的机器人学习

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At the present time, industrial robots for assembly tasks only constitute a small portion of the annual robot sales. One of the main reasons is that it is difficult for conventional industrial robots to adapt to the complicity and flexibility of assembly processes. Therefore, intelligent industrial robotic systems are attracting more and more attention. However, because of the modeling difficulty and low efficiency of the existing solutions, optimal performance is difficult to achieve. In this paper, a parameter learning method is developed based on Gaussian Process Regression Bayesian Optimization Algorithm (GPRBOA). Gaussian Process Regression(GPR) is utilized to model the relationship between the process parameters and system performance. GPRBOA is proposed to optimize the process parameters. The experiments were performed using a complex three stage torque converter assembly process. The experimental results verify the effectiveness of the robot learning method and demonstrate its efficiency compared to Design Of Experiment(DOE) methods. The proposed method can greatly increase the manufacturing efficiency and will generate big economic impact.
机译：目前，用于组装任务的工业机器人仅占年度机器人销售量的一小部分。主要原因之一是常规工业机器人很难适应组装过程的复杂性和灵活性。因此，智能工业机器人系统越来越受到关注。但是，由于现有解决方案的建模困难和效率低下，因此难以实现最佳性能。本文提出了一种基于高斯过程回归贝叶斯优化算法（GPRBOA）的参数学习方法。高斯过程回归（GPR）用于对过程参数与系统性能之间的关系进行建模。提出了GPRBOA来优化工艺参数。实验是使用复杂的三级变矩器组装过程进行的。实验结果验证了机器人学习方法的有效性，并证明了其与实验设计（DOE）方法相比的有效性。所提出的方法可以大大提高制造效率，并产生巨大的经济影响。

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