Configuration optimization of robot in-place machining system based on error prediction surrogate model

Industrial robots have become another important manufacturing equipment in addition to machine tools due to their advantages such as large working range and flexible working modes. However, compared with machine tools, larger errors restrict their application in precision-dependent scenarios. The robot's processing error is directly related to the system configuration. Under the same task, different system configurations will directly lead to different processing qualities. In the task scenario