Analysis of the Causal Structures Linking Process Variables to Robot Repeatability and Accuracy

Abstract

This article presents the results of an experimental investigation of how process variables affect robot performance in assembly operations. A linear structural relations (LISREL) model is developed to investigate the causal effects of load, speed, and distance traversed by a robot's gripper on the accuracy and repeatability of the robot's performance. The model analytically distinguishes between the direct and indirect effects that speed, weight, and distance traveled have on both accuracy and repeatability. Reciprocal effects among the performance criteria highlight the complexity of the task confronting those attempting to assess robot performance. They also demonstrate the inherent inadequacy of combining several individually derived bivariate assessments of a robot's performance to predict how well the robot will perform when confronted with a particular complex task. The excellent fit of the model demonstrates that the performance implications of altering task characteristics, and the complex interrelations among the multiple performance measures themselves can be effectively encapsulated by a LISREL model.