A Robust Multivariate Statistical Procedure for Evaluation and Selection of Industrial Robots

Abstract

Abstract Industrial robots are increasingly used by many manufacturing firms. The number of robot manufacturers has also increased, with many of these firms now offering a wide range of robots. A potential user is thus faced with many options in both performance and cost. Proposes a decision model for the robot selection problem using both a robustified Mahalanobis distance analysis, i.e. a multivariate distance measure, and principal‐components analysis. Unlike most other models for robot selection, this model takes into consideration the fact that a robot′s performance, as specified by the manufacturer, is often unobtainable in reality. The robots selected by the proposed model become candidates for factory testing to verify manufacturers′ specifications. Tests the proposed model on a real data set and presents an example.

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