A Statistical Method Assessing the Influence of Cobots' Technical Parameters on Performance Indices

Coexisting-cooperative-cognitive (Tri-Co) robots are advanced systems designed for interaction with environ-ments, humans, and other robots. Collaborative robots (cobots), a subset of Tri-Co robots, specifically work safely alongside humans. When it comes to improving cobot performance, understanding the relationship between their technical parameters and performance indices becomes crucial. This paper proposes a method that combines the idea of experimental science and statistics, using Pearson correlation analysis to find this relationship. We define local Pearson correlation coefficients and influential indicators to measure each technical parameter's impact on performance indices. A case study on Rethink Sawyer (Sawyer) cobot validates our theoretical framework and underscores the practical applicability of our method.