Optimizing industrial robot selection using novel trigonometric pythagorean fuzzy normal aggregation operators

Abstract

The modern world uses an increasing number of robots, notably service robots. Robots will be able to easily manipulate everyday objects in the future, but only if they are paired with planning and decision-making procedures that allow them to comprehend how to complete a task. This research presents new techniques to handling multi-attribute problem solving with trigonometric Pythagorean normal fuzzy numbers. The sine trigonometric Pythagorean fuzzy sets combine the concept of Pythagorean fuzzy sets with sine trigonometric functions to represent uncertainty in decision-making. It is feasible to combine trigonometric Pythagorean fuzzy numbers and normal fuzzy numbers to get trigonometric Pythagorean fuzzy normal numbers. In addition to the fundamental interaction aggregation operators, we define the trigonometric Pythagorean fuzzy normal numbers. The trigonometric Pythagorean fuzzy normal numbers satisfy the following properties: associative, distributive, idempotent, bounded, commutative and monotonicity. Four novel approaches are introduced such as weighted averaging, weighted geometric, generalized weighted averaging and generalized weighted geometric. These operators can be used in the development of a multi-attribute decision-making algorithm. We demonstrate how improved Euclidean and Hamming distances are used in practical situations. For industrial robots, the two most crucial elements are computer science and machine tool technology. The four criteria of weights, orientations, speeds and accuracy may be used to assess robotic systems. They are also more practical, easier to understand, and more adept at identifying the best answer more quickly. The effectiveness and accuracy of the models we are looking at are demonstrated by comparing many existing models with those that have been developed.