Selection of robot technology using q-rung normal fuzzy interaction based decision-making model

In this paper, we present novel approaches to multi-attribute decision-making problems using q-rung normal fuzzy numbers. The q-rung normal fuzzy sets are an important way to express uncertain information, and they are superior to the intuitionistic fuzzy sets, Pythagorean fuzzy sets and normal Pythagorean fuzzy sets. A combination of the q-rung fuzzy number and the normal fuzzy number becomes q-rung normal fuzzy numbers. The new averaging and geometric operations of q-rung normal fuzzy numbers are studied using the general aggregation function. There are both commutative, associative, idempotent and boundedness compatible using q-rung normal fuzzy numbers. There are four new aggregating operators presented such as q-rung normal fuzzy interaction weighted averaging, q-rung normal fuzzy interaction weighted geometric, q-rung generalized normal fuzzy interaction weighted averaging and q-rung generalized normal fuzzy interaction weighted geometric. Usually, the aggregation functions are taken to be the Euclidean distance and Hamming distance. The existence of these operators enables the development of algorithms that solve multi-attribute decision-making problems. We demonstrate the application of enhanced Euclidean and Hamming distances to problems arising in real-world situations. An important component of a robot sensor is computer science and machine tool technology. There are four factors that can be used to evaluate robotics systems: resolution, sensitivity, error and accuracy. In order to determine the best alternative, expert opinions should be compared to the criteria. The accuracy of these models is better and they are closer to q. Finally, we used some practical examples to illustrate the validity and superiority of the proposed method by comparing with other existing methods.