Empowering Robotic Decision-making with Complex Fuzzy Rough Sets: Toward Autonomous Industrial Applications

The ever-growing pace of developing industrial automation technologies and the growing complexity of robotic autonomous systems have raised the demand for complex decision-making models that can address complex information environments. This chapter aims to overcome this challenge by proposing a novel theoretical framework for enhancing robotic decisionmaking using CFRS, especially in the context of autonomous industrial environments where uncertainty, roughness, and complexity of information are inevitable. We introduce a novel suite of aggregation operators: complex fuzzy rough Frank power averaging (CFRFPA), complex fuzzy rough Frank power weighted averaging (CFRFPWA), complex fuzzy rough Frank power geometric (CFRFPG), and complex fuzzy rough Frank power weighted geometric (CFRFPWG) operators. These operators are intended for the management and summarization of CFR information in MCDM conditions. The theoretical background of these operators is well grounded, their basic characteristics and peculiarities of functioning are described. Based on these operators, we propose a complex MCDM methodology for the cases with the use of CFR information. As a proof of concept for our framework, we provide an extensive real-world example that is centered on the identification of robotic autonomous solutions for industrial uses. This chapter demonstrates how the proposed approach can be applied to solve practical problems in industrial automation decision-making where uncertainty and imprecision are inevitable. The usefulness of the proposed framework is also demonstrated by comparing it to the existing approaches in the area. The results clearly show that our approach outperforms the benchmark in terms of its ability to capture the interactions between uncertainty, roughness, and the underlying information complexity of the robotic autonomous system selection problem. This comparative analysis also shows that the proposed framework offers more comprehensive and accurate decision support for applications of industrial automation.