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AI-powered robots contain many sensors, such as vision devices like 2D/3D cameras, vibration sensors, proximity sensors, accelerometers, and other environmental sensors. These sensors enable real-time sensing data to be obtained and analyzed. In this work, we aim to examine AI robotic systems based on a bipolar complex fuzzy soft set (BCFSS) with Power dombi aggregation operators (PDAO). Moreover, we aim to examine BCFS power dombi average AOs and BCFS power dombi geometric AOs. Three BCFS power dombi average (PDA) AOs are identified in this work, namely the BCFS power dombi weighted average (BCFSPDWA) AO, BCFS power dombi hybrid average (BCFSPDHA) AO and BCFS power dombi ordered weighted average (BCFSPDOWA) AO. Likewise, three types of BCFS power dombi geometric AOs have been identified, namely: BCFS power dombi weighted geometric (BCFSPDWG) AO, BCFS power dombi order-weighted geometric (BCFSPDOWG) AO and BCFS power dombi hybrid geometric (BCFSPDHG) AO. Subsequently, we will propose a numerical model for the proposed operators, as well as a multi-attribute decision-making (MADM) model that can be used to select the best robot in AI. Finally, we will compare the results of the numerical model with the prevailing outcomes in terms of supremacy and dominance.

Analysis and Applications of Bipolar Complex Fuzzy Soft Power Dombi Aggregation Operators for Robot selection in Artificial Intelligence