Uncertainty-Aware Multi-Agent Robotic Path Planning in Industrial Internet of Things using Complex Fermatean Threshold Neutrosophic Graphs

Abstract

 In highly dynamic and uncertain environments such as those enabled by the 
Industrial Internet of Things (IIoT), multi-agent robotic systems require robust and 
expressive models to support intelligent decision-making under indeterminate 
conditions. To this end, we aim to contribute a foundational structure toward uncertainty
aware autonomous operations in IIoT environments. In particular, we present a new 
Complex Fermatean Threshold Neutrosophic Graph (CFTNGs) to offer richer 
representations of uncertainty through complex-valued memberships governed by 
Fermatean norms. With this design, CFTNGs allow modeling of both magnitude and 
phase information related to robot perception and communication. Our approach 
provides a thoughtful exploration of CFTNGs through providing a formal definition of 
key graph operations, structural properties, and algebraic transformations, along with 
new aggregation and scoring operators suitable for robot coordination tasks. To validate 
the applicability of CFTNG, we introduce a comprehensive case study on multi-agent 
robotic path planning with a six-node IIoT-enabled factory floor. Our analysis 
demonstrates that CFTNGs provide significant improvements in representing dynamic 
connectivity and handling multi-source inconsistency in robot navigation.  

DOI: 10.5281/zenodo.16496376