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Manufacturing, Control, and Performance Evaluation of a Gecko-Inspired Soft Robot

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Prominence-guided link prediction in fuzzy robotic networks.

D Joseph Jeyakumar¹, V Rajkumar², T P Dayana Peter³

¹Department of Electronics and Communication Engineering, Sri Muthukumaran Institute of Technology, Mangadu, Kanchipuram, Tamilnadu, India.

Scientific Reports

|December 19, 2025

Summary

This study introduces a modified Strength Prominence (SP) index for predicting interactions in complex cyber-physical systems (CPSs). The enhanced index improves early detection of failures and collaboration prediction in robotic networks.

Keywords:

Cyber-physical systems Fuzzy graph theory Link prediction Prominence degree Robotic interaction modeling Strength prominence index

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Area of Science:

Cyber-Physical Systems (CPSs)
Robotics and Intelligent Automation
Network Science

Background:

Increasing complexity in cyber-physical systems (CPSs) necessitates robust predictive models for dependable operations.
Existing fuzzy link prediction methods face challenges in accurately modeling uncertain interactions within robotic networks.
The Strength Prominence (SP) index, originally for social networks, requires adaptation for CPS applications.

Purpose of the Study:

To reformulate the Strength Prominence (SP) index for fuzzy interaction graphs in robotic and intelligent automation systems.
To enhance the prediction of link probabilities in cyber-physical systems (CPSs) by considering connectedness and prominence.
To validate the modified SP index's performance against traditional fuzzy indices for link prediction.

Main Methods:

Reformulation of the Strength Prominence (SP) index for fuzzy interaction graphs, where nodes represent robotic components and edges denote uncertain dependencies.
Assessment of link probability using connectedness strength and prominence levels, even without common neighbors.
Theoretical demonstration of properties like symmetry, boundedness, and monotonicity.

Main Results:

The modified SP index demonstrated superior predictive accuracy compared to traditional fuzzy indices (CN, RSM, CAR) on real-world and ROS-based robotic datasets.
Achieved higher precision, AUC (Area Under the Curve), and AUP (Area Under the Precision-Recall Curve) measurements.
Successfully identified interaction failures early and improved collaboration prediction.

Conclusions:

The adapted SP index offers a novel interdisciplinary tool for fuzzy link prediction in cyber-physical systems (CPSs).
This approach aids in the development of fault-tolerant designs and resilient network structures for autonomous systems.
Enables improved real-time robotic collaboration and enhances the dependability of complex robotic networks.