Three-Arm Robotic Diagnostic Coordination Using Artificial Neural Network-Based Decision Support

Hariprasath Manoharan; Murugesh T.S; Abirami Manoharan; Durga R; Senthilkumar M

doi:10.35882/jeeemi.v8i3.1752

Hariprasath Manoharan Department of Electronics and Communication Engineering, Panimalar Engineering College, Poonamallee, Chennai, Tamil Nadu, India https://orcid.org/0000-0001-5034-3034
Murugesh T.S Department of Electronics and Communication Engineering, Government College of Engineering Srirangam, Trichy, Tamil Nadu, India https://orcid.org/0000-0003-1965-0225
Abirami Manoharan Department of Electrical and Electronics Engineering, Government College of Engineering Srirangam, Trichy, Tamil Nadu, India https://orcid.org/0000-0003-2199-3913
Durga R Department of Electronics and Communication Engineering, Government College of Engineering Srirangam, Trichy, Tamil Nadu, India https://orcid.org/0000-0001-8236-3398
Senthilkumar M

DOI: https://doi.org/10.35882/jeeemi.v8i3.1752

Keywords: Artificial Intelligence (AI), Medical Robots, Diagnosis, Neural Networks

Abstract

The growing demand for smart healthcare systems and increasing burden on healthcare professionals have necessitated the need for autonomous diagnostic technologies that can facilitate real-time clinical decision-making. Current robotic diagnostic systems are often limited to discrete tasks, including sensing, monitoring, and diagnostic support. This results in limited coordination, transparency, and decision-making capabilities. The aim of the proposed method is to design a three-arm diagnostic robot with Artificial Neural Network (ANN) intelligence to improve healthcare support. The proposed framework includes dedicated robotic arms for sensing, visualization, and diagnostic tool manipulation, along with a coordinated communication architecture. A decision-support module based on an ANN gathers diagnostic information from the different subsystems of a robot and offers intelligent diagnostic evaluations. A seven-axis coordination approach is implemented to improve the synchronous performance of robotic components and to reduce the operational liabilities during diagnostic operations. The proposed framework was evaluated with four scenarios, and the performance was assessed in terms of transparency, coordination efficiency, association error, diagnostic accuracy, sensing latency, and communication delay. The experimental results showed that the proposed system achieved a diagnosis accuracy of 93% versus 71% for the baseline method. Moreover, the framework achieved 93% of transparency rate, 85% of coordination efficiency, 12% of reduction of association error, a 40 ms sensing latency, and a 15 ms communication delay. Statistical analysis reported consistent performance with deviation values of 1.2%, 1.7%, and 1.3% for arm coordination, visualization, and diagnostic tool management, respectively. The results confirm that the combination of ANN-based decision support and synchronized multi-arm robotic work can significantly improve the diagnostic efficiency and the operational reliability. The proposed architecture provides a strong foundation for future intelligent healthcare systems and enables the development of autonomous robotic diagnostics for advanced medical applications

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