AN EXPLAINABLE EDGE-AI FRAMEWORK FOR COLLABORATIVE ROBOT MONITORING IN SMART CAMPUS INFRASTRUCTURE
Keywords:
artificial intelligence, edge AI, collaborative robots, smart campus, explainable monitoring, risk assessment, robotic decision support.Abstract
The digital transformation of campuses and organizations requires intelligent systems that can monitor people, robots and infrastructure in shared environments. This paper proposes an explainable edge-AI framework for collaborative robot monitoring in smart campus infrastructure. The framework combines multimodal sensing, edge-based perception, temporal risk inference, human-readable explanations and robotic action planning. Unlike conventional video surveillance or rule-based alarm systems, the proposed approach evaluates the level of risk, identifies the main warning causes and selects an appropriate robotic response such as speed reduction, route correction, notification or operator escalation. A scenario-based analytical evaluation shows that the model improves early recognition of unsafe situations while reducing false alarms compared with single-factor monitoring rules.
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