SAFETY AND RELIABILITY ENHANCEMENT OF ROBOTIC SYSTEMS THROUGH ARTIFICIAL INTELLIGENCE: MATHEMATICAL MODELING APPROACHES

Abstract

The paper provides mathematical models for enhancing the safety and reliability of
robotic systems through the integration of artificial intelligence. The study explores
theoretical models to address primary issues in autonomous robotics, including
uncertainty, fault detection, and decision-making under constraints. The findings
indicate that hybrid mathematical models with Bayesian inference, Markov decision
processes, and neural network architectures have robust frameworks to be
implemented in safety-critical applications. The research contributes to the
theoretical foundation of intelligent robotics since it offers mathematical principles
that ensure operating reliability under computational efficiency.