RISK ANALYSIS IN HUMAN-ROBOT INTERACTIVE ENVIRONMENTS

Abstract

As robotic systems gradually integrate into environments shared with humans, effective risk analysis in human-robot interactive settings has become necessary to guarantee optimized collaboration, and safety, and boost user acceptance. This review discovers methodologies for risk assessment in human-robot interactive (HRI), examining factors like physical proximity, dynamic unpredictability, and cognitive inconsistency amongst human users. This paper explores how sensor technologies, real-time data, and predictive algorithms can be influenced to alleviate risks related to robot faults, unpredicted human actions, and environmental dangers. The key discussion suggests that advanced sensor fusion, coupled with machine learning, allows robots to enhance, and adapt to human behavior, decreasing collision risks and refining response times. Moreover, adaptive risk modeling and continuous environment monitoring improve meaningfully the development of safer interactive systems. By suggesting an outline that assimilates risk analysis into the design and operation of communicating robots, this article aims to adopt safer, more intuitive human-robot environments in different applications, from manufacturing and healthcare to public spaces