Human–Robot Collaboration in Industrial Automation: Leveraging AI-Driven Interfaces to Enhance Productivity, Task Adaptability, and Workplace Safety

Abstract

The aim of this study is to examine how artificial intelligence (AI) driven interfaces can improve productivity, task adaptability and workplace safety for Human–Robot Collaboration (HRC) in industrial automation environments. Given that Industry 4.0 and emerging Industry 5.0 paradigms are expected to migrate industries towards flexible and responsive manufacturing systems, the integration of AI in collaborative robotics is becoming increasingly necessary. Based on a mixed method approach, this study compares the settings of conventional HRC with those augmented by AI interfaces based on natural language understanding, gesture recognition and predictive safety algorithms in real time, using a controlled experiment on a semi-automated assembly line. The quantitative data shows improved performance metrics: productivity growth of 23%, 36% reduction in task switching latency and 74% decrease in safety alerts. These findings were supported by qualitative interviews which revealed that workers trust AI, see AI as highly usable and perceive collaboration with AI to be effective. The triangulated results indicate that AI enhanced HRC is not only operationally better but also leads to higher human acceptance and trust in robotic systems. This thesis is part of a growing body of knowledge suggesting that cognitive and adaptive robotic systems will be fundamental to the future industrial workplace.