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The integration of Artificial Intelligence (AI) and Machine Learning (ML) into autonomous robotics has heralded significant advancements in industrial applications, enhancing operational efficiencies, precision, and adaptability. This paper explores the transformative impact of AI and ML technologies on autonomous robotics in industrial settings, emphasizing the enhancements in automation and control mechanisms. Through a comprehensive literature review and analysis, we discuss the synergistic relationship between AI, ML, and robotics, and how this integration not only improves sensory and decision-making capabilities but also introduces adaptive learning and collaborative functionalities in robotic systems. Our findings reveal that AI-enhanced sensory technologies enable robots to perform complex recognition and manipulation tasks with unprecedented accuracy. Simultaneously, ML algorithms facilitate predictive maintenance, reducing downtime and extending the lifecycle of machinery. Moreover, adaptive learning capabilities allow robots to adjust to new environments and tasks without extensive reprogramming, showcasing significant flexibility and cost-efficiency. The deployment of AI and ML in robotics is not without challenges. The paper identifies key limitations such as data dependency, high computational demands, and adaptability issues. Ethical and societal implications, including job displacement and privacy concerns, are also critically examined to propose a balanced approach towards technology adoption. These include increased investment in R&D, the development of robust ML models, enhanced data governance frameworks, and the establishment of ethical standards to ensure responsible integration of these technologies into industrial practices. By addressing these challenges and leveraging collaborative efforts across sectors, the potential of AI and ML in revolutionizing industrial robotics can be fully realized, leading to a new era of manufacturing excellence.
Singh et al. (Sun,) studied this question.