Published Paper Details:

The convergence of Artificial Intelligence (AI) and industrial automation has created transformative opportunities in modern manufacturing systems. AI-driven approaches enable flexibility, adaptability, and autonomy in scheduling, task planning, and robotic motion control, which are critical in Industry 4.0 environments. This paper synthesizes insights from three major contributions: AI-based combined scheduling and motion planning in flexible robotic assembly lines, task planning of cooperating low-cost mobile manipulators, and advanced AI-driven task and motion planning of robotic assembly operations. By analyzing these studies, we present a comprehensive overview of methodologies, applications, challenges, and future research directions. The study highlights how AI techniques such as reinforcement learning, deep learning, hybrid symbolic-neural models, and distributed cooperative planning are redefining automation. Applications across flexible manufacturing lines, cooperative multi-robot systems, and intelligent robotic assembly operations are examined. Finally, challenges such as computational complexity, safety, interoperability, and sustainability are addressed, alongside opportunities for future research in digital twins, edge AI, blockchain-enabled automation, and Industry 5.0 paradigms.