Task Planning and Execution for Industrial Automation: A Comprehensive Analysis of Traditional and Emerging Methods

This study provides a comprehensive analysis of task planning paradigms in industrial automation, evaluating different active and reactive methods like: Finite State Machines (FSMs), Behavior Trees, Hierarchical Task Networks, Symbolic Planning (PDDL), and Reinforcement Learning. It assesses the trade-offs of each method concerning scalability, reactivity, and verifiability. The core focus is the transformative role of generative AI, not as a standalone solution, but as an enabling technology that augments existing frameworks. Key generative AI applications include generating plans from natural language and creating synthetic training environments. The conclusive statement of the study is that the future of automation lies in hybrid deliberative-reactive architectures, such as combining PDDL and BTs, where AI enhances human expertise under rigorous verification.