Large Language Models for Planning and Decision-Making in Autonomous Unmanned Systems

Dear Colleagues,

The rapid evolution of Foundation Models—particularly Large Language Models (LLMs) and their multimodal extensions—has opened a transformative frontier for autonomous unmanned systems. Traditional planning and decision-making frameworks often rely on rigid rule-based logic or classic optimization, which can struggle with zero-shot generalization in complex, unstructured environments. By integrating LLMs, unmanned platforms (such as ground vehicles, aerial robots, and marine vessels) gain unprecedented capabilities in open-vocabulary semantic reasoning, complex task decomposition, and intuitive human–system interaction. This Special Issue invites original research and review articles focusing on novel architectures, algorithmic frameworks, and edge-deployment strategies that bridge the gap between high-level cognitive reasoning and low-level physical control.

This Special Issue strictly emphasizes practical deployment and the seamless integration of LLMs with classic control and automation frameworks. Aligning with the core focus of the Automation journal, this Special Issue provides a dedicated platform for researchers and engineers working at the intersection of advanced artificial intelligence, control theory, and mechatronics. We highly encourage authors to submit works demonstrating how LLMs and cognitive AI paradigms can be practically integrated into automated systems. If your research addresses traditional bottlenecks in trajectory generation, dynamic task planning, system safety, or multi-agent coordination through large-model-driven approaches, we welcome your contributions to help shape the future of true intelligent automation.

Dr. Heng Shi
Dr. Yunjie Yang
Dr. Chenzhou Xu
Dr. Minchi Kuang
Dr. Zheng Chen
Prof. Dr. Jihong Zhu
Guest Editors

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