文献类型：期刊文献

英文题名：LLM-DTS: Resilience Formation Control Via Semantic Reasoning and Adaptive Topology Switching

作者：Zhou, Xuanjie[1]; Zhang, Yafen[1]; Zhou, Zhao[1]; Xue, Dong[1]

机构：[1] East China University of Science and Technology, Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, Shanghai, 200237, China

年份：2026

起止页码：1552

外文期刊名：IEEE International Conference on Control and Automation, ICCA

收录：EI(收录号：20263321312257)

语种：英文

外文关键词：Adaptive control systems - Control system analysis - Industrial robots - Intelligent robots - Multipurpose robots - Navigation - Predictive control systems - Semantics - Topology

摘要：Achieving robust formation navigation for MultiRobot Systems (MRS) in complex and non-convex environments remains a fundamental challenge in modern robotics. Traditional Model Predictive Control (MPC) methods, which rely on fixed parameters and rigid topologies, are highly susceptible to local minima and navigation deadlocks when encountering dense obstacle traps or narrow apertures. To address these limitations, this paper proposes LLM-DTS, a hierarchical method that utilizes a Large Language Model (LLM) as a cognitive reasoner to perform environmental semantic analysis and dynamically reconfigure the underlying MPC topology and control parameters. The simulation results demonstrate that the proposed method increases navigation success rates across diverse scenarios and enables autonomous formation reshaping to traverse extreme spatial bottlenecks, enhancing the stability and environmental adaptability of the system. ? 2026 IEEE.