Heterogeneous multi-robot systems are increasingly deployed in long-horizon missions that require coordination among robots with diverse capabilities. However, existing planning approaches struggle to construct accurate symbolic representations and maintain plan consistency in dynamic environments. Classical PDDL planners require manually crafted symbolic models, while LLM-based planners often ignore agent heterogeneity and environmental uncertainty. We introduce KGLAMP, a knowledge-graph-guided LLM planning framework for heterogeneous multi-robot teams. The framework maintains a structured knowledge graph encoding object relations, spatial reachability, and robot capabilities, which guides the LLM in generating accurate PDDL problem specifications. The knowledge graph serves as a persistent, dynamically updated memory that incorporates new observations and triggers replanning upon detecting inconsistencies, enabling symbolic plans to adapt to evolving world states. Experiments on the MAT-THOR benchmark show that KGLAMP improves performance by at least 25.5% over both LLM-only and PDDL-based variants.

翻译：异构多机器人系统在需要具有多样化能力机器人之间协调的长时程任务中日益广泛部署。然而，现有规划方法难以在动态环境中构建精确的符号表示并保持规划一致性。经典PDDL规划器需要手动构建符号模型，而基于LLM的规划器往往忽略智能体异构性与环境不确定性。本文提出KGLAMP——一种面向异构多机器人团队的知识图谱引导LLM规划框架。该框架维护一个编码物体关系、空间可达性与机器人能力的结构化知识图谱，用于引导LLM生成精确的PDDL问题描述。该知识图谱作为持续动态更新的记忆模块，能够整合新观测信息并在检测到不一致时触发重规划，从而使符号规划能够适应不断演化的世界状态。在MAT-THOR基准测试上的实验表明，KGLAMP相比纯LLM与基于PDDL的变体方法，性能提升至少25.5%。