Multi-Agent Task Assignment and Planning (MATP) has attracted growing attention but remains challenging in terms of scalability, spatial reasoning, and adaptability in obstacle-rich environments. To address these challenges, we propose OATH: Adaptive Obstacle-Aware Task Assignment and Planning for Heterogeneous Robot Teaming, which advances MATP by introducing a novel obstacle-aware strategy for task assignment. First, we develop an adaptive Halton sequence map, the first known application of Halton sampling with obstacle-aware adaptation in MATP, which adjusts sampling density based on obstacle distribution. Second, we propose a cluster-auction-selection framework that integrates obstacle-aware clustering with weighted auctions and intra-cluster task selection. These mechanisms jointly enable effective coordination among heterogeneous robots while maintaining scalability and near-optimal allocation performance. In addition, our framework leverages an LLM to interpret human instructions and directly guide the planner in real time. We validate OATH in NVIDIA Isaac Sim, showing substantial improvements in task assignment quality, scalability, adaptability to dynamic changes, and overall execution performance compared to state-of-the-art MATP baselines. A project website is available at https://llm-oath.github.io/.

翻译：多智能体任务分配与规划（MATP）领域日益受到关注，但在可扩展性、空间推理以及障碍物密集环境中的适应性方面仍面临挑战。为应对这些挑战，我们提出OATH：异构机器人集群的自适应障碍感知任务分配与规划框架。该框架通过引入创新的障碍感知任务分配策略，推动了MATP领域的发展。首先，我们构建了自适应Halton序列地图——这是Halton采样在MATP中首次结合障碍感知自适应机制的应用，能够根据障碍物分布动态调整采样密度。其次，我们提出了集群-拍卖-选择三层框架，将障碍感知聚类、加权拍卖机制与集群内任务选择相融合。这些机制共同保障了异构机器人间的有效协同，同时保持了系统的可扩展性与近似最优的分配性能。此外，本框架利用大语言模型（LLM）解析人类指令，并实时指导规划器运行。我们在NVIDIA Isaac Sim仿真环境中验证了OATH系统，相较于当前最先进的MATP基线方法，本框架在任务分配质量、可扩展性、动态环境适应性及整体执行性能方面均展现出显著提升。项目网站详见https://llm-oath.github.io/。