发表机构
Beijing Institute of Technology（北京理工大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
CoRelNav提出了一种将多机器人探索与候选驱动协作验证相结合的方法，用于空间约束语义导航，在模拟和真实机器人上验证了其有效性。
AI 中文摘要
空间约束语义导航要求机器人识别目标，这些目标不仅由语义类别指定，还由与周围物体的关系指定。在未知环境中，解决此类目标需要高效探索以及足够的目标和上下文证据以进行可靠的关系验证。现有方法在很大程度上将关系感知验证与多机器人协作割裂开来：关系导航主要是单智能体的，而多机器人系统很少协调分布式观测以进行实例特定的关系验证。我们提出了CoRelNav，其核心是将任务条件化的多机器人探索与候选驱动的协作验证相结合。一个空间语义场将任务约束、场景节点和物体特征转换为探索效用；随着候选信息的积累，机器人在团队导航成本下被重新分配到互补证据处，同时跨拓扑节点聚合实例一致的观测。这种耦合减少了冗余搜索，并使得关系假设能够从独立的探索或孤立视图验证可能留下模糊性的分布式部分证据中得到解决。在照片级真实模拟中的实验表明，与代表性基线相比有一致的改进，消融实验验证了所提出的探索和验证机制。我们进一步在两台物理移动机器人上部署了完整系统，展示了其在真实世界协作导航中的适用性。
英文摘要
Spatially constrained semantic navigation requires robots to identify targets specified not only by semantic categories but also by relations to surrounding objects. In unknown environments, resolving such goals requires efficient exploration together with sufficient target and contextual evidence for reliable relation verification. Existing methods leave relation-aware verification and multi-robot collaboration largely disconnected: relational navigation is predominantly single-agent, while multi-robot systems seldom coordinate distributed observations for instance-specific relation verification. We propose CoRelNav, whose core is coupling task-conditioned multi-robot exploration with candidate-driven collaborative verification. A spatial-semantic field converts task constraints, scene nodes, and object features into exploration utility; as candidate information accumulates, robots are reallocated toward complementary evidence under team navigation costs, while instance-consistent observations are aggregated across topology nodes. This coupling reduces redundant search and enables relation hypotheses to be resolved from distributed partial evidence that independent exploration or isolated-view verification can leave ambiguous. Experiments in photorealistic simulation demonstrate consistent improvements over representative baselines, with ablations validating the proposed exploration and verification mechanisms. We further deploy the complete system on two physical mobile robots, demonstrating its applicability to real-world collaborative navigation.
Comments8 pages, 6 figures, 3 tables, Submitted to ICRA 2027
