DM$^3$-Nav：去中心化多智能体多模态多目标语义导航
DM$^3$-Nav: Decentralized Multi-Agent Multimodal Multi-Object Semantic Navigation
AI总结：
DM$^3$-Nav是一种去中心化的多智能体语义导航系统，支持多模态开放词汇目标指定和多目标任务，通过局部通信实现自主协调，验证了在真实办公环境中的有效性。
摘要：
我们提出了DM$^3$-Nav，一种完全去中心化的多智能体语义导航系统，支持多模态开放词汇目标指定和多目标任务。在我们的设置中，去中心化意味着没有中央协调器、全局地图聚合或共享的全局状态。机器人自主运行并通过随机配对通信协调，交换局部地图、目标状态和导航意图，无需同步。隐式的任务分配机制结合意图广播和距离加权前沿选择，减少冗余探索同时保持去中心化操作。在HM3DSem场景上使用HM3Dv0.2和GOAT-Bench数据集的评估表明，DM$^3$-Nav在匹配或超越集中式和共享地图基线的同时，消除了集中式架构中固有的单点故障。最后，我们通过两个移动机器人在真实办公环境中的验证，展示了完全依赖于机载感知和计算的成功部署。我们的现实实验视频可在在线获取：https://drive.google.com/file/d/1QiUSCn5rIvtuTUqtuXLPgmt6S8x9-MCZ/view?usp=drive_link
英文摘要：
We present DM$^3$-Nav, a fully decentralized multi-agent semantic navigation system supporting multimodal open-vocabulary goal specification and multi-object missions. In our setting, decentralization implies operation without a central coordinator, global map aggregation, or shared global state at runtime. Robots operate autonomously and coordinate through ad-hoc pairwise communication, exchanging local maps, goal status, and navigation intent without synchronization. An implicit task allocation mechanism combining intent broadcasting and distance-weighted frontier selection reduces redundant exploration while preserving decentralized operation. Evaluations on HM3DSem scenes using the HM3Dv0.2 and GOAT-Bench datasets demonstrate that DM$^3$-Nav matches or exceeds centralized and shared-map baselines while eliminating single points of failure inherent in centralized architectures. Finally, we validate our approach in a real-world office environment using two mobile robots, demonstrating successful deployment relying entirely on onboard sensing and computation. A video of our real-world experiments is available online: https://drive.google.com/file/d/1QiUSCn5rIvtuTUqtuXLPgmt6S8x9-MCZ/view?usp=drive_link
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
