通过多模态思维链分数协作增强多机器人语义导航
Enhancing Multi-Robot Semantic Navigation Through Multimodal Chain-of-Thought Score Collaboration
AI总结：
针对家庭服务多机器人系统探索效率低、通信成本高的问题，提出多模态思维链协同导航（MCoCoNav）方法，结合VLMs与全局语义图实现高效协作导航，在HM3D_v0.2和MP3D上验证了有效性。
摘要：
理解人类如何协作利用语义知识探索陌生环境并决定导航方向，对家庭服务多机器人系统至关重要。以往方法主要聚焦于单机器人集中式规划策略，严重限制了探索效率。近期研究考虑了多机器人的分散式规划策略，为每个机器人分配独立的规划模型，但这些方法常忽略通信成本。在本研究中，我们提出多模态思维链协同导航（MCoCoNav），这是一种利用多模态思维链规划多机器人协作语义导航的模块化方法。MCoCoNav将视觉感知与视觉语言模型（VLMs）结合，通过概率评分评估探索价值，从而降低时间成本并实现稳定输出。此外，全局语义图被用作通信桥梁，在整合观测结果的同时最小化通信开销。在反映探索趋势的分数引导下，机器人利用该地图评估是探索新前沿点还是重访历史节点。在HM3D_v0.2和MP3D上的实验证明了我们方法的有效性。我们的代码可在https://github.com/FrankZxShen/MCoCoNav.git获取。
英文摘要：
Understanding how humans cooperatively utilize semantic knowledge to explore unfamiliar environments and decide on navigation directions is critical for house service multi-robot systems. Previous methods primarily focused on single-robot centralized planning strategies, which severely limited exploration efficiency. Recent research has considered decentralized planning strategies for multiple robots, assigning separate planning models to each robot, but these approaches often overlook communication costs. In this work, we propose Multimodal Chain-of-Thought Co-Navigation (MCoCoNav), a modular approach that utilizes multimodal Chain-of-Thought to plan collaborative semantic navigation for multiple robots. MCoCoNav combines visual perception with Vision Language Models (VLMs) to evaluate exploration value through probabilistic scoring, thus reducing time costs and achieving stable outputs. Additionally, a global semantic map is used as a communication bridge, minimizing communication overhead while integrating observational results. Guided by scores that reflect exploration trends, robots utilize this map to assess whether to explore new frontier points or revisit history nodes. Experiments on HM3D_v0.2 and MP3D demonstrate the effectiveness of our approach. Our code is available at https://github.com/FrankZxShen/MCoCoNav.git.
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。
