让机器人通过理解人话实现动态环境下的可靠导航
Language-guided Robust Navigation for Mobile Robots in Dynamically-changing Environments
- 用语义地图和障碍物对齐图解析人类自然语言反馈
- 实时检测环境变化并触发人机交互，提升路径鲁棒性
- 适合需要人机协作的农业、建筑等实地场景
本文构建了一个面向人机协同导航的具身智能系统，使用轮式移动机器人在动态环境中执行任务。提出一种直接有效的方法，通过监控机器人当前路径来检测显著影响其预期轨迹的环境变化，并向人类请求反馈。同时开发了一种将自然语言形式的人类反馈解析为局部导航航点，并利用语义特征地图与对齐的障碍物地图将其集成到全局规划系统中的机制。在仿真环境及配备资源受限硬件的真实世界场景中进行大量测试，验证了该方法的有效性与鲁棒性。本工作可支持精准农业、建筑施工等需持续监测环境状态的应用。
原文摘要 · Abstract (English)
In this paper, we develop an embodied AI system for human-in-the-loop navigation with a wheeled mobile robot. We propose a direct yet effective method of monitoring the robot's current plan to detect changes in the environment that impact the intended trajectory of the robot significantly and then query a human for feedback. We also develop a means to parse human feedback expressed in natural language into local navigation waypoints and integrate it into a global planning system, by leveraging a map of semantic features and an aligned obstacle map. Extensive testing in simulation and physical hardware experiments with a resource-constrained wheeled robot tasked to navigate in a real-world environment validate the efficacy and robustness of our method. This work can support applications like precision agriculture and construction, where persistent monitoring of the environment provides a human with information about the environment state.
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