Mobile robots often rely on pre-existing maps for effective path planning and navigation. However, when these maps are unavailable, particularly in unfamiliar environments, a different approach become essential. This paper introduces DynaCon, a novel system designed to provide mobile robots with contextual awareness and dynamic adaptability during navigation, eliminating the reliance of traditional maps. DynaCon integrates real-time feedback with an object server, prompt engineering, and navigation modules. By harnessing the capabilities of Large Language Models (LLMs), DynaCon not only understands patterns within given numeric series but also excels at categorizing objects into matched spaces. This facilitates dynamic path planner imbued with contextual awareness. We validated the effectiveness of DynaCon through an experiment where a robot successfully navigated to its goal using reasoning. Source code and experiment videos for this work can be found at: https://sites.google.com/view/dynacon.

翻译：摘要：移动机器人通常依赖预存地图以实现有效路径规划与导航。然而，在陌生环境中缺乏地图时，亟需采用不同策略。本文提出DynaCon，一种新型系统，旨在为移动机器人提供导航过程中的环境感知与动态适应能力，从而消除对传统地图的依赖。DynaCon整合了实时反馈机制、对象服务器、提示工程及导航模块。通过利用大语言模型（LLMs），DynaCon不仅能理解给定数值序列中的模式，还擅长将物体分类至匹配空间，从而支持具备环境感知的动态路径规划。我们通过实验验证了DynaCon的有效性：机器人成功运用推理能力抵达目标。本工作源代码及实验视频可于以下网址获取：https://sites.google.com/view/dynacon。