Autonomous robots exploring unknown areas face a significant challenge -- navigating effectively without prior maps and with limited external feedback. This challenge intensifies in sparse reward environments, where traditional exploration techniques often fail. In this paper, we introduce TopoNav, a novel framework that empowers robots to overcome these constraints and achieve efficient, adaptable, and goal-oriented exploration. TopoNav's fundamental building blocks are active topological mapping, intrinsic reward mechanisms, and hierarchical objective prioritization. Throughout its exploration, TopoNav constructs a dynamic topological map that captures key locations and pathways. It utilizes intrinsic rewards to guide the robot towards designated sub-goals within this map, fostering structured exploration even in sparse reward settings. To ensure efficient navigation, TopoNav employs the Hierarchical Objective-Driven Active Topologies framework, enabling the robot to prioritize immediate tasks like obstacle avoidance while maintaining focus on the overall goal. We demonstrate TopoNav's effectiveness in simulated environments that replicate real-world conditions. Our results reveal significant improvements in exploration efficiency, navigational accuracy, and adaptability to unforeseen obstacles, showcasing its potential to revolutionize autonomous exploration in a wide range of applications, including search and rescue, environmental monitoring, and planetary exploration.

翻译：自主机器人探索未知区域面临重大挑战——在没有先验地图且外部反馈有限的情况下有效导航。这一挑战在稀疏奖励环境中尤为严峻，传统探索技术在此类场景下往往失效。本文提出TopoNav，一种新型框架，使机器人能够突破这些限制，实现高效、适应性强且目标导向的探索。TopoNav的核心构建模块包括主动拓扑建图、内在奖励机制以及分层目标优先级排序。在探索过程中，TopoNav构建动态拓扑地图，捕捉关键位置与路径；利用内在奖励引导机器人前往地图中的指定子目标，即便在稀疏奖励设置下也能促进结构化探索。为确保高效导航，TopoNav采用分层目标驱动主动拓扑框架，使机器人在保持全局目标的同时，优先处理避障等即时任务。我们在模拟真实条件的仿真环境中验证了TopoNav的有效性。结果表明，该框架在探索效率、导航精度及对未知障碍的适应性上均有显著提升，展现了其在搜救、环境监测及行星探测等广泛应用中革新自主探索的潜力。