AI 中文总结
本研究提出SGE框架，将语义分割融入路点采样与时域优化，通过仿真与实机实验验证其在非结构化环境探索中兼具体积覆盖竞争力与语义任务偏置能力，适配多平台多场景。
AI 中文摘要
本研究提出了语义引导探索（Semantically-Guided Exploration，SGE），这是一种面向地面车辆的模块化探索框架，它将像素级语义分割集成到基于采样的路点选择和后退时域路径优化中。与传统的几何探索方法不同，SGE 采用感知语义的效用函数直接在图像空间评估候选探索目标，该函数考虑了地形可通行性、障碍物 proximity、感兴趣对象以及基于深度的探索奖励。采样得到的路点被投影到三维空间，并通过实时旅行商问题（Traveling Salesman Problem，TSP）公式排序，从而实现后退时域目标选择。为解决现实世界的导航不确定性，该框架引入了多种机制，包括处理导航失败的临时禁忌区域，以及用于在已探索区域高效回溯的基于图的重定位策略。我们在标准化仿真基准测试中，将 SGE 与最先进的探索规划器进行对比评估，结果表明其在体积覆盖度方面具备竞争力，同时能够实现纯几何方法无法达成的语义任务偏置。该框架还通过在室内校园建筑、石灰岩矿和煤矿中使用多个机器人平台开展的现实世界实验得到验证，结果显示其在不同平台和领域均具备稳定的性能与适应性。
英文摘要
This work introduces Semantically-Guided Exploration (SGE), a modular exploration framework for ground vehicles that integrates pixel-level semantic segmentation into sampling-based waypoint selection and receding-horizon route optimization. Unlike conventional geometric exploration methods, SGE evaluates candidate exploration goals directly in the image space using a semantic-aware utility function that accounts for terrain traversability, obstacle proximity, objects of interest, and depth-based exploration reward. Sampled waypoints are projected into 3D and ordered through a real-time Traveling Salesman Problem (TSP) formulation, enabling receding-horizon goal selection. To address real-world navigation uncertainty, the framework introduces mechanisms, including temporary taboo regions to handle navigation failures and a graph-based relocation strategy for efficient backtracking across explored areas. We evaluate SGE in standardized simulation benchmarks against state-of-the-art exploration planners and demonstrate competitive performance in volumetric coverage, while enabling semantic task biasing that cannot be achieved by purely geometric methods. The framework is further validated through real-world experiments using multiple robotic platforms in indoor campus buildings and in limestone and coal mines. Results show consistent performance and adaptability across platforms and domains.
