Autonomous robot person-following (RPF) systems are crucial for personal assistance and security but suffer from target loss due to occlusions in dynamic, unknown environments. Current methods rely on pre-built maps and assume static environments, limiting their effectiveness in real-world settings. There is a critical gap in re-finding targets under topographic (e.g., walls, corners) and dynamic (e.g., moving pedestrians) occlusions. In this paper, we propose a novel heuristic-guided search framework that dynamically builds environmental maps while following the target and resolves various occlusions by prioritizing high-probability areas for locating the target. For topographic occlusions, a belief-guided search field is constructed and used to evaluate the likelihood of the target's presence, while for dynamic occlusions, a fluid-field approach allows the robot to adaptively follow or overtake moving occluders. Past motion cues and environmental observations refine the search decision over time. Our results demonstrate that the proposed method outperforms existing approaches in terms of search efficiency and success rates, both in simulations and real-world tests. Our target search method enhances the adaptability and reliability of RPF systems in unknown and dynamic environments to support their use in real-world applications. Our code, video, experimental results and appendix are available at https://medlartea.github.io/rpf-search/.

翻译：自主机器人跟随系统在个人辅助与安防领域至关重要，但在动态未知环境中常因遮挡导致目标丢失。现有方法依赖预先构建的地图并假设环境静态，限制了其在真实场景中的有效性。当前在应对地形遮挡（如墙壁、拐角）与动态遮挡（如移动行人）条件下重新定位目标方面存在显著不足。本文提出一种新颖的启发式引导搜索框架，该框架在跟随目标过程中动态构建环境地图，并通过优先搜索目标高概率区域来解决各类遮挡问题。针对地形遮挡，构建信念引导搜索场以评估目标存在可能性；针对动态遮挡，采用流场方法使机器人能自适应地跟随或超越移动遮挡物。系统持续利用历史运动线索与环境观测优化搜索决策。实验结果表明，所提方法在仿真与真实场景测试中，其搜索效率与成功率均优于现有方法。本目标搜索方法增强了机器人跟随系统在未知动态环境中的适应性与可靠性，为其在实际应用中的部署提供支持。代码、演示视频、实验结果及附录详见 https://medlartea.github.io/rpf-search/。