Exploration and object search require robots to perceive their environment, identify regions of interest, and plan trajectories that improve target-detection likelihood or maximize information gain. Many IPP methods, especially in continuous environmental monitoring, rely on Gaussian-process belief models, while object-search settings often produce complex, multimodal belief maps from semantic or open-vocabulary perception. Global trajectory generation directly conditioned on such non-Gaussian belief maps remains comparatively underexplored. Although diffusion-based planners offer strong capabilities for modeling such distributions, their use in informative path planning remains limited. In this work, we propose DIFF-IPPO, a pipeline that integrates an open-vocabulary belief map generator with a diffusion-based planner for global trajectory generation over belief maps. The method generates trajectories that concentrate sensor coverage over high-belief regions, achieving normalized detection scores between 81.49% and 86.55% across different dataset scenarios. We validate the system in a simulated search-and-rescue scenario where the planner searches candidate building regions to locate a burning building. In this setting, a team of five drones using batched belief-map-conditioned trajectory generation achieves first detections in 3.5 minutes.

翻译：探索与物体搜索要求机器人感知环境、识别感兴趣区域，并规划能提升目标检测概率或最大化信息增益的轨迹。许多信息路径规划（IPP）方法，尤其在连续环境监测中，依赖高斯过程置信模型，而物体搜索场景往往通过语义或开放词汇感知生成复杂的多模态置信图。直接以此类非高斯置信图为条件进行全局轨迹生成的研究仍相对不足。尽管基于扩散的规划器在建模此类分布时具有强大能力，但其在信息路径规划中的应用仍有限。本文提出DIFF-IPPO流水线，集成开放词汇置信图生成器与基于扩散的规划器，用于置信图上的全局轨迹生成。该方法生成的轨迹将传感器覆盖集中在高置信区域，在不同数据集场景下实现81.49%至86.55%的归一化检测得分。我们在模拟搜救场景中验证该系统：规划器搜索候选建筑区域以定位燃烧建筑物。在此场景中，五架无人机团队采用批处理的置信图条件轨迹生成方法，在3.5分钟内实现首次检测。