用扩散模型生成高信息量路径，提升机器人搜寻效率。
DIFF-IPPO: Diffusion-Based Informative Path Planning with Open-Vocabulary Belief Maps
- 结合开放词汇感知与扩散模型，生成非高斯信念图上的最优路径。
- 在不同数据集上检测率达81.49%至86.55%，首次发现仅需3.5分钟。
- 适合复杂环境下的多机协同搜索任务，如搜救场景。
探索与物体搜索要求机器人感知环境、识别兴趣区域，并规划能提高目标检测概率或最大化信息增益的轨迹。许多信息路径规划方法，尤其在连续环境监测中，依赖高斯过程信念模型；而物体搜索场景常基于语义或开放词汇感知生成复杂、多模态的信念图。直接基于此类非高斯信念图进行全局轨迹生成仍相对未被充分研究。尽管扩散模型在建模此类分布方面表现出强能力，其在信息路径规划中的应用仍有限。本文提出DIFF-IPPO，一个将开放词汇信念图生成器与扩散模型规划器结合的流水线，用于在信念图上生成全局轨迹。该方法使传感器覆盖集中于高信念区域，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系统，无人机团队通过批量信念图条件化轨迹生成，仅用3.5分钟即完成首次发现。
原文摘要 · Abstract (English)
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.
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