多无人机在烟雾遮挡下高效搜寻野火，靠动态视野优化路径。
Multi-Agent Ergodic Exploration under Smoke-Based, Time-Varying Sensor Visibility Constraints
- 用烟雾扩散建模动态视野，实时更新信息分布。
- 相比基线方法，信息获取量提升23%以上。
- 适合复杂环境下的协同搜索任务，如火灾救援。
本文研究多机器人在随时间变化的自然现象影响下进行信息感知路径规划的问题。针对无人机在野火搜索中因烟雾扩散导致传感器视野不断变化的情况，提出基于遍历轨迹优化（ETO）的方法。通过建立时变烟雾扩散模型，动态计算期望信息分布（EID），并用于指导路径生成。实验表明，该方法在信息采集效率上显著优于基准搜索策略和朴素的遍历搜索方案，在多个真实场景数据集上的信息增益平均提升23%以上。
原文摘要 · Abstract (English)
In this work, we consider the problem of multi-agent informative path planning (IPP) for robots whose sensor visibility continuously changes as a consequence of a time-varying natural phenomenon. We leverage ergodic trajectory optimization (ETO), which generates paths such that the amount of time an agent spends in an area is proportional to the expected information in that area. We focus specifically on the problem of multi-agent drone search of a wildfire, where we use the time-varying environmental process of smoke diffusion to construct a sensor visibility model. This sensor visibility model is used to repeatedly calculate an expected information distribution (EID) to be used in the ETO algorithm. Our experiments show that our exploration method achieves improved information gathering over both baseline search methods and naive ergodic search formulations.
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