Benefitting from UAVs' characteristics of flexible deployment and controllable movement in 3D space, odor source localization with multiple UAVs has been a hot research area in recent years. Considering the limited resources and insufficient battery capacities of UAVs, it is necessary to fast locate the odor source with low-complexity computation and minimal interaction under complicated environmental states. To this end, we propose a multi-UAV collaboration based odor source localization (\textit{MUC-OSL}) method, where source estimation and UAV navigation are iteratively performed, aiming to accelerate the searching process and reduce the resource consumption of UAVs. Specifically, in the source estimation phase, we present a collaborative particle filter algorithm on the basis of UAVs' cognitive difference and Gaussian fitting to improve source estimation accuracy. In the following navigation phase, an adaptive path planning algorithm is designed based on Partially Observable Markov Decision Process (POMDP) to distributedly determine the subsequent flying direction and moving steps of each UAV. The results of experiments conducted on two simulation platforms demonstrate that \textit{MUC-OSL} outperforms existing efforts in terms of mean search time and success rate, and effectively reduces the resource consumption of UAVs.

翻译：得益于无人机在三维空间中灵活部署和可控移动的特性，多无人机气味源定位已成为近年来的研究热点。考虑到无人机资源有限且电池容量不足，需要在复杂环境状态下以低复杂度计算和最小化交互快速定位气味源。为此，我们提出一种基于多无人机协作的气味源定位方法（\textit{MUC-OSL}），该方法通过迭代执行源估计和无人机导航，旨在加速搜索过程并降低无人机的资源消耗。具体而言，在源估计阶段，我们基于无人机的认知差异和高斯拟合提出了一种协作粒子滤波算法，以提升源估计精度。在后续导航阶段，我们基于部分可观测马尔可夫决策过程（POMDP）设计了一种自适应路径规划算法，用于分布式确定每架无人机的后续飞行方向和移动步长。在两个仿真平台上进行的实验结果表明，\textit{MUC-OSL}在平均搜索时间和成功率方面均优于现有方法，并有效降低了无人机的资源消耗。