  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.
