Resource-aware probability-based collaborative odor source localization using multiple UAVs

doi:10.23919/JCC.ja.2023-0208

Resource-aware probability-based collaborative odor source localization using multiple UAVs

2025-12-30

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DOI：10.23919/JCC.ja.2023-0208

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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 (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 collaborative information to improve source estimation accuracy. In the following navigation phase, an adaptive path planning algorithm is designed based on partially observable Markov decision process to distributedly determine the subsequent flying direction and moving steps of each UAV. The results of experiments conducted on two simulation platforms demonstrate that MUC-OSL outperforms existing efforts in terms of mean search time and success rate, and effectively reduces the resource consumption of UAVs.

Keywords:

distributed path planning

multiple UAVs

odor source localization

source estimation

Resource-aware probability-based collaborative odor source localization using multiple UAVs

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