Resource-aware Probability-based Collaborative Odor Source Localization Using Multiple UAVs

TL;DR

This paper introduces a resource-efficient multi-UAV odor source localization method that combines collaborative particle filtering and POMDP-based path planning, significantly improving search speed and success rate while minimizing resource use.

Contribution

The paper presents a novel multi-UAV collaboration framework integrating particle filtering and POMDP for efficient odor source localization under resource constraints.

Findings

Outperforms existing methods in search time and success rate

Reduces UAV resource consumption

Demonstrates effectiveness in simulation environments

Abstract

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.