Scalable Multi-Source Hazard Localization Using Consumer-Grade Drones in Urban Environments

Xiaoran Zhang, Yatai Ji, Yong Zhao, Chuan Ai, Bin Chen, Zhengqiu Zhu

Abstract: Consumer-grade drones equipped with low-cost sensors have emerged as a cornerstone of Autonomous Intelligent Systems (AISs) for environmental monitoring and hazardous substance detection in urban environments. However, existing studies predominantly focus on single-source search problems, neglecting the complexities of real-world scenarios where the number and location of hazardous sources are unknown. To address this gap, we propose the Dynamic Likelihood-Weighted Cooperative Infotaxis (DLW-CI) approach to enhance search efficiency and accuracy by integrating the Infotaxis strategy with optimized source term estimation and a dedicatedly-designed cooperative mechanism. Specifically, we introduce a multiple particle filter-based source term estimation method, in which each filter independently estimates the parameters of a potential unknown source, enabling scalable multi-source detection. Additionally, we develop a dynamic likelihood-weighted cooperative mechanism to prevent redundant estimation for the same source, thereby improving energy efficiency while expanding search coverage. Experimental results demonstrate that DLW-CI significantly outperforms baseline methods in terms of success rate, estimation accuracy, and root mean square error, particularly in scenarios with relatively few sources. The approach is further validated in a computational fluid dynamics (CFD)-generated diffusion scenario, confirming its robustness under realistic conditions. Our findings highlight the potential of DLW-CI to enhance environmental safety monitoring in smart city infrastructure, offering a scalable solution for multi-source detection using consumer drone networks.

External IDs:dblp:journals/tce/ZhangJZACZ25