DLW-CI: A Dynamic Likelihood-Weighted Cooperative Infotaxis approach for multi-drone cooperative multi-source search

Abstract

Drones have gradually been employed to search for unknown sources during leakage accidents. However, current studies have mainly focused on the single-source search problem, while in practical situations, the location and quantity of the sources are commonly unknown. Existing multi-source search methods fail to accurately estimate the source term, primarily due to the inefficient utilization of concentration information. This limitation results in sub-optimal drone movement strategies. To address these issues, we propose a Dynamic Likelihood-Weighted Cooperative Infotaxis (DLW-CI) approach. The approach integrates the Infotaxis cognitive search strategy with multi-drone cooperation by optimizing both source term estimation and the cooperative mechanism. Specifically, we devise a novel source term estimation method that leverages multiple parallel particle filters, with each filter estimating the parameters of a potentially unknown source in scenarios. Subsequently, we introduce a cooperative mechanism based on dynamic likelihood weight to prevent multiple drones from concurrently estimating and searching for the same source. The results show that the success rate for the localization of 2–4 diffusion sources reaches 90%, 78%, and 42% respectively when employing the DLW-CI approach, achieving a 37% average improvement over baseline methods. Our findings indicate that the proposed DLW-CI approach significantly improves estimation accuracy and search efficiency for multi-drone cooperative multi-source search, making a valuable contribution to environmental safety monitoring applications.