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...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.展开更多
基金supported by the National Natural Science Foundation of China 62173337Youth Independent Innovation Foundation of NUDT(ZK-2023-21).
文摘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.
