Accidents involving hazardous gas leakage from chemical plants occur frequently, highlighting the critical importance of accurately locating air pollution sources. Rapid localization of pollution sources through the collaborative efforts of multiple unmanned aerial vehicles (UAVs) can help prevent major disasters. To address this, this paper presents an air pollution source tracking algorithm based on an improved particle swarm optimization (IPSO) algorithm. By enhancing the inertia weight coefficient, the search efficiency and accuracy of the UAV swarm are significantly improved. The simulation environment considers a Gaussian plume model, which incorporates various atmospheric constraints such as temperature and wind speed, for UAV swarm navigation. The simulation results demonstrate that employing the particle swarm optimization algorithm to guide UAVs in locating pollution sources enables them to accurately identify the sources within a short period, exhibiting a high level of effectiveness and robustness.
Multi-UAVs Cooperative Pollution Source Localization Based on Improved Particle Swarm Optimization Algorithm
13.10.2023
433321 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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