Air pollution represents a significant global challenge, and the precise identification and tracking of pollution sources is crucial for effective pollution control and management. Unmanned aerial vehicles (UAVs) possess inherent advantages due to their portability and the ability to integrate various sensors on demand, making them an ideal tool for this purpose. This study aims to develop an efficient multi- UAV system for pollution source tracking, termed a Multi-UAV Cluster Traceability Distributed (MCTD) control structure. The MCTD framework facilitates collaboration among multiple UAVs, expanding the coverage area and monitoring duration. Complementing this structure is the Dynamic Suppression Psychology (DSP) algorithm, inspired by the social impact theory, which simulates social interactions among UAVs. Each UAV adjusts its behavior based on the influence of other UAVs in the cluster, optimizing the tracking strategy. This approach enhances multi-UAV coordination, enabling more effective tracking and localization of airborne pollutants and overcoming single-UAV limitations in terms of coverage and duration. Experimental results show that tracking success rates significantly increase with the number of UAVs, reaching a saturation point at approximately 15 UAVs, with an approximate success rate of 85%. The MCTD-DSP system developed in this study effectively improves pollution source tracking efficiency, offering promising prospects for its application.
Recommendations for Resource Managers:
– A multi-UAV cluster traceability distributed (MCTD) control structure is established.
– A dynamic suppression psychological algorithm for multi-UAV based on the social impact theory is proposed.
– The increase in the number of UAVs can effectively improve the traceability efficiency.