A fuzzy control algorithm for tracing air pollution based on unmanned aerial vehicles

Area of Science:

• Environmental Science
• Robotics and Control Systems
• Atmospheric Chemistry

Background:

• Atmospheric pollutant traceability using unmanned aerial vehicles (UAVs) is complex due to numerous influencing factors.
• Existing methods for odor plume tracking by UAVs face limitations in efficiency and accuracy.

Purpose of the Study:

• To propose and validate a novel fuzzy control traceability (FCT) algorithm for intelligent odor plume tracking by UAVs.
• To enhance the performance of UAV-based atmospheric pollutant detection systems.

Main Methods:

• Developed a fuzzy controller integrating UAV characteristics and environmental data for intelligent odor plume tracking.
• Implemented fuzzy control rules mimicking human decision-making to determine UAV movement (turning angle, move length).
• Compared the FCT algorithm against the bio-inspired "ZigZag" algorithm through experiments in various simulated concentration fields.

Main Results:

• The FCT algorithm demonstrated superior performance in odor plume tracking across different concentration fields.
• Experiments showed an average success rate of 95.4% higher for the FCT algorithm compared to the "ZigZag" algorithm.
• The study successfully applied fuzzy control logic to drone-based air pollutant traceability.

Conclusions:

• The proposed FCT algorithm offers an effective and intelligent solution for UAV-based odor plume traceability.
• Fuzzy control logic significantly improves the success rate and efficiency of pollutant tracking.
• Future work involves optimizing fuzzy control with multiple traceability subjects to address limitations of single-subject systems.