A Multi-UAV Indoor Air Real-Time Detection and Gas Source Localization Method Based on Improved Teaching–Learning-Based Optimization

Abstract

This paper presents an improved Teaching–Learning-Based Optimization Algorithm for controlling a UAV swarm to achieve real-time monitoring of indoor air quality and possible pollution source location search. The improvements to the Teaching–Learning-Based Optimization Algorithm focus on the following four points: proposing a variable teaching factor to improve search speed and accuracy, establishing an individual capability value model to integrate real-world UAV status with the algorithm, increasing the number of teachers and providing updated formulas to improve the algorithm’s convergence speed, and proposing a grouping strategy to achieve real-time monitoring of indoor air quality and pollution source search within a unified algorithm framework. At the end of the article, possible legal challenges for deploying this algorithm are also discussed.