Enhancing Situational Awareness with Adaptive Firefighting Drones: Leveraging Diverse Media Types and Classifiers

Abstract

High-rise fires are among the largest threats to safety in modern cities, and autonomous drones with multi-modal sensors can be employed to enhance situational awareness in such unfortunate disasters. In this paper, we study the fine-grained measurement selection problem for drones being dispatched to perform situation monitoring tasks in high-rise fires. Our problem considers multiple sensor/media types, classifier designs, and measurement locations, which were overlooked in prior waypoint scheduling studies. For concrete discussion, we adopt window openness as the target situation, while other situations can be readily supported by our solution as well. More specifically, we: (i) develop diverse window openness classifiers, (ii) mathematically formulate the fine-grained measurement selection problem and solve it using two algorithms, and (iii) create a photo-realistic simulator and an event-driven simulator to evaluate our algorithms. The evaluation results demonstrate that our proposed algorithms achieve higher classification accuracy (up to 50% improvement), deliver more feasible solutions (up to 100% improvement), and reduce energy consumption (up to 6.78 times reduction), compared to the current practices.