An Intelligent Monitoring and Warning Framework in Drone Swarm Digital Twin Systems

Abstract

In drone swarms, where multiple drones collaborate closely to achieve shared objectives within constrained spatial domains, the intricacies of these interrelated actions can lead to potential issues. Despite rigorous pre-deployment planning, the inherent probability of complications persists. These complications stem from onboard computational resources, hardware failures, and network communication disruptions. While the malfunction of an individual drone may seem inconsequential, it can escalate into a substantial predicament when it disrupts the seamless coordination of the entire swarm. Therefore, the need to proactively monitor drones for predictive failure analysis and the subsequent examination of failed drones to mitigate future occurrences becomes imperative. This paper introduces a comprehensive framework for systematically collecting and processing data within drone swarms. The framework gathers critical information about onboard characteristics and communication metrics. These data points are subjected to advanced analysis using Complex Bayesian Networks to probabilistically uncover complex and hidden relationships between random features. The results demonstrate exceptional accuracy, with influences ranging from 99% to 79%, that ensures the reliability and effectiveness of the predictive capabilities in enhancing drone safety and network performance.