Understanding the Role of Time-Varying Targets in Adaptive Distributed Area Coverage Control

Among the research community, interest is sparkling about the critical challenges of real-world applications where drones are deployed for tasks such as surveillance, environmental monitoring, and search-and-rescue missions. Within this context, coverage control can be seen as a basic behavior, a building block for complex and dynamic missions. Oftentimes, the environment is inherently dynamic, with variable points of interest subject to fluctuations in their importance and distribution. Additionally, the configuration of the drone fleet, including the number and positions of drones, may vary due to logistical constraints or mission-specific requirements. This experimental article presents the results of a comprehensive study focusing on a novel coverage algorithm based on Voronoi diagrams in the context of drone deployment. The primary objective is to empirically demonstrate the robustness of the proposed algorithm in real settings and dynamically changing environment with varying drone configurations. To substantiate our approach’s efficacy, we conducted an experimental study encompassing an extensive set of simulations, which were assessed using a predefined set of metrics. At the same time, the algorithm has been field tested employing a fleet of quad-copters in a series of proof-of-concept experiments that involved the deployment of up to seven aerial units.