Efficient Battery Management in Maritime Surveillance: MUR-TAP Algorithm Approach

This study deals with the method of unmanned vehicles autonomously allocating their tasks for continuous monitoring of unidentified objects in the ocean. Existing literature primarily discusses methods where maritime surveillance systems perform distance-based centralized task allocation without considering network topology or drone battery status. From a theoretical perspective, the main goal has been to reduce the computational complexity of task allocation. However, this task allocation problem is challenging to apply in real systems due to constraints on battery life and the necessity of a central detection center. In this paper, we discuss performing battery-aware maritime surveillance missions using the C-ADMM based MUR-TAP algorithm. In maritime surveillance missions, drones autonomously exchange state information with neighboring drones when given a network topology. If the battery is low, they autonomously adjust their positions with drones located at charging stations on ships. Simulation results quantitatively demonstrate that drone batteries can be consistently maintained at high levels, enabling decentralized execution of maritime surveillance missions.