Energy-aware Trajectory Planning Model for Mission-oriented Drone Networks

Abstract

The high mobility and easy deployment of drone networks encourage people to adopt this type of network for various projects, such as package delivery, systemic assessment, crisis control, border surveillance, etc., after equipped necessary sensors. However, the limited battery capacity largely constrains the operation time of drones. Elaborate and stringent planning is essential to succeed in mission execution energy-efficiently. We propose an energy-aware trajectory planning model for drones to accomplish all tasks in a mission-oriented network energyefficiently. Our focus in this study is on minimizing the energy spent on travel to save more energy for task execution. In our study, task lengths are not binary, which means that each task takes more than one time-unit to complete, and a drone may execute a portion of a task. To the best of our knowledge, our work is the first to introduce the energy spent on task execution to travel-cost minimization models, considering that both travel and task execution consume the battery power of drones. We also evaluate the performance of the proposed model. We found that the total-traveled distance of drones that follow the trajectories generated by the proposed model is significantly less than that of the drones that employ the strategy proposed in recent work regardless of the task length.