Energy Efficient Scheduling for Position Reconfiguration of Swarm Drones

Abstract

Enhancing the energy efficiency of drones, particularly in extending the flight lifetime, has emerged as a crucial area. Position reconfiguration has been explored as a mechanism to achieve this goal for swarm drones. Building on this concept, we investigate how position reconfiguration can be applied within urban wind environments to further extend the lifetime of drone swarms. Despite its potential, efficiently implementing position reconfiguration remains challenging. To address it, we propose an efficient position reconfiguration scheme that reduces the energy consumption imbalance of the swarm and prolongs the lifetime. The scheme includes: (1) a MIP (mixed integer programming)-based optimization method. (2) an approximation algorithm that runs in pseudo-polynomial time and without the need for an optimization solver. The scheme provides a complete position reconfiguration solution that determines (i) the number of position reconfiguration; (ii) when to perform reconfiguration; (iii) who to change positions. Simulation and experimental results demonstrate the effectiveness of our scheme. Note to Practitioners—In urban environments, the significant variation in wind speeds leads to an energy imbalance among swarm drones performing tasks. This paper addresses the practical issue of extending the lifetime of drones in such environments by optimizing position reconfiguration. Specifically, drones operating in high wind speed areas require more energy to maintain hovering, resulting in faster battery depletion. By allowing drones with more remaining energy to exchange positions with those experiencing higher energy consumption, the overall energy usage can be balanced, thus extending the mission duration. We propose an energy-efficient scheduling scheme to determine when and which drones should reconfigure their positions. The scheme strikes a balance between the benefits of reconfiguration and the associated energy costs, preventing unnecessary movement that could waste energy while ensuring drones do not deplete their batteries prematurely. This solution is particularly suited for drone swarms operating in urban environments. Future research could further explore the integration of this scheme into real-time drone fleet management systems.