Data and additional materials for the paper "A Geese-Inspired Energy-Aware Harmonization Algorithm for UAV Swarms"

Purpose. This study develops and evaluates a geese-inspired energy harmonization and leader rotation algorithm for UAV swarms, optimizing energy consumption and enhancing swarm coordination to extend mission endurance and improve operational efficiency. Design/methodology/approach. The research methodology involves a quantitative approach with controlled indoor and outdoor experiments to test the proposed algorithm, employing pre-test and post-test analysis, parallel form reliability testing, and statistical evaluations to assess battery consumption, energy harmonization effectiveness, leader rotation performance, and mission success. Findings. The study findings demonstrated that the proposed algorithm effectively balanced energy consumption across UAV swarms, improved leader-follower rotation efficiency, and extended mission endurance, with the algorithm showing significant performance improvements in both indoor and outdoor environments. Originality. This study introduces a geese-inspired energy harmonization and leader rotation algorithm that dynamically rotates leadership roles based on real-time battery levels, enhancing swarm coordination and endurance. Research limitations/implications. The research limitations are the dependence on controlled indoor and outdoor environments, which may not fully capture the complexities and challenges of real-world dynamic conditions, and the need for further validation with larger UAV swarms and extended operational scenarios. Practical implications. The practical implications of this research include the potential for enhancing the efficiency and operational range of UAV swarms in mission-critical applications such as search and rescue, environmental monitoring, and surveillance, by optimizing energy consumption and improving swarm coordination through the proposed algorithm.