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<p>Transporting payloads using multiple drones is a promising approach for improving aerial delivery performance by distributing load and enhancing system resilience. However, coordinating multiple drones while ensuring energy-efficient operation remains a significant challenge. This study proposes a new formation-transition strategy for cooperative multi-drone payload transportation, integrating trajectory control, collision avoidance, and dynamic formation optimization. A generalized dynamic model is developed for <i>N</i> heterogeneous drones transporting a single payload, and a sliding mode control scheme is designed to ensure robust tracking performance. An exponential potential function is used for inter-drone collision avoidance, while a virtual-leader strategy coordinates the drones during formation transitions. To enhance energy efficiency, a Late Acceptance Hill Climbing algorithm is employed to determine the optimal formation-transition strategy. Additional evaluations confirm the scalability of the proposed framework up to five drones and its robustness under more realistic conditions, including aerodynamic drag and payload center-of-mass variations. Simulation results demonstrate that the proposed method reduces energy usage and improves thrust distribution balance, achieving an improvement of 45.6% compared to the system with no transition.</p>

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Formation Optimization of Heterogeneous Multi-drone Systems for Load Transport

  • 期刊论文

  • 发表日期：

    2026年1月4日

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  • Ardian Rizaldi,
  • Yoonsoo Kim

摘要

Transporting payloads using multiple drones is a promising approach for improving aerial delivery performance by distributing load and enhancing system resilience. However, coordinating multiple drones while ensuring energy-efficient operation remains a significant challenge. This study proposes a new formation-transition strategy for cooperative multi-drone payload transportation, integrating trajectory control, collision avoidance, and dynamic formation optimization. A generalized dynamic model is developed for N heterogeneous drones transporting a single payload, and a sliding mode control scheme is designed to ensure robust tracking performance. An exponential potential function is used for inter-drone collision avoidance, while a virtual-leader strategy coordinates the drones during formation transitions. To enhance energy efficiency, a Late Acceptance Hill Climbing algorithm is employed to determine the optimal formation-transition strategy. Additional evaluations confirm the scalability of the proposed framework up to five drones and its robustness under more realistic conditions, including aerodynamic drag and payload center-of-mass variations. Simulation results demonstrate that the proposed method reduces energy usage and improves thrust distribution balance, achieving an improvement of 45.6% compared to the system with no transition.