Swarm robotics, or very large-scale robotics (VLSR), has many meaningful applications for complicated tasks. However, the complexity of motion control and energy costs stack up quickly as the number of robots increases. In addressing this problem, our previous studies have formulated various methods employing macroscopic and microscopic approaches. These methods enable microscopic robots to adhere to a reference Gaussian mixture model (GMM) distribution observed at the macroscopic scale. As a result, optimizing the macroscopic level will result in an optimal overall result. However, all these methods require systematic and global generation of Gaussian components (GCs) within obstacle-free areas to construct the GMM trajectories. This work utilizes centroidal Voronoi tessellation to generate GCs methodically. Consequently, it demonstrates performance improvement while also ensuring consistency and reliability.

翻译：群体机器人学，或称超大规模机器人学，在复杂任务中具有许多重要应用。然而，随着机器人数量增加，运动控制的复杂性和能量成本迅速累积。针对此问题，我们先前的研究已提出多种采用宏观与微观方法相结合的方案。这些方法能使微观机器人遵循在宏观尺度观测到的高斯混合模型参考分布。因此，优化宏观层面将获得全局最优结果。然而，所有这些方法都需要在无障碍区域内系统且全局地生成高斯分量以构建GMM轨迹。本研究利用质心Voronoi剖分方法系统化生成高斯分量，从而在保证一致性与可靠性的同时实现了性能提升。