用重心型沃罗诺伊图优化大规模机器人路径规划
SwarmCVT: Centroidal Voronoi Tessellation-Based Path Planning for Very-Large-Scale Robotics
- 用重心型沃罗诺伊图系统生成机器人群落的参考点
- 在复杂障碍环境中实现更高效、一致的路径规划
- 适合大规模机器人协同任务的研究者参考
群体机器人，或称超大规模机器人（VLSR），在执行复杂任务中具有重要应用。然而，随着机器人数量增加，运动控制复杂度和能耗迅速上升。此前研究采用宏观与微观相结合的方法，使微观机器人遵循宏观尺度上的高斯混合模型（GMM）分布，从而通过优化宏观层面实现整体最优。但这些方法需在无障区域内系统性地生成高斯成分（GCs）以构建GMM轨迹。本文利用重心型沃罗诺伊图（Centroidal Voronoi Tessellation）方法，系统化生成GCs，显著提升性能，同时保证路径规划的一致性与可靠性。
原文摘要 · Abstract (English)
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.
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