用树结构压缩形状记忆，让无人机群快速高效组装成指定形状
Memory-Efficient 2D/3D Shape Assembly of Robot Swarms
- 用分层树结构编码2D/3D形状，大幅降低内存占用
- 相比顶尖方法内存降10~100倍，形状进入速度提升2~4倍
- 适合需要低内存、高实时性的无人机集群系统
基于均值漂移的方法最近成为机器人集群形态组装的代表性技术，其核心是通过图像型目标形状表示计算局部密度梯度并进行均值漂移探索。然而，此类表示在高分辨率或3D形状下会产生显著的内存开销。为此，我们提出一种内存高效的树形表示，可对用户指定的2D和3D形状进行层次化编码。基于该表示，设计了一种无需分配的分布式行为控制器，实现免分配的形态组装。与当前最优的均值漂移算法相比，2D和3D仿真显示内存使用降低一到两个数量级，形状进入速度提升两到四倍。6至7架无人机的物理实验进一步验证了其在真实场景中的可行性。
原文摘要 · Abstract (English)
Mean-shift-based approaches have recently emerged as a representative class of methods for robot swarm shape assembly. They rely on image-based target-shape representations to compute local density gradients and perform mean-shift exploration, which constitute their core mechanism. However, such representations incur substantial memory overhead, especially for high-resolution or 3D shapes. To address this limitation, we propose a memory-efficient tree representation that hierarchically encodes user-specified shapes in both 2D and 3D. Based on this representation, we design a behavior-based distributed controller for assignment-free shape assembly. Comparative 2D and 3D simulations against a state-of-the-art mean-shift algorithm show one to two orders of magnitude lower memory usage and two to four times faster shape entry. Physical experiments with 6 to 7 UAVs further validate real-world practicality.
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