通过模块化设计与共享边界驱动，实现柔性操作表面的可扩展精准抓取。
Scalable Surface-Based Manipulation Through Modularity and Inter-Module Object Transfer
- 采用共享边沿执行器和分层控制，减少90%以上执行器数量。
- 在2×2模块下实现1×1米范围内的亚厘米级定位与异形物体稳定传递。
- 适合食品加工、物流等需柔性处理脆弱物品的场景。
机器人操作表面（RMS）通过变形其承载表面来操控物体，能安全并行处理多样且易损物品。但现有设计存在根本矛盾：精细控制通常需要密集执行器阵列，限制了可扩展性。模块化架构虽可拓展工作区，但在柔软连续变形表面上跨模块可靠传递物体仍是难题。本文提出一个多模块软体操作平台，实现模块间协同物体转移与精确位置控制。通过冲突无解的曼哈顿路径规划、定向传递策略及几何PID控制器，达到亚厘米级定位精度，并稳定传递包括脆弱物品在内的异形物体。平台采用共享边界驱动，使$ n \times n $网格的执行器数从$4n^2$降至$(n + 1)^2$；一个$2\times 2$原型仅用9个执行器即可覆盖$1\times 1$平方米。该缩放带来代价：共享执行器造成邻近模块机械耦合，引发操作干扰。我们系统表征了不同空间配置下的耦合效应，并提出补偿策略，使被动物体位移减少59%–78%。这些贡献为软体操作表面在食品加工与物流等领域的应用奠定了可扩展基础。
原文摘要 · Abstract (English)
Robotic Manipulation Surfaces (RMS) manipulate objects by deforming the surface on which they rest, offering safe, parallel handling of diverse and fragile items. However, existing designs face a fundamental tradeoff: achieving fine control typically demands dense actuator arrays that limit scalability. Modular architectures can extend the workspace, but transferring objects reliably across module boundaries on soft, continuously deformable surfaces remains an open challenge. We present a multi-modular soft manipulation platform that achieves coordinated inter-module object transfer and precise positioning across interconnected fabric-based modules. A hierarchical control framework, combining conflict-free Manhattan-based path planning with directional object passing and a geometric PID controller, achieves sub-centimeter positioning and consistent transfer of heterogeneous objects including fragile items. The platform employs shared-boundary actuation, where adjacent modules share edge actuators, reducing the required count from $4n^2$ to $(n + 1)^2$ for an $n \times n$ grid; a $2\times 2$ prototype covers $1\times 1$ m with only 9 actuators. This scaling comes at a cost: shared actuators mechanically couple neighbouring modules, creating interference during simultaneous manipulation. We systematically characterise this coupling across spatial configurations and propose compensation strategies that reduce passive-object displacement by 59--78\%. Together, these contributions establish a scalable foundation for soft manipulation surfaces in applications such as food processing and logistics.
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