School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
Department of Mechanical Engineering, Massachusetts Institute of Technology Cambridge, MA 02139.
Proc Natl Acad Sci U S A. 2022 Oct 18;119(42):e2209819119. doi: 10.1073/pnas.2209819119. Epub 2022 Oct 10.
Grasping, in both biological and engineered mechanisms, can be highly sensitive to the gripper and object morphology, as well as perception and motion planning. Here, we circumvent the need for feedback or precise planning by using an array of fluidically actuated slender hollow elastomeric filaments to actively entangle with objects that vary in geometric and topological complexity. The resulting stochastic interactions enable a unique soft and conformable grasping strategy across a range of target objects that vary in size, weight, and shape. We experimentally evaluate the grasping performance of our strategy and use a computational framework for the collective mechanics of flexible filaments in contact with complex objects to explain our findings. Overall, our study highlights how active collective entanglement of a filament array via an uncontrolled, spatially distributed scheme provides options for soft, adaptable grasping.
在生物和工程机制中,抓取对夹持器和物体形态以及感知和运动规划非常敏感。在这里,我们通过使用一系列流体驱动的细长中空弹性细丝主动缠绕在几何和拓扑复杂性不同的物体上来避免对反馈或精确规划的需求。由此产生的随机相互作用使我们能够在一系列目标物体上实现独特的软且适应性强的抓取策略,这些目标物体在尺寸、重量和形状上各不相同。我们通过实验评估了我们策略的抓取性能,并使用了一个用于与复杂物体接触的柔性细丝集体力学的计算框架来解释我们的发现。总的来说,我们的研究强调了如何通过不受控制的、空间分布式的方案主动集体缠绕细丝阵列,为软的、适应性强的抓取提供了选择。