Traditional robotic mechanisms contain a series of rigid links connected by rotational joints that provide powered motion, all of which is controlled by a central processor. By contrast, analogous mechanisms found in nature, such as octopus tentacles, contain sensors, actuators, and even neurons distributed throughout the appendage, thereby allowing for motion with superior complexity, fluidity, and reaction time. Smart materials provide a means with which we can mimic these features artificially. These specialized materials undergo shape change in response to changes in their environment. Previous studies have developed material-based actuators that could produce targeted shape changes. Here we extend this capability by introducing a novel computational and experimental method for design and synthesis of material-based morphing mechanisms capable of achieving complex pre-programmed motion. By combining active and passive materials, the algorithm can encode the desired movement into the material distribution of the mechanism. We demonstrate this new capability by de novo design of a 3D printed self-tying knot. This method advances a new paradigm in mechanism design that could enable a new generation of material-driven machines that are lightweight, adaptable, robust to damage, and easily manufacturable by 3D printing.

翻译：传统机器人机构包含一系列通过旋转关节连接的刚性连杆，由中央处理器控制其动力运动。相比之下，自然界中类似的机构（如章鱼触手）将传感器、执行器乃至神经元分布于整个附肢，从而实现具有卓越复杂性、流畅性和反应时间的运动。智能材料为人工模拟这些特征提供了途径——这些特殊材料会随环境变化而发生形态改变。以往研究已开发出能产生目标形变的材料基执行器，而本研究通过引入一种新颖的计算与实验方法，扩展了这一能力，用于设计和合成能够实现复杂预编程运动的材料基变形机构。该算法通过融合主动与被动材料，将期望运动编码至机构的材料分布中。我们通过从头设计一个3D打印的自系绳结来验证这一新能力。该方法推进了机构设计的新范式，有望催生新一代轻量化、自适应、抗损伤且易于通过3D打印制造的材料驱动型机器。