Enabling Tunable Stiffness, Adhesive Grasping, and Interaction-Driven Reconfiguration: A Shape-Memory-Polymer-Enhanced Fin-Ray Gripper

作者：Haotian Guo, Haotian Guo, Hao Wu, Hao Wu, Yanzhe Wang, Yanzhe Wang, Yaoting Xue, Tuck‐Whye Wong, Tiefeng Li, Huixu Dong, Huixu Dong · 发表于：Soft Robotics · 年份：2025 · DOI：10.1177/21695172251381461 · 被引用次数：3 · 研究领域：Advanced Materials and Mechanics、Modular Robots and Swarm Intelligence、Advanced Sensor and Energy Harvesting Materials

Soft grippers offer a compelling solution for handling tasks in diverse environments due to their inherent safety and adaptability. However, enhancing their versatility, particularly in load capacity and grasping range, while minimizing actuation, remains a persistent challenge. To address this, we propose a soft gripper with reconfigurable morphology, combining structure (Fin Ray Effect [FRE] gripper), and intelligent material (shape memory polymers [SMPs]) as a union, to achieve tunable stiffness, adhesive grasping, and interaction-driven reconfiguration. First, SMPs are integrated into both the front and back beams of the FRE fingers, enabling adhesion grasping and grasping force modulation through phase transition, respectively. Additionally, by leveraging its shape-locking capabilities through intentional environmental interactions, the gripper achieves versatile reconfiguration with a single motor. Besides, inspired by humans interacting with tools and grasping in constrained spaces, we demonstrate three extra grasping modes, including precision pinching, hooking, and corner grasping. Experimental results validate its ability to handle diverse objects, from thin sheets and small nuts to items up to 50 times its own weight. This passive reconfigurable design allows for effective handling of disparate surfaces and contours, guaranteeing safe grasping in constrained spaces. This work opens new possibilities for soft robotic hands, balancing system simplicity with versatili...