AUTHOR=Zhang Jiawei , Jackson Andrew , Mentzer Nathan , Kramer Rebecca TITLE=A Modular, Reconfigurable Mold for a Soft Robotic Gripper Design Activity JOURNAL=Frontiers in Robotics and AI VOLUME=4 YEAR=2017 URL=https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2017.00046 DOI=10.3389/frobt.2017.00046 ISSN=2296-9144 ABSTRACT=

Soft robotics is an emerging field with strong potential to serve as an educational tool due to its advantages such as low costs and shallow learning curves. In this paper, we introduce a modular and reconfigurable mold for flexible design of pneumatic soft robotic grippers. By using simple assembly kits, students at all levels are able to design and construct soft robotic grippers that vary in function and performance. The process of constructing the modular mold enables students to understand how design choices impact system performance. Our unique modular mold allows students to select the number and length of fingers in a gripper, as well as to adjust the internal geometry of the pneumatic actuator cavity, which dictates how and where bending of a finger occurs. In addition, the mold may be deconstructed and reconfigured, which allows for fast iterative design and lowers material costs (since a new mold does not need to be made to implement a design change). We further demonstrate the feasibility of the modular mold by implementing it in a soft robot design activity in classrooms and showing a sufficiently high rate of student success in designing and constructing a functional soft robotic gripper.