Jun Zhang: Collaborative soft robotic grippers

Jun Zhang


Collaborative soft robotic grippers


Jun Zhang


Mechanical Engineering

Bio sketch

Dr. Zhang joined the Department of Mechanical Engineering at the University of Nevada, Reno as an assistant professor in August 2018. His research interests lie in the intersection of control theory, robotics, smart materials and artificial muscles. In particular, he is interested in design, modeling, and control of smart materials and artificial muscles with applications to biomimetic soft robots, assistive robots and microelectromechanical systems.

He worked as a postdoctoral scholar at the University of California, San Diego from January 2016 to August 2018. He received the Ph.D. degree in electrical and computer engineering from Michigan State University in 2015, and the B.S. degree in automation from the University of Science and Technology of China, Hefei, China, in 2011. He was the recipient of the Student Best Paper Competition Award at the ASME Conference on Smart Materials, Adaptive Structures, and Intelligent Systems (SMASIS 2012), and the Best Conference Paper in Application Award at the ASME Dynamic Systems and Control Conference (DSCC 2013), and was named the Electrical Engineering Outstanding Graduate Student at Michigan State University for 2014-2015.

Project overview

There is an increasing need for versatile robotic grippers to collect samples in various unstructured environments, such as underwater, space, industrial warehouses, and home. Compared to their rigid counterparts, soft robotic grippers have shown strong promise to handle delicate or irregular objects, like fruits, padded envelopes, and stuffed plastic bags. Existing studies have predominantly focused on developing novel soft robotic grippers with unique actuation and control strategies. The goal of this project is to greatly enhance the existing grasping capabilities of soft grippers by having communication and coordination among soft grippers. This project will use the anthropomorphic soft grippers that were recently developed in our lab, which demonstrated strong performance in terms of dexterity and grasp force with low mechanical complexity and cost. The student will first enable communications between the soft robotic grippers. Then, the student will develop an algorithm to enable the soft grippers to work together to complete a challenging task, like carrying a tray with food. Lastly, the student will verify the effectiveness of the algorithm in experiments.