Session: 13-15-02: Mechanics of Soft Materials II

Paper Number: 173108

Physically Intelligent Soft Robotics 

Different from neuron-based computational intelligence through the brain, physical intelligence leverages structural designs and smart materials to physically encode sensing, actuation, control, adaption, and decision-making into the body of an agent. The stimuli-responsive body materials can enable autonomous sensory, actuation, powering, and other physical intelligence functions. The structural designs of soft body can simplify the required actuation for deformation and motion, as well as enable real-time feedback control-free locomotion and self-adaption.

I will discuss our recent work in embodying mechanical intelligence of structural designs and/or materials intelligence of soft active materials in soft robotics, for achieving high speed and high efficiency in locomotion, autonomy, and intelligence. First, I will talk about how to leverage snapping instabilities for achieving high-performance soft swimming robots and jumping devices. Spontaneous snapping stroke in the monostable flapping wing of a manta-like soft swimmer is utilized to achieve fast speed, high efficiency, and high maneuverability in a single soft swimmer while using simple actuation and control. The monostable wing is pneumatically actuated to instantaneously snap through to stroke down, and upon deflation, it will spontaneously stroke up by snapping back to its initial state, driven by elastic restoring force, without consuming additional energy. This largely simplifies designs, actuation, and control for achieving a record-high speed of 6.8 body length per second, high energy efficiency, and high maneuverability and collision resilience in navigating through underwater unstructured environments with obstacles by simply tuning single-input actuation frequencies. Second, I will discuss examples of integrating structural designs with soft active materials for achieving autonomy and intelligence in soft robots. We explored combining both geometric and materials intelligence in liquid crystal elastomer–based self-rolling robots for autonomous escaping from complex multichannel mazes without the need for human-like brain. Combining self-snapping for motion reflection, it shows unique curved zigzag paths to avoid entrapment, which allows for successful self-escaping from various challenging mazes, including mazes on granular terrains, mazes with narrow gaps, and even mazes with in situ changing layouts. We further showed that simply binding the two ends of the twisted ribbon forms a closed-loop twisted ring topology alongside a defect at the binding site, generating distinct self-motion modes. As opposed to linear motion in self-rolling twisted ribbons under constant thermal actuation, the defected twisted ring exhibits three periodic coupled self-flip–spin–orbit motion with programmed circular and re-programmed non-circular paths in free and confined spaces, respectively, arising from the defect-induced rotational symmetry breaking in the twisted ring topology.

Presenting Author: Jie Yin North Carolina State University

Presenting Author Biography: Dr. Jie Yin is currently a Professor in the Department of Mechanical and Aerospace Engineering at NC State University. Prior to join NC State, he was an Assistant and Associate Professor at Temple University. Dr. Yin received his Ph.D. in engineering mechanics from Columbia University. He is the recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE) from the White House, the Cozzarelli Prize from the National Academy of Sciences (NAS), NSF CAREER Award, and Extreme Mechanics Letters (EML) Young Investigator Award. His group’s current research focuses on mechanics guided design of soft robotics, mechanical metamaterials, and shape-morphing functional materials for sustainability.

Authors:

Jie Yin North Carolina State University