Shuyuan Wang
I am currently an MSE student in the Department of Mechanical Engineering at Johns Hopkins University (JHU), where I am advised by Prof. Noah J. Cowan. I am a member of the Locomotion in Mechanical and Biological Systems (LIMBS) laboratory, where I develop robotic system to explore the intersection of control theory and animal sensorimotor behavior.
I have earned my Bachelor's Degree in Robotics Engineering at Southern University of Science and Technology (SUSTech), where I was advised by Prof. Hongqiang Wang. I was a member of the Advanced Actuators & Robotics Lab (AAR Lab), where I worked on electroadhesion, soft robotics, deep learning, advanced actuation, etc.
Email / Curriculum Vitae / Scholar / GitHub / Instagram
Education
Johns Hopkins University (JHU) 09 / 2022 -
Department of Mechanical Engineering
Master of Engineering (expected)
Tsinghua University (THU) 06 / 2021 - 09 / 2021
Department of Mechanical Engineering
Visiting Student
Southern University of Science and Technology (SUSTech) 09 / 2018 - 07 / 2022
Department of Mechanical and Energy Engineering
Bachelor of Engineering
Selected Projects
The ultimate goal of my research has been, and remains, to augment human capabilities, with the intersection of robotics, deep learning, neuromechanics, and advanced actuation. As a college basketball player who suffered from two severe ACL surgeries, struggling with limited athletic abilities and painful recovery, I recognize the critical need to avoid and relieve the process with soft robots and exoskeletons. For the surgeons who operate on the boundaries of human capability, dealing with minuscule structures that are both fragile and hard to discern, I want to design delicate medical robots making the surgery easy, precise, and harmless. As a fish observer who explores incredible nature selection, reverse engineering thousands-of-year optimal solutions to the bio-inspired design, I literally need a robotic platform and intelligent algorithm to help track and analyze animal behaviors.
IEEE RoboSoft 2021 Conference | news
An obstacle course with fall, slope, terrain, and gap sections was set to showcase the motion capabilities and robustness of the soft robot. I designed a bionic cat tongue papillae structure and installed it on the robot's chassis to provide forward friction for locomotion on various surfaces. Then an origami-inspired leg with contraction and expansion was proposed to pass through the gap task. I wrote an embedded integrated control algorithm to realize robot gaits through sequent pneumatic actuation. I gradually realized the extraordinary athletic capacity performed by soft robots, and hence why they play an exclusive role in human performance enhancement, i.e., to gently convert mechanical force into organism motions.
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Our group were honored to win the locomotion competition of IEEE RoboSoft 2021 Conference at Yale University. It was of great pride to be interviewed by college media.
This project was carried out under Prof. Gang Wang at Tsinghua University. The ultimate goal was to design an automated production facility that matches the geometry feature with the existing heat treatment processes. I was exposed to state-of-the-art research on neural networks and taught to apply the surpassing computation capability of deep learning to robotic research by Prof. Tom Mitchell from Carnegie Mellon University. Then, based on the point cloud, a straightforward recognition network on 3D aerospace models was proposed while current deep learning strategies focused on 2D convolution. From my point of view, if desired, everything with less human intelligence, such as matching, prediction, and other repeatable work, could be handled by machine learning. After all, the substantive work implemented by learning is to obtain fitting functions from accumulated data. I would like to apply these principles again in the future to assist me in predicting human behaviors via experimental data and applying appropriate robotic strategies to various situations automatically.
Our group has developed a single-motor-actuated walking robot with eight legs capable of fast walking and maintaining good stability. We built a mathematical model of a six-linkage mechanism revealing the motion trajectory of the end of the legs. Motion analysis using MATLAB and Adams provided the information of kinematics and dynamics, i.e., the velocity and acceleration of the joints and the torque of the driving shaft. The trajectory parameters, including the stride length and step height, were introduced. Then, the DC motor and the transmission system parameters composed of gears and a hex roller were determined according to the required torque simulated by Adams. The model of the robot is designed and tested by the SOLIDWORKS motion module. Finally, the walking robot is assembled and tested on the flat floor.
This project aims to develop a transfer process based on a UBTECH walking robot. We utilized machine vision to locate the robot and the target. Expressly, we used ZhengYou Zhang Calibration Method to realize the mapping from the 3D world to the 2D image. Then QR codes were set to calculate the relative position and posture. I simulated the static walking gait on CoppeliaSim (V-REP) and proposed a new stride based on the linear inverted pendulum model. Both walking strategies performed well on the UBTECH robot.
Research
Grants
- Self-healing Mechanism of the Soft Electrostatic Adhesion Actuator
Sponsor: National Undergraduate Training Program for Innovation and Entrepreneurship
Number: 202114325012
PI: Shuyuan Wang
2021 - 2022, ¥ 20K - Mechanism and Processing Method of Stretchable Soft Electrostatic Adhesion Unit
Sponsor: "Climbing Program" Special Funds for the Cultivation of Guangdong Province College Students' Scientific and Technological Innovation Special Funds
Number: pdjh2021c0044
PI: Shuyuan Wang
2020 - 2022, ¥ 20K - InFiber Mach–Zehnder Interferometer Based on Er Doped Up-Taper and Peanut-Shaped Fiber Structure in Fiber Ring Laser
Sponsor: Collegial Undergraduate Training Program for Innovation and Entrepreneurship
Number: 2021X17
PI: Shengjie Zhou
2021 - 2022, ¥ 10K
Publications
- Liu, D., Li, G., Wang, S., Liu, Z., Wang, Y., Connolly, L., ... & Iordachita, I. (2024). An magnetic resonance conditional robot for lumbar spinal injection: Development and preliminary validation. The International Journal of Medical Robotics and Computer Assisted Surgery, 20(1), e2618.
PDF - Liu, Z., Wang, S., Liu, D., Li, G., & Iordachita, I. Autonomous Marker Localization for Lumbar Epidural Steroid Injection Robot. IEEE International Conference on Robotic Computing (IRC). IEEE. (to be submitted)
PDF - Wang, S., Usevitch, D., Armand, M., & Iordachita, I. The Continuum Loop Actuated Wire (CLAW) – a Low-Cost Disposable Manipulator for Subretinal Injection. International Conference on Robotics and Automation (ICRA). IEEE. (to be submitted)
- A Soft and Robust Electroadhesive Device
Yanzhou Fu, Shuyuan Wang, Dongliang Fan, and Hongqiang Wang
the 7th International Conference on Smart Materials and Nanotechnology in Engineering (SMN2019)
oral presentation | slides | PDF
Videos
In memory of Kobe Bryant, I made this video for GE2229 Public Speaking.
This was my first college game in freshman year. We (yellow) gave them (red) a severe lesson.
Haha!
Scholarships
2021
First Class of the Merit Student Scholarship (Top 2% at SUSTech, slides)
Excellent Student Service Scholarship (Top 5 of 150 Students)
2020
Progress Scholarship
Advanced Sports Team Scholarship
2019
Excellent Student Service Scholarship (Top 5 of 150 Students)
Popular Class Scholarship
Advanced Sports Team Scholarship
2018
Third Class of the Merit Student Scholarship (Top 10% at SUSTech)
Honors
2022
2021
2020
2019
2018