Conducting collaborative experimental studies in undergraduate laboratories have become a challenge with the advent of the COVD-19 pandemic. It has been reported that numerous universities and colleges have switched their teaching to an online/remote format. With in-person activities halted and strict guidelines to maintain the spread low, both student’s adequate learning as well the progress of experimental research have been limited. This is particular true in engineering programs, where lab experience is intrinsically attached to the proper training of engineering students. Part of this training involves experience with experimental setup, data acquisition, measurement equipment operation, etc. (Some programs have resolved to providing students with numerical results in order for them to carry out the analysis and report write up.) In regards to original experimental research, halting ongoing work could have detrimental consequences in regards to progress and loss of previously acquired data. We present a case study of a solution to the aforementioned problem that has been successfully implemented in the Soft Robotics Research Lab (SRRL), in the School of Engineering at Liberty University. This remote lab setup developed at the onset of the COVID-19 pandemic provides insight as to how students can utilize existing technology in new ways to continue academic coursework and research. Prior to the pandemic, the SRRL had started machine learning research requiring extensive time to gather experimental data. The research work is aiming at controlling dielectric elastomer actuators using artificial intelligence techniques. Part of the challenges of controlling this highly nonlinear elastomeric materials is due to the large number of degrees of freedom and other mechanical behaviors exhibited by them (such as hysteresis, pseudo elasticity, etc.) With the pandemic stopping all in-person experiments, the group was provided with only two options: stop the experimental research or adapt to the new normal. The team began to develop new skills required to develop a remote testing apparatus. Using skills from the Engineering Sciences (e.g., mechanics, manufacturing, signal processing, controls, etc.), Networking, and Computer Science, the research team successfully developed a unique solution to allow for experimental research to continue with only one student present in the lab on campus and another student at a remote location (another city). This poster will present the challenges, solutions, and lessons learned by the SRRL group. I was observed that the students involved in this project had to learn skills outside of their major or expertise, exhibited resilience during this crisis and a challenge-driven attitude. In addition, it was found that the research group gained valuable experience in developing a networked experimental setup where remote students could connect to a computer controlling the required equipment to perform experiments, which can be implemented in some engineering program’s laboratory curriculum. Furthermore, by allowing students to take ownership of their challenging work, new ideas and opportunities for advancing engineering research and technology development could occur. It is believed that the use of the remote lab paradigm developed herein could aid to improve the overall experience of undergraduate students learning during situations similar to the current crisis. Finally, we outline future work and other challenges that could lead to improving the solutions presented in this case study.


Remote Lab, Soft Actuators, and Machine Learning: Experimenting During a Pandemic

Division / Sponsors: 
Aerospace, Bioengineering, Applied Mechanics, Design Engineering Division, Advanced Energy Systems, Manufacturing Engineering Division, Noise Control and Acoustics, Nondestructive Evaluation, Diagnosis, and Prognosis (NDPD), Nuclear Engineering Division, Process Industries Division, Fluids Engineering, Heat Transfer, Materials, Microelectromechanical Systems, Safety Engineering and Risk Analysis, Computers & Information in Engineering


Paper Proceedings: https://asmedigitalcollection.asme.org/IMECE/IMECE2020/volume/84478

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