University of Louisville
Simulated Industrial Assembly: Robot vs. Paper Instructions
Grade Level at Time of Presentation
Sophomore
Major
Computer Science and Engineering
Minor
Psychology
Institution 23-24
University of Louisville
KY House District #
42
KY Senate District #
35
Faculty Advisor/ Mentor
Dr. Karla Welch, PhD
Department
Computer Science and Engineering
Abstract
With the ever-increasing demand for goods and services, manufacturing companies constantly strive to manufacture products more efficiently. Recently, the incorporation of collaborative robots (i.e cobots) in a manufacturing setting had gained interest. In an ideal scenario where cobots focus on simple tasks, human energy would not be drained for mundane activities, allowing them to reserve their energy for more complicated problems.
Two main goals of this proposed research are to (1) Select performance metrics for human-in-the-loop activities utilizing multi-modal sensors for collaborative human-robot interfaces. As well as (2) Test biological sensors in the measurement of human intent estimation in an advanced manufacturing environment. Much of this experiment was heavily inspired by Funk et al. (2016). We hypothesize that physiological data will be a good measure of the cognitive workload that participant’s undergo while performing each task. Therefore, we asked participants to wear the Empatica E4 wristbands throughout the experiment to collect physiological data.
The research featured a balanced representation of genders in equal proportions. A random generator was used to balance order effects across participants. Participants completed predetermined assembly tasks using LEGOs with (1) Paper instructions (paper task) and (2) Cobot instructor (robot task using the NAO robot). This study used two surveys: (1) NASA TLX survey, to evaluate the cognitive workload associated with a task and (2) Godspeed and RoSAS questionnaires to evaluate participants' reactions to robots.
Improving human-robot interaction (HRI) will boost productivity, lead to more jobs, lower production costs, and overall increase economic growth. This will help create a thriving local economy in Kentucky. Leading research in HRI will make Kentucky a global influence in technology. The study's insights could be adopted globally, shaping future technologies and influencing international standards for responsible and inclusive human-robot collaboration.
Simulated Industrial Assembly: Robot vs. Paper Instructions
With the ever-increasing demand for goods and services, manufacturing companies constantly strive to manufacture products more efficiently. Recently, the incorporation of collaborative robots (i.e cobots) in a manufacturing setting had gained interest. In an ideal scenario where cobots focus on simple tasks, human energy would not be drained for mundane activities, allowing them to reserve their energy for more complicated problems.
Two main goals of this proposed research are to (1) Select performance metrics for human-in-the-loop activities utilizing multi-modal sensors for collaborative human-robot interfaces. As well as (2) Test biological sensors in the measurement of human intent estimation in an advanced manufacturing environment. Much of this experiment was heavily inspired by Funk et al. (2016). We hypothesize that physiological data will be a good measure of the cognitive workload that participant’s undergo while performing each task. Therefore, we asked participants to wear the Empatica E4 wristbands throughout the experiment to collect physiological data.
The research featured a balanced representation of genders in equal proportions. A random generator was used to balance order effects across participants. Participants completed predetermined assembly tasks using LEGOs with (1) Paper instructions (paper task) and (2) Cobot instructor (robot task using the NAO robot). This study used two surveys: (1) NASA TLX survey, to evaluate the cognitive workload associated with a task and (2) Godspeed and RoSAS questionnaires to evaluate participants' reactions to robots.
Improving human-robot interaction (HRI) will boost productivity, lead to more jobs, lower production costs, and overall increase economic growth. This will help create a thriving local economy in Kentucky. Leading research in HRI will make Kentucky a global influence in technology. The study's insights could be adopted globally, shaping future technologies and influencing international standards for responsible and inclusive human-robot collaboration.