GGrantIndex
← Search

Adding Mechatronic Experiential Learning into Mechanical Engineering Curriculum

$48,791FY2014EDUNSF

University Of Texas At Arlington, Arlington TX

Investigators

Abstract

This project at the University of Texas Arlington is working to develop an effective approach in robotics and mechatronics engineering education for students at this Hispanic-Serving Institution (HSI). The research focuses on improving retention in upper-level engineering courses and helping students to transfer and apply knowledge in the synthesis and construction of new designs. This work will develop an upper-level mechatronics course that combines theory with a series of hands-on investigations and projects. The experiential portion of the course will help students learn to use fundamental components of mechatronic systems such as actuators, motors, sensors, and processors. The project addresses the problem that upper-level engineering courses emphasize the theoretical and mathematical aspects of engineering science topics while often providing limited connection to actual hardware. The project also addresses a concern that students from underrepresented groups are less likely to acquire hands-on skills through informal means, and helps to ensure that these students develop experience working with critical hardware as part of the regular engineering curriculum. Project work will focus on the implementation of an experiential learning aspect to an Introduction to Robotics course. This course will be taken by junior and senior engineering students. Key mechatronic hardware topics to be included are: complex sensors such as ultrasonic range finders, 3-axis accelerometers, gyroscopes, infrared proximity sensors, ambient light sensors and encoders. Other devices incorporated will include actuators, stepper motors, geared motors, servo motors, and microprocessors. These experiments will correspond to theoretical topics in mechatronic systems such as dynamic simulation, closed-loop model-based control, sampling frequency and digital filtering. Evaluation will focus on the extent to which students who have completed the experiential upper-level robotics course are successful in transferring knowledge beyond the course to a subsequent capstone design course. It is anticipated that the experiential robotics course will enable students, in particular those from underrepresented minorities, to design and build more complex mechatronic systems than would otherwise be possible. Complexity will be evaluated based on a rubric which considers the number and sophistication of components utilized, development of appropriate data acquisition, design of signal processing hardware and software, system integration, and equality of contribution among team members.

View original record on NSF Award Search →