A student-focused course was created that provides students with a fundamental knowledge of cobot use and capabilities. Students learn to use a Universal Robots UR3e cobot, first with an exisiting free online training simulator. After completing the online training, students gain hands-on experience completing tasks with a cobot on a custom-built workstation. Two of these workstations were created. Two students trialed the program, and both reported enjoying the program and feeling significantly more confident in their cobot programming abilities.

The CNC mill is a highly valuable tool for engineering design, allowing for the creation of precise and complex metal parts. However, due to their high cost, many engineers do not have access to these machines until they enter industry, limiting the knowledge and experience of engineering students. This also restricts the level of engineering design they can participate in as they are limited to lower strength materials and processes. To expand the possibilities for engineering students, hobbyists, and small businesses, we created a reliable and affordable desktop CNC mill. Our machine is capable of cutting non-ferrous metals such as aluminum with 70?m repeatable part precision and be compatible with coolant and vacuum systems.

Visual odometry (VO) plays a crucial role in determining the position and orientation of an autonomous vehicle as it navigates through its environment. However, the performance of visual odometry can be significantly affected by errors in disparity estimation and LIDAR depth measurements. This thesis investigates the use of LIDAR depth correction and Stereo disparity matching, combined with stronger match filtering, to improve the accuracy and reliability of VO estimations. The study utilizes a dataset consisting of a sequence of image frames, ground truth position data, and a range of feature detection, description, and matching techniques. Results indicate that the proposed approach significantly improves the accuracy of VO estimations, providing a valuable contribution to the development of reliable and safe autonomous navigation systems. The proposed method consists of two main components: (1) an advanced disparity matching algorithm to obtain more accurate and robust disparity estimations, and (2) a LIDAR depth correction module that employs a sensor fusion approach to refine the depth information generated by LIDAR sensors. The LIDAR depth correction module combines data from multiple sensors, including LIDAR, camera, and inertial measurement unit (IMU), to produce a more accurate depth estimation. The performance of the proposed approach is evaluated using real-world datasets and benchmark visual odometry challenges. Results demonstrate that the proposed method significantly improves the accuracy and robustness of visual odometry, leading to better localization and navigation performance for autonomous vehicles. This research contributes to the ongoing development of autonomous vehicle technology by addressing critical challenges in visual odometry and offering a practical solution for more accurate and reliable self-localization

The goal of this thesis project is to work with children’s hospitals in the valley to develop unique exhibits or artifacts that would help children and families feel less anxious during hospital visits and stays. We would like these artifacts to encourage children to feel safe and happy by sparking fun moments that would evolve into cheerful memories. Overall, we created an LED Light Up Message Board, that will allow children the ability to reduce stress and help express themselves. We created the PCBs to make the letters light up and be easy enough to plug into the board so that the children can create messages on it.