The purpose of this creative project was to create a stereo sound system in a unique medium. As a team, we decided to integrate a Tesla Coil with a bluetooth audio source. These high frequency, high voltage systems can be configured to emit their electrical discharge in a manner that resembles playing tunes. Originally the idea was to split the audio into left and right, then to further segregate the signals to have a treble, mid, and base emitter for each side. Due to time, budget, and scope constraints, we decided to complete the project with only two coils.<br/><br/>For this project, the team decided to use a solid-state coil kit. This kit was purchased from OneTelsa and would help ensure everyone’s safety and the project’s success. The team developed our own interrupting or driving circuit through reverse-engineering the interrupter provided by oneTesla and discussing with other engineers. The custom interpreter was controlled by the PSoC5 LP and communicated with an audio source through the DFRobot Bluetooth module. Utilizing the left and right audio signals it can drive the two Tesla Coils in stereo to play the music.

With FDM printing becoming ubiquitous within the commercial and private sectors, there are many who would want to print a part without supports for a variety of reasons. Usually, they want to prints a part with difficult to reach places that would make it impossible to remove any support material without damaging the part. I will be going over options to consider when designing parts to ensure a given model will be able to be printed without support material.

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.

Cornhole, traditionally seen as tailgate entertainment, has rapidly risen in popularity since the launching of the American Cornhole League in 2016. However, it lacks robust quality control over large tournaments, since many of the matches are scored and refereed by the players themselves. In the past, there have been issues where entire competition brackets have had to be scrapped and replayed because scores were not handled correctly. The sport is in need of a supplementary scoring solution that can provide quality control and accuracy over large matches where there aren’t enough referees present to score games. Drawing from the ACL regulations as well as personal experience and testimony from ACL Pro players, a list of requirements was generated for a potential automatic scoring system. Then, a market analysis of existing scoring solutions was done, and it found that there are no solutions on the market that can automatically score a cornhole game. Using the problem requirements and previous attempts to solve the scoring problem, a list of concepts was generated and evaluated against each other to determine which scoring system design should be developed. After determining that the chosen concept was the best way to approach the problem, the problem requirements and cornhole rules were further refined into a set of physical assumptions and constraints about the game itself. This informed the choice, structure, and implementation of the algorithms that score the bags. The prototype concept was tested on their own, and areas of improvement were found. Lastly, based on the results of the tests and what was learned from the engineering process, a roadmap was set out for the future development of the automatic scoring system into a full, market-ready product.

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