Mechatronics Bachelor Curriculum Development in Light of Industry 4.0 Technology Needs: Contrasting US and German University Curricula

This study compares Mechatronics bachelor curricula at universities in the United States of America and German universities. Mechatronics education is relatively new in the United States, but has been common in Germany for over a decade. With the multidisciplinary nature of technologies required by the 4’th industrial revolution, a.k.a. Industry 4.0, composing an appropriate Mechatronics curriculum becomes a challenge and an opportunity. This paper studies how Mechatronics education can address the future needs of industry, while building on a specific university’s strengths and industry links. We have also analyzed the new undergraduate Mechatronics program at Michigan Technological University (MTU) and compared its content to other US and German universities

Vision-based Online Defect Detection of Polymeric Film via Structural Quality Metrics

Nondestructive and contactless online approaches for detecting defects in polymer films are of significant interest in manufacturing. This paper develops vision-based quality metrics for detecting the defects of width consistency, film edge straightness, and specks in a polymeric film production process. The three metrics are calculated from an online low-cost grayscale camera positioned over the moving film before the final collection roller and can be implemented in real-time to monitor the film manufacturing for process and quality control. The objective metrics are calibrated to correlate with an expert ranking of test samples, and results show that they can be used to detect defects and measure the quality of polymer films with satisfactory accuracy

A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset

The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using a new underwater image dataset. The collected dataset is comprised of 100 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths

Visual feature extraction from dermoscopic colour images for classification of melanocytic skin lesions

The early diagnosis of Melanoma is a challenging task for dermatologists, because of the characteristic similarities of Melanoma with other skin lesions such as typical moles and dysplastic nevi. Aims: This work aims to help both experienced and non-experienced dermatologists in the early detection of cutaneous Melanoma through the development of a computational helping tool based on the “ABCD” rule of dermoscopy. Moreover, it aims to decrease the need for invasive biopsy procedure for each tested abnormal skin lesion. Methods: This is accomplished through the utilization of MATLAB programming language to build a feature extraction tool for the assessment of lesion asymmetry, borders irregularity, and colors variation in the tested lesion. Results: The helping tool obtained a sensitivity of 81.48%, a specificity of 52.83% and accuracy of 62.50% in the assessment of the Asymmetry Index. A new metric for the borders irregularity index was built. Finally, for the Colors Variation Index algorithm a sensitivity of 51.37%, a specificity of 61.51% and accuracy of 57.80% was achieved. Conclusions: This work created a computational tool based on the ABCD-rule, which is helpful for both experienced and non-experienced dermatologists in the early discrimination of Melanoma than other types of skin lesions and to eliminate the need of the biopsy procedure. A new metric for the Borders Irregularity Index was established depending on the number of inflection points in the lesion’s borders

Gesture Controlled Collaborative Robot Arm and Lab Kit

In this paper, a mechatronics system was designed and implemented to include the subjects of artificial intelligence, control algorithms, robot servo motor control, and human-machine interface (HMI). The goal was to create an inexpensive, multi-functional robotics lab kit to promote students’ interest in STEM fields including computing and mechtronics. Industrial robotic systems have become vastly popular in manufacturing and other industries, and the demand for individuals with related skills is rapidly increasing. Robots can complete jobs that are dangerous, dull, or dirty for humans to perform. Recently, more and more collaborative robotic systems have been developed and implemented in the industry. Collaborative robots utilize artificial intelligence to become aware of and capable of interacting with a human operator in progressively natural ways. The work created a computer vision-based collaborative robotic system that can be controlled via several different methods including a touch screen HMI, hand gestures, and hard coding via the microcontroller integrated development environment (IDE). The flexibility provided in the framework resulted in an educational lab kit with varying levels of difficulty across several topics such as C and Python programming, machine learning, HMI design, and robotics. The hardware being used in this project includes a Raspberry Pi 4, an Arduino Due, a Braccio Robotics Kit, a Raspberry Pi 4 compatible vision module, and a 5-inch touchscreen display. We anticipate this education lab kit will improve the effectiveness of student learning in the field of mechatronics

What Is Near?: Room Locality Learning for Enhanced Robot Vision-Language-Navigation in Indoor Living Environments

Humans use their knowledge of common house layouts obtained from previous experiences to predict nearby rooms while navigating in new environments. This greatly helps them navigate previously unseen environments and locate their target room. To provide layout prior knowledge to navigational agents based on common human living spaces, we propose WIN (\textit{W}hat \textit{I}s \textit{N}ear), a commonsense learning model for Vision Language Navigation (VLN) tasks. VLN requires an agent to traverse indoor environments based on descriptive navigational instructions. Unlike existing layout learning works, WIN predicts the local neighborhood map based on prior knowledge of living spaces and current observation, operating on an imagined global map of the entire environment. The model infers neighborhood regions based on visual cues of current observations, navigational history, and layout common sense. We show that local-global planning based on locality knowledge and predicting the indoor layout allows the agent to efficiently select the appropriate action. Specifically, we devised a cross-modal transformer that utilizes this locality prior for decision-making in addition to visual inputs and instructions. Experimental results show that locality learning using WIN provides better generalizability compared to classical VLN agents in unseen environments. Our model performs favorably on standard VLN metrics, with Success Rate 68\% and Success weighted by Path Length 63\% in unseen environments

Operation of a Controllable Force-sensing Industrial Pneumatic Parallel Gripper System

As part of the advanced programmable logic controllers (PLC) course at Michigan Tech, this class project was performed on a mechatronics system gifted by Donald Engineering, a Michigan-based supplier of industrial automation systems and components. This paper explores the functionality and application of a force-programmable and sensing pneumatic parallel gripper system. Force sensing is a critical part of many systems in modern automation systems. Applications such as prosthetics, robotic surgery, or basic manufacturing systems may rely on the ability to properly read and control forces applied to an object. This work evaluates the basic operation of the pneumatic force-sensing gripper system, through a human machine interface (HMI), and presents two demonstrations using programmable logic controllers to open the door for future customized developments. Different gripper force-time and pressure-time responses are presented to demonstrate the control and visualization of the grippers force

Assessment of Sentinel-2 and Landsat-8 OLI for Small-Scale Inland Water Quality Modeling and Monitoring Based on Handheld Hyperspectral Ground Truthing

This study investigates the best available methods for remote monitoring inland small-scale waterbodies, using remote sensing data from both Landsat-8 and Sentinel-2 satellites, utilizing a handheld hyperspectral device for ground truthing. Monitoring was conducted to evaluate water quality indicators: chlorophyll-a (Chl-a), colored dissolved organic matter (CDOM), and turbidity. Ground truthing was performed to select the most suitable atmospheric correction technique (ACT). Several ACT have been tested: dark spectrum fitting (DSF), dark object subtraction (DOS), atmospheric and topographic correction (ATCOR), and exponential extrapolation (EXP). Classical sampling was conducted first; then, the resulting concentrations were compared to those obtained using remote sensing analysis by the above-mentioned ACT. This research revealed that DOS and DSF achieved the best performance (an advantage ranging between 29% and 47%). Further, we demonstrated the appropriateness of the use of Sentinel-2 red and vegetation red edge reciprocal bands (1/(B4 X B6)) for estimating Chl-a (R2 = 0.82, RMSE = 14.52mg/m3). As for Landsat-8, red to near-infrared ratio (B4/B5) produced the best performing model (R2 = 0.71, RMSE = 39.88 mg/m3), but it did not perform as well as Sentinel-2. Regarding turbidity, the best model (with (R2 =0.85, RMSE = 0.87 NTU) obtained by Sentinel-2 utilized a single band (B4), while the best model (with R2 = 0.64, RMSE = 0.90 NTU) using Landsat-8 was performed by applying two bands (B1/B3). Mapping the water quality parameters using the best performance biooptical model showed the significant effect of the adjacent land on the boundary pixels compared to pixels of deeper water

An Industrial Pneumatic and Servo Four-axis Robotic Gripper System: Description and Unitronics Ladder Logic Programming

As part of the advanced programmable logic controllers (PLC) course at Michigan Tech, this class project is performed on a mechatronics system gifted by Donald Engineering, a Michigan-based supplier of industrial automation systems and components. This paper explores the functionality and ladder programming of a four-axis robot enclosed in a cage with one side guarded by an optical fence. The robot has pneumatically actuated X-Y linear motion and a pneumatic gripper. Furthermore, the Z-axis motion and gripper wrist rotation are controlled by servo motors. A human machine interface (HMI) is also present, and it allows for easy manipulation and programming of the robot. This type robot can be used to transfer small components between conveyer belts or for light assembly functions. This paper details of the system’s components, operation, and custom programming