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C-Quad is an origami-inspired, foldable, miniature robot whose legs and body are all machined from one PET sheet each. The already famous compliant legs are modified such that they can be manufactured from a flat PET sheet and folded into the C-shape wanted. The compliant legs enable the miniature robot to run fast and scale obstacles with ease due to the geometry of the legs. C-Quad has four legs that are manufactured separately from the main body frame, which is also manufactured from a single PET sheet. All of its legs are actuated individually with a total of four DC motors. Despite the thin PET film, the structural rigidity and robustness of the body frame is increased by using specialized folds and locks. The manufacturing and assembly of the robot takes approximately 2.5 hours. C-Quad carries a battery, an Arduino Pro Micro control board, a bluetooth communication module, custom made encoders and commercially available IR sensors for motor speed control and motor drivers, all of which weighs 38 grams. By using very simple control strategies, it can achieve the speed of 2.7 Bodylengths/sec, can perform in-place turns and can climb over obstacles more than half of its height.
The field of smart health monitoring, intelligent fault detection and diagnosis is expanding dramatically in order to maintain successful operation in many engineering applications. Considering possible fault scenarios that can occur in a system, indicating the type of fault in a sensor is one of the most important and challenging problems in the area of intelligent sensor fault diagnostics. Within this frame of reference, we extended the physics-informed transfer learning framework, first presented previously for a fault cause assignment, to the level of sensor fault diagnostics for a range of different fault scenarios. Hence, the framework is utilized to perform intelligent sensor fault diagnostics for the first time. The underlying dynamics of the reference system are extracted using a completely data-driven methodology and dynamic mode decomposition with control (DMDc) in order to generate time-frequency illustrations of each sample with continuous wavelet transform (CWT). Then, sensor fault diagnostics for bias, drift over time, sine disturbance and increased noise sensor fault scenarios are achieved using the idea of transfer learning with a pre-trained image classification algorithm. The classification results yields a good performance on sensor fault diagnostics with 91.5% training and 84.7% test accuracy along with a fair robustness level with a set of reference benchmark system parameters.
Mechatronics and control education is supported by laboratory intensive assignments that allow students acquire software and hardware skills to solve real world problems. However, COVID-19 force many schools to switch into remote learning complicating the instruction of practical assignments. This paper presents a novel proposal for interactive remote teaching of the laboratory component of the course ME-142: Mechatronics at the University of California, Merced using Digital Twins (DT) and the flipped classroom methodology. Each lab experience is composed by a set of on-demand supporting materials with the foundations of mechatronics simulation using MATLAB/Simulink to enhance and adapt the learning experience of the students. Once the students acquire advanced simulation skills, a set of Digital Twin models are provided to the students in order to begin their interaction with virtual representations of real systems for identification, analysis, controller design and validation, which are available online for remote access. By the end of the course, students were able not only to gain valuable experience with mechatronic systems but also interact and build advanced modelling techniques as Digital Twin, contributing to compensate the lack of remote hardware interaction.
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