This work presents the development of a joystick with two degrees of freedom. The joystick is designed considering a cable-capstan mechanism allowing the movement of the central axis for Pitch and Roll angles, where the central axis has a workspace in a plane (XY). The joystick design and instrumentation consider future haptic tasks and applications in practical engineering education courses. This device was designed based on fast prototype manufacturing, including 3D printed parts. The design is small and portable, and the development of the joystick includes a user interface to display and save data and graphs of different signals such as angular positions, central axis position, control signals, current of the motors, and errors, and allows tuning different parameters for the control implementation. In order to show the functionality of the designed mechanism, we show its performance in tracking trajectory tasks implementing a proportional, integral, derivative controller (PIDC) with a Super Twisting algorithm (STA). The results showed the joystick with the complete instrumentation and communication with the user interface in a personal computer. We compared the PIDC using an Euler's derivative vs. the PIDC with the STA's derivative estimated. The results of the trajectory tracking control with the PIDC-STA algorithm showed a better performance of the joystick with an average error norm of 2.73 degrees, taking the Pitch and Roll errors, compared with the PIDC- Euler controller with an average error norm of 3.22 degrees.

This work presents the design of a control trajec-tory algorithm based on Barrier Lyapunov functions (BLF) for a class of third-order electro-mechanical systems. The method considers unknown control gains and uncertain plant parame-ters, a common problem in control engineering applications. In order to deal with the uncertain parameters, this study proposes a backstepping adaptive strategy applying Nussbaum functions to know the sign of the control gains. The BLF imposes partial constraints to keep the trajectories of the system inside a safe operation set. The proposed controller is tested in a virtual one-link robotic manipulator including actuator dynamics to develop an adequate trajectory tracking. The maximum tracking error norm was 0.02, which fulfilled the maximum constrained bound of 0.5.

Currently, different types of learning models seek to confront students with complex problems, problems that will develop in the students the skills required for finding feasible solutions. Both the diagnosis of engineering problems as well as the search and implementation of solution alternatives require first that the student acquire knowledge in their discipline and second, apply the knowledge to solve the aforementioned problems. This paper presents a comparative analysis between the implementations of two learning models, namely Serious Games (SG) and Challenge-based Learning (CBL) to teach the Lean Manufacturing philosophy in Industrial Engineering curriculum. These pedagogical approaches were implemented in two independent groups of the same course with 3rd year undergraduate industrial engineering students from January to May 2018. The main contribution of this article is to show evidence of the implementation of both approaches with undergraduate students. Additionally, a comparative statistical analysis measures the development of disciplinary competencies in terms of teaching results of a course with these learning models. At the end of the semester, the student opinions indicated good results in learning the concepts and techniques of industrial engineering. Improvements to the assessment instruments, however, would provide adequate feedback to the student in the development of their competencies.

Computer vision systems are important for capturing environments, for facial recognition and as a way to scan objects for documenting and for manufacturing. One of the current challenges is to scan objects that change dynamically, whether rigid transformations or shape deformations. This paper presents a new system based on an RGB-D camera array, an array which is calibrated by means of a set of equations that relate the distance, angles and resolution of the cameras. The Iterative Closest Point algorithm is proposed for a fine alignment, as well with a process of reconstruction and elimination of noise by means of a Poisson distribution function. The system was exhaustively validated using two forms with different properties. When comparing the obtained result of the scan versus the real models by means of the distance of Hausdorff, errors of no more than 0.0045 mm were obtained. In addition, an experiment is performed by scanning the palm of the hand under deformations and movements. These results show that the system can scan static and non-static and dynamic forms, thereby demonstrating its usefulness for the reconstruction, analysis and manufacture of objects of different classes.