Although different physiological signals, such as electrooculography (EOG) have been widely used in the control of assistance systems for people with disabilities, customizing the signal classification system remains a challenge. In most interfaces, the user must adapt to the classification parameters, although ideally the systems must adapt to the user parameters. Therefore, in this work the use of a multilayer neural network (MNN) to model the EOG signal as a mathematical function is presented, which is optimized using genetic algorithms, in order to obtain the maximum and minimum amplitude threshold of the EOG signal of each person to calibrate the designed interface. The problem of the variation of the voltage threshold of the physiological signals is addressed by means of an intelligent calibration performed every 3 min; if an assistance system is not calibrated, it loses functionality. Artificial intelligence techniques, such as machine learning and fuzzy logic are used for classification of the EOG signal, but they need calibration parameters that are obtained through databases generated through prior user training, depending on the effectiveness of the algorithm, the learning curve, and the response time of the system. In this work, by optimizing the parameters of the EOG signal, the classification is customized and the domain time of the system is reduced without the need for a database and the training time of the user is minimized, significantly reducing the time of the learning curve. The results are implemented in an HMI for the generation of points in a Cartesian space (X, Y , Z) in order to control a manipulator robot that follows a desired trajectory by means of the movement of the user's eyeball.
This paper presents two-swim operators to be added to the chemotaxis process of the modified bacterial foraging optimization algorithm to solve three instances of the synthesis of four-bar planar mechanisms. One swim favors exploration while the second one promotes fine movements in the neighborhood of each bacterium. The combined effect of the new operators looks to increase the production of better solutions during the search. As a consequence, the ability of the algorithm to escape from local optimum solutions is enhanced. The algorithm is tested through four experiments and its results are compared against two BFOA-based algorithms and also against a differential evolution algorithm designed for mechanical design problems. The overall results indicate that the proposed algorithm outperforms other BFOA-based approaches and finds highly competitive mechanisms, with a single set of parameter values and with less evaluations in the first synthesis problem, with respect to those mechanisms obtained by the differential evolution algorithm, which needed a parameter fine-tuning process for each optimization problem.
Nowadays, industrial robot applications are required to customize the manufacturing of diverse products to reduce both downtime and standoff variability. The two methods for robot programming are regularly implemented to carry out that goal. The first one, online programming, requires a specialized operator to guide the robot through desired poses, and the quality of the result is directly limited by his skill level. On the other side, off-line programming uses software packaging to simulate robot applications before their implementation. It reduces downtime with respect to online programming but requires additional calibration steps. In this paper, a novel procedure is presented to obtain accurate surface approximations by combining linear interpolations generated during online programming with a triangulated surface reconstruction of a workpiece surface representation. The method uses a point cloud instead of a predefined mesh to reduce the standoff variability between the robotic tool center point and the surface. Additionally, a technique based on a penalized least squares method was implemented to smooth the trajectory, including position and orientation. The proposed methodology was validated with three well-known case studies involving real trajectories, with simulations in MATLAB and RobotStudio, as well as by experimentation with an industrial ABB robot. The quality of the results demonstrates a great efficiency of this method for path generation based on surface reconstruction. INDEX TERMS Robot programming procedure, industrial robot, path generation, surface reconstruction.
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