The aim of this paper is to improve the dynamic window approach algorithm for mobile robots by implementing a prediction window with a fuzzy inference system to adapt to fixed parameters, depending on the surrounding conditions. The first implementation shows the advantage of the prediction step in terms of optimizing the path selection. The second improvement uses fuzzy inference to optimize each of the fixed parameters’ values to increase the algorithm performance. Nevertheless, a simple fuzzy inference system (FIS) was not used for this particular study; instead, an artificial neuro-fuzzy inference system (ANFIS) was used, thus making it possible to develop a FIS system with a back-propagation technique. Each parameter would have a particular ANFIS, in order to modify the ${\mathsf{\alpha }}_{\mathrm{D}}$ , ${\mathsf{\beta }}_{\mathrm{D}}$ , and ${\mathsf{\gamma }}_{\mathrm{D}}$ parameters individually. At the end of the article, different scenarios are analyzed to determine whether the developments in this article have improved the DWA behavior. The results show that the prediction step and ANFIS adapt DWA performance by optimizing the path resolution.

In this study, a semantic segmentation network is presented to develop an indoor navigation system for a mobile robot. Semantic segmentation can be applied by adopting different techniques, such as a convolutional neural network (CNN). However, in the present work, a residual neural network is implemented by engaging in ResNet-18 transfer learning to distinguish between the floor, which is the navigation free space, and the walls, which are the obstacles. After the learning process, the semantic segmentation floor mask is used to implement indoor navigation and motion calculations for the autonomous mobile robot. This motion calculations are based on how much the estimated path differs from the center vertical line. The highest point is used to move the motors toward that direction. In this way, the robot can move in a real scenario by avoiding different obstacles. Finally, the results are collected by analyzing the motor duty cycle and the neural network execution time to review the robot’s performance. Moreover, a different net comparison is made to determine other architectures’ reaction times and accuracy values.

Flow control devices have been introduced in the wind energy sector to improve the aerodynamic behavior of the wind turbine blades (WTBs). Among these flow control devices, Gurney flaps (GFs) have been the focus of innovative research, due to their good characteristics which enhance the lift force that causes the rotation of the wind turbine rotor. The lift force increment introduced by GFs depends on the physical characteristics of the device and the angle of attack (AoA) of the incoming wind. Hence, despite a careful and detailed design, the real performance of the GFs is conditioned by an external factor, the wind. In this paper, an active operation of GFs is proposed in order to optimize their performance. The objective of the active Gurney flap (AGF) flow control technique is to enhance the aerodynamic adaption capability of the wind turbine and, thus, achieve an optimal operation in response to fast variations in the incoming wind. In order to facilitate the management of the information used by the AGF strategy, the aerodynamic data calculated by computational fluid dynamics (CFD) are stored in an artificial neural network (ANN). Blade element momentum (BEM) based calculations have been performed to analyze the aerodynamic behavior of the WTBs with the proposed AGF strategy and calculate the corresponding operation of the wind turbine. Real wind speed values from a meteorological station in Salt Lake City, Utah, USA, have been used for the steady BEM calculations. The obtained results show a considerable improvement in the performance of the wind turbine, in the form of an enhanced generated energy output value and a reduced bending moment at the root of the WTB.

Trailing edge flaps (TEFs) are high-lift devices that generate changes in the lift and drag coefficients of an airfoil. A large number of 2D simulations are performed in this study, in order to measure these changes in aerodynamic coefficients and to analyze them for a given Reynolds number. Three different airfoils, namely NACA 0012, NACA 64(3)-618, and S810, are studied in relation to three combinations of the following parameters: angle of attack, flap angle (deflection), and flaplength. Results are in concordance with the aerodynamic results expected when studying a TEF on an airfoil, showing the effect exerted by the three parameters on both aerodynamic coefficients lift and drag. Depending on whether the airfoil flap is deployed on either the pressure zone or the suction zone, the lift-to-drag ratio, C_L/C_D, will increase or decrease, respectively. Besides, the use of a larger flap length will increase the higher values and decrease the lower values of the C_L/C_D ratio. In addition, an artificial neural network (ANN) based prediction model for aerodynamic forces was built through the results obtained from the research.