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.

Differential evolution (DE) is a simple and efficient population-based stochastic algorithm for solving global numerical optimization problems. DE largely depends on algorithm parameter values and search strategy. Knowledge on how to tune the best values of these parameters is scarce. This paper aims to present a consistent methodology for tuning optimal parameters. At the heart of the methodology is the use of an artificial neural network (ANN) that learns to draw links between the algorithm performance and parameter values. To do so, first, a data-set is generated and normalized, then the ANN approach is performed, and finally, the best parameter values are extracted. The proposed method is evaluated on a set of 24 test problems from the Black-Box Optimization Benchmarking (BBOB) benchmark. Experimental results show that three distinct cases may arise with the application of this method. For each case, specifications about the procedure to follow are given. Finally, a comparison with four tuning rules is performed in order to verify and validate the proposed method’s performance. This study provides a thorough insight into optimal parameter tuning, which may be of great use for users.

The growth in size and weight of wind turbines over the last years has led to the development of flow control devices, such as Gurney flaps (GFs). In the current work, a parametric study is presented to find the optimal GF length to improve the airfoil aerodynamic performance. Therefore, the influence of GF lengths from 0.25% to 3% of the airfoil chord c on a widely used DU91W(2)250 airfoil has been investigated by means of RANS based numerical simulations at Re = 2 × 10⁶. The numerical results showed that, for positive angles of attack, highest values of the lift-to-drag ratio C_L/C_D are obtained with GF lengths between 0.25% c and 0.75% c. Particularly, an increase of 21.57 in C_L/C_D ratio has been obtained with a GF length of 0.5% c at 2° of angle of attack AoA. The influence of GFs decreased at AoAs larger than 5°, where only a GF length of 0.25% c provides a slight improvement in terms of C_L/C_D ratio enhancement. Additionally, an ANN has been developed to predict the aerodynamic efficiency of the airfoil in terms of C_L/C_D ratio. This tool allows to obtain an accurate prediction model of the aerodynamic behavior of the airfoil with GFs.

The industry is changing in order to improve the economy sector. This is the reason why technology is improving and developing new devices. The autonomous guided vehicle with free navigation is a new machine, which uses different techniques to move such as mapping, localization, path planning, and path following. In this paper, a path following is proposed. The path following is called moving to a point, which uses the proportional distance between the target and the autonomous guided vehicles (AGV) to calculate the velocity and direction. If some obstacles appear in the trajectory, however, the vehicle stops. Instead of stopping the machine, by using moving to a point logic, an obstacle avoidance function will be implemented. In this implementation, different parameters can be configured, such as: security distance, which determinates when the obstacle avoidance must correct the pose; and proportional values, which modify the velocity and steering commands. It is also compared to a dynamic window approach (DWA) obstacle avoidance solution. Additionally, the AGV navigates to a non-static target with a path following algorithm.