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Ander Sánchez-Chica
University of the Basque Country (UPV/EHU)

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Conference paper
Published: 29 August 2020 in Advances in Intelligent Systems and Computing
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Autonomous Mobile Robots (AMR) need a positioning function to move into unknown areas. These kinds of vehicles do not use a magnetic tape to guide into warehouses. Therefore, AMR use two different alternative techniques to solve the localization problem. First one is based on absolute positioning, and second one is established on relative localization. The absolute localization uses Simultaneous Localization and Mapping algorithms, in order to obtain a global position. However, the relative localization is based on odometry techniques. With the intention of developing a navigation system for an industrial mobile robot, which is being programmed in a structured text language, a relative localization is done utilizing LiDAR data acquisition. This novel concept analyzes two LiDAR datasets from different periods to calculate the AMR movement, by implementing Point matching and Linear Regression (LR) techniques. To understand the differences between conventional Iterative Closest Point (ICP) and LR a comparison is performed.

ACS Style

Daniel Teso-Fz-Betoño; Ekaitz Zulueta; Ander Sánchez-Chica; Unai Fernandez-Gamiz; Irantzu Uriarte; Jose Manuel Lopez-Guede. A Relative Positioning Development for an Autonomous Mobile Robot with a Linear Regression Technique. Advances in Intelligent Systems and Computing 2020, 627 -635.

AMA Style

Daniel Teso-Fz-Betoño, Ekaitz Zulueta, Ander Sánchez-Chica, Unai Fernandez-Gamiz, Irantzu Uriarte, Jose Manuel Lopez-Guede. A Relative Positioning Development for an Autonomous Mobile Robot with a Linear Regression Technique. Advances in Intelligent Systems and Computing. 2020; ():627-635.

Chicago/Turabian Style

Daniel Teso-Fz-Betoño; Ekaitz Zulueta; Ander Sánchez-Chica; Unai Fernandez-Gamiz; Irantzu Uriarte; Jose Manuel Lopez-Guede. 2020. "A Relative Positioning Development for an Autonomous Mobile Robot with a Linear Regression Technique." Advances in Intelligent Systems and Computing , no. : 627-635.

Journal article
Published: 10 July 2020 in Sustainability
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The photovoltaic cell temperature is a key factor in solar energy harvesting. Solar radiation raises temperature on the cell, lowering its peak efficiency. Air jet impingement is a high heat transfer rate system and has been previously used to cool the back surface of photovoltaic modules and cells. In this work, an experimental comparison of the cooling performance of two different air jet impingement crossflow schemes was performed. Crossflow is defined as the air mass interacting with a certain jet modifying its movement. This leads to a change in its heat exchange capabilities and is related with the inlet-outlet arrangement of the fluid. In this work, zero and minimum crossflow schemes were compared. The main contribution of this work considered the consumption of the flow supplying devices to determine the most suitable system. The best configuration increased the net power output of the cell by 6.60%. These results show that air impingement cooling can play a role in increasing photovoltaic profitability. In terms of uniformity, on small impingement plates with a low number of nozzles, the advantages expected from the zero crossflow configuration did not stand out.

ACS Style

Pablo Martínez-Filgueira; Ekaitz Zulueta; Ander Sánchez-Chica; Gustavo García; Unai Fernandez-Gamiz; Josu Soriano. Experimental Air Impingement Crossflow Comparison and Theoretical Application to Photovoltaic Efficiency Improvement. Sustainability 2020, 12, 5577 .

AMA Style

Pablo Martínez-Filgueira, Ekaitz Zulueta, Ander Sánchez-Chica, Gustavo García, Unai Fernandez-Gamiz, Josu Soriano. Experimental Air Impingement Crossflow Comparison and Theoretical Application to Photovoltaic Efficiency Improvement. Sustainability. 2020; 12 (14):5577.

Chicago/Turabian Style

Pablo Martínez-Filgueira; Ekaitz Zulueta; Ander Sánchez-Chica; Gustavo García; Unai Fernandez-Gamiz; Josu Soriano. 2020. "Experimental Air Impingement Crossflow Comparison and Theoretical Application to Photovoltaic Efficiency Improvement." Sustainability 12, no. 14: 5577.

Journal article
Published: 25 May 2020 in Mathematics
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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.

ACS Style

Daniel Teso-Fz-Betoño; Ekaitz Zulueta; Ander Sánchez-Chica; Unai Fernandez-Gamiz; Aitor Saenz-Aguirre. Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot. Mathematics 2020, 8, 855 .

AMA Style

Daniel Teso-Fz-Betoño, Ekaitz Zulueta, Ander Sánchez-Chica, Unai Fernandez-Gamiz, Aitor Saenz-Aguirre. Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot. Mathematics. 2020; 8 (5):855.

Chicago/Turabian Style

Daniel Teso-Fz-Betoño; Ekaitz Zulueta; Ander Sánchez-Chica; Unai Fernandez-Gamiz; Aitor Saenz-Aguirre. 2020. "Semantic Segmentation to Develop an Indoor Navigation System for an Autonomous Mobile Robot." Mathematics 8, no. 5: 855.

Journal article
Published: 19 December 2019 in Energies
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Artificial Neural Networks (ANNs) have proven to be a powerful tool in many fields of knowledge. At the same time, evolutionary algorithms show a very efficient technique in optimization tasks. Historically, ANNs are used in the training process of supervising networks by decreasing the error between the output and the target. However, we propose another approach in order to improve these two techniques together. The ANN is trained with the points obtained during an optimization process by a genetic algorithm and a flower pollination algorithm. The performance of this ANN is used as a stop criterion for the optimization process. This new configuration aims to reduce the number of iterations executed by the genetic optimizer when learning the cost function by an ANN. As a first step, this approach is tested with eight benchmark functions. As a second step, the authors apply it to an air jet impingement design process, optimizing the surface temperature and the fan efficiency. Finally, a comparison between the results of a regular optimization and the results obtained in the present study is presented.

ACS Style

Ander Sánchez-Chica; Ekaitz Zulueta; Daniel Teso-Fz-Betoño; Pablo Martínez-Filgueira; Unai Fernandez-Gamiz. ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design. Energies 2019, 13, 16 .

AMA Style

Ander Sánchez-Chica, Ekaitz Zulueta, Daniel Teso-Fz-Betoño, Pablo Martínez-Filgueira, Unai Fernandez-Gamiz. ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design. Energies. 2019; 13 (1):16.

Chicago/Turabian Style

Ander Sánchez-Chica; Ekaitz Zulueta; Daniel Teso-Fz-Betoño; Pablo Martínez-Filgueira; Unai Fernandez-Gamiz. 2019. "ANN-Based Stop Criteria for a Genetic Algorithm Applied to Air Impingement Design." Energies 13, no. 1: 16.

Journal article
Published: 29 April 2019 in Energies
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Air jet impingement systems have proven to be a very efficient way of heat transfer in single phase flows, which has allowed them to be applied in several industries. However, the complexity of the physical phenomena that take place in the cooling or heating processes makes the task of designing and sizing a system of this type very difficult. The objective of this work is to develop a methodology for the optimization of the impingement plate for electronic components cooling systems. The component chosen to exemplify this work is an insulated gate bipolar transistor (IGBT) such as those employed in photovoltaic inverters. The proposed methodology is divided into the thermo-hydraulic calculation process and the optimization of the system. This optimization is carried out using a multi-objective particle swarm optimization (PSO) algorithm that seeks the best compromise between two variables: Component temperature and manufacturing time of the impingement plate. The result is a calculation tool that can quickly find the solution that meets the requirements of the designer without the need to evaluate all possible solutions.

ACS Style

Pablo Martínez-Filgueira; Ekaitz Zulueta; Ander Sánchez-Chica; Unai Fernández-Gámiz; Josu Soriano. Multi-Objective Particle Swarm Based Optimization of an Air Jet Impingement System. Energies 2019, 12, 1627 .

AMA Style

Pablo Martínez-Filgueira, Ekaitz Zulueta, Ander Sánchez-Chica, Unai Fernández-Gámiz, Josu Soriano. Multi-Objective Particle Swarm Based Optimization of an Air Jet Impingement System. Energies. 2019; 12 (9):1627.

Chicago/Turabian Style

Pablo Martínez-Filgueira; Ekaitz Zulueta; Ander Sánchez-Chica; Unai Fernández-Gámiz; Josu Soriano. 2019. "Multi-Objective Particle Swarm Based Optimization of an Air Jet Impingement System." Energies 12, no. 9: 1627.