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Seam tracking and welding bead geometry analysis for autonomous welding robot 14

Seam tracking and welding bead geometry analysis for autonom...

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14th Latin American robotics Symposium and 5th Brazilian Symposium on robotics, LARS-SBR 2017

作者： Soares, Luciane B. Weis, Átila A. Rodrigues, Ricardo N. Drews, Paulo L. J. Guterres, Bruna Botelho, Silvia S. C. Filho, Nelson D. Intelligent Robotics and Automation Group - Nautec Federal University of Rio Grande - FURG Brazil

ISBN: (纸本)9781538609569

Welding is a process recognized by the laborious work and hazardous work environment it takes place, but it is an important process in different industrial scenarios, like the shipbuilding industry. The use of robots has been increasing in recent years, reducing the human interference necessary for the process. This paper proposes a system for automated seam tracking and a geometric welding bead analysis systems based on passive monocular vision. Using an vision system attached to a linear welding robot, images of the gap between two plates are acquired, during the welding process in order to identify the groove's characteristics and perform a autonomous welding. These characteristics are used during the geometric weld bead analysis to detect geometric failures such as misalignment, lack or excess of fusion, among others. © 2017 IEEE.

关键词： Geometry

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Automated seam tracking system based on passive monocular vision for automated linear robotic welding process 15

Automated seam tracking system based on passive monocular vi...

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15th IEEE International Conference on Industrial Informatics, INDIN 2017

作者： Weis, Atila A. Mor, Jusoan L. Soares, Luciane B. Steffens, Cristiano R. Drews-Jr, Paulo L. J. De Faria, Matheus F. Evald, Paulo J. D. O. Azzolin, Rodrigo Z. Filho, Nelson D. Botelho, Silvia S. Da C. Intelligent Robotics and Automation Group NAUTEC Science Computer Center - C3 Federal University of Rio Grande Brazil

ISBN: (纸本)9781538608371

Welding is an important process in the industrial scenario, especially in the shipbuilding industry. This process is recognized by the laborious work and the hazardous work environment. The use of robots to automate the welding process can reduce the human interference and improve the productivity. This paper proposes a system for automated seam tracking based on passive monocular vision. The vision provides a data feedback to the automated robotic welding system allowing quality and productivity gains. A trajectory controller is developed to correct the robot's movement over the seam reference. The controller and a visual algorithm to find the seam reference in a real Gas Metal Arc Welding (GMAW) process are presented. The proposed system allows the automated seam tracking, the trajectory control of the welding torch, and a higher automation level in linear robotic welding. The capabilities of the method is evaluated using a commercial linear welding robot showing its viability. © 2017 IEEE.

关键词： Productivity

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Automated seam tracking system based on passive monocular vision for automated linear robotic welding process 17

Automated seam tracking system based on passive monocular vi...

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32nd Annual ACM Symposium on Applied Computing, SAC 2017

作者： Weis, Átila Astor Werhli, Adriano Velasque Filho, Nelson L. Duarte Federal University of Rio Grande-FURG Computer Science Center-C3 Group Automation and Intelligent Robotics-NAUTEC Brazil

ISBN: (纸本)9781450344869

Welding is a very important process in the industrial scenario, especially in the shipbuilding industry. Many researches employ robots to automate different welding process. This paper proposes a system for automated seam tracking, based on passive vision, to act as a data feedback to the automated robotic welding system, with quality and productivity gains in the process. Different cameras arrangements are presented and tested as the vision acquisition system in a commercial linear welding robot. A physical method and an algorithm to remove visual noises from images, produced by the GMAW welding process, are presented and the results discussed. © 2017 ACM.

关键词： Computer vision

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GuidedNet: Single Image Dehazing Using an End-to-End Convolutional Neural Network

GuidedNet: Single Image Dehazing Using an End-to-End Convolu...

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Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI)

作者： Lucas Teixeira Gonçalves Joel Felipe de Oliveira Gaya Paulo Jorge Lilles Drews Junior Silvia Silva da Costa Botelho Intelligent Robotics and Automation Group - NAUTEC Universidade Federal do Rio Grande - FURG Rio Grande Brazil Universidade Federal do Rio Grande Rio Grande RS BR

Poor visibility is a common problem when capturing images in participating mediums such as mist or water. The problem of generating a haze-free image based on a hazy one can be described as image dehazing. Previous approaches dealt with this problem using physical models based on priors and simplifications. In this paper, we demonstrate that an end-to-end convolutional neural network is able to learn the dehazing process with no parameters or priors required, resulting in a more generic method. Even though our model is trained entirely with hazy indoor images, we are able to fully restore outdoor images with real haze. Also, we propose an architecture containing the novel Guided Layers, introduced in order to reduce the loss of spatial information while restoring the images. Our method outperforms other machine learning based models, yielding superior results both qualitatively and quantitatively.

关键词： Atmospheric modeling Neural networks Image restoration Training Scattering Degradation Network architecture

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Towards comparison of Kalman filter methods for localisation in underwater environments 14

Towards comparison of Kalman filter methods for localisation...

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14th Latin American robotics Symposium and 5th Brazilian Symposium on robotics, LARS-SBR 2017

作者： Thiago Silva Da Rosa, Romulo Zaffari, Guilherme Brunei Dias De Oliveira Evald, Paulo Jefferson Jorge Drews, Paulo Lilles Silva Da Costa Botelho, Silvia Intelligent Robotics and Automation Group - NAUTEC Center of Computational Sciences - C3 Federal University of Rio Grande - FURG Rio Grande RS Brazil

ISBN: (纸本)9781538609569

Kalman Filters are utilised for filtering and estimation in a large set of application. Here, this methodology is utilised for trajectory estimation of an underwater robot. In this work, three Kalman Filter methods are proposed for trajectory estimation. There are: Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF) and Central Difference Kalman Filter (CDKF). Simulation results are presented and discussed, where UKF and CDKF presented better performance than EKF with data that were collected from our dataset. However, UKF had a slightly smaller execution time than CDKF with almost the same error. © 2017 IEEE.

关键词： Extended Kalman filters

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Semantic Mapping on Underwater Environment Using Sonar Data 13

Semantic Mapping on Underwater Environment Using Sonar Data

引用

13th Latin American robotics Symposium and 4th Brazilian Symposium on robotics, LARS/SBR 2016

作者： Machado, M. Drews, P. Núñez, P. Botelho, S. NAUTEC Intelligent Robotics and Automation Group Univ. Federal do Rio Grande - FURG Rio Grande Brazil Department of Computer and Communication Technology Universidad de Extremadura Cáceres Extremadura Spain

ISBN: (纸本)9781509036561

The use of robots in underwater exploration is increasing in the last years. The automation of the monitoring, inspection and underwater maintenance tasks requires the understanding of the environment. One of the key issues of these systems is to recognize in the objects in the scene. This paper proposes a method to provide a semantic mapping using acoustic images acquired by forward looking sonar (FLS). The method represents the environment using Gaussian probability density functions. Furthermore, we efficiently segment and classify the structures in the scene. Finally, we create a semantic map of the scene. We evaluate the method in a real dataset acquired by an underwater vehicle performing autonomous navigation and mapping tasks in a harbor area. © 2016 IEEE.

关键词： Mapping

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A modified topological descriptor for forward looking sonar images

A modified topological descriptor for forward looking sonar ...

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OCEANS 2016 - Shanghai

作者： Dos Santos, Matheus M. Zaffari, Guilherme B. Duarte, Amanda C. De Fernandes, Daniel A. Drews, Paulo L. J. Da Botelho, Silvia S. C. Intelligent Robotics and Automation Group - NAUTEC Centre for Computational Science - C3 Federal University of Rio Grande - FURG Av. Italia km 8 Rio Grande RS96203-900 Brazil

ISBN: (纸本)9781467397247

The use of robots for underwater exploration has increased around the world in the last years. The automation of the underwater monitoring, inspection, and maintenance tasks often requires a mapping and localization system. One of the key issues of such system is to be able to recognize previously visited places through the sensory information. This paper proposes a modified method for description and recognition of acoustic images. The method builds a graph of the Gaussian probability density function that represents both the shape and the topological relation among the images. It was originally proposed by the first author and his co-authors in 2015. The modifications proposed herein include a new segmentation step for automatic estimation of parameters and a new graph comparison approach that does not need to make use of the orientation of the segmented regions of the images. The new approach was evaluated in a real experiment in a harbor area. It proved to be less dependent on the input parameters than the previous approach. © 2016 IEEE.

关键词： Probability density function

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Exploring the DolphinSLAM's parameters

Exploring the DolphinSLAM's parameters

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OCEANS 2016 - Shanghai

作者： Zaffari, Guilherme B. Dos Santos, Matheus M. Duarte, Amanda C. De Fernandes, Daniel A. Da Botelho, Silvia S. C. Intelligent Robotics and Automation Group - NAUTEC Centre for Computational Science - C3 Federal University of Rio Grande - FURG Av. Italia km 8 Rio Grande RS96203-900 Brazil

ISBN: (纸本)9781467397247

This paper presents the bio-inspired underwater 3D SLAM algorithm called DolphinSLAM. First, every module of the DolphinSLAM algorithm is explained. Then, the effects of parameter variations regarding the parameters of the DolphinSLAM algorithm are investigated based on the use of the Underwater Simulator (UWSim). The parameters of interest are i) the image feature extractors, ii) the vocabulary size regarding the BoW model, and iii) the equations for experience map correction. © 2016 IEEE.

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Vision-Based Obstacle Avoidance Using Deep Learning

Vision-Based Obstacle Avoidance Using Deep Learning

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IEEE Latin American robotics Symposium, LARS

作者： Joel O. Gaya Lucas T. Gonçalves Amanda C. Duarte Breno Zanchetta Paulo Drews Silvia S.C. Botelho Intelligent Robotics and Automation Group - NAUTEC Universidade Federal do Rio Grande - FURG Rio Grande Brazil

This paper describes a vision-based obstacle avoidance strategy using Deep Learning for Autonomous Underwater Vehicles (AUVs) equipped with a simple colored monocular camera. For each input image, our method uses a deep neural network to compute a transmission map that can be understood as a relative depth map. The transmission map is estimated for each patch of the image to determine the obstacles nearby. With this map we are able to identify the most appropriate Region of Interest (RoI) and to find a direction of escape. This direction allows the robot to avoid obstacles by performing a control action. We evaluate our approach in two underwater video sequences. The results show the approach is able to successful find a RoI that avoids coral reefs, fish, the seafloor and other object present in the scene.

关键词： Cameras Robots Collision avoidance Neural networks Backscatter Shape Vehicles

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Real-time monocular obstacle avoidance using underwater dark channel prior

Real-time monocular obstacle avoidance using underwater dark...

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2016 IEEE/RSJ International Conference on intelligent Robots and Systems, IROS 2016

作者： Drews, Paulo Hernández, Emili Elfes, Alberto Nascimento, Erickson R. Campos, Mario Autonomous Systems Laboratory Data61-CSIRO Brisbane Australia Computer Vision and Robotics Laboratory Dep. de Ciência da Computação Univ. Federal de Minas Gerais - UFMG Belo Horizonte Brazil NAUTEC Intelligent Robotics and Automation Group Univ. Federal do Rio Grande - FURG Rio Grande Brazil

ISBN: (纸本)9781509037629

In this paper we propose a new vision-based obstacle avoidance strategy using the Underwater Dark Channel Prior (UDCP) that can be applied to any Unmanned Underwater Vehicle (UUV) equipped with a simple monocular camera and minimal on-board processing capabilities. For each incoming image, our method first computes a relative depth map to estimate the obstacles nearby. Then, the map is segmented and the most promising Region of Interest (RoI) is identified. Finally, an escape direction is computed within the RoI and a control action is performed accordingly to avoid the obstacles. We tested our approach on a video sequence in a natural environment and compared it against a stateofthe-art method showing better performance, specially in light changing conditions. We also provide online results on a low-cost Remotely Operated Vehicle (ROV) in a controlled environment. © 2016 IEEE.

关键词： Remotely operated vehicles

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