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Mergulhão Bot: Design and Prototyping of a Low-Cost Hybrid Vehicle for Oil Leakage Detection 20

Mergulhão Bot: Design and Prototyping of a Low-Cost Hybrid ...

20th Latin American robotics Symposium, 15th Brazilian Symposium on robotics, and 14th Workshop of robotics in Education, LARS/SBR/WRE 2023

作者： De Oliveira, Guilherme C. Garcia, Enrique F. Dos Santos, Julio Cesar S. Pedroso, Adir A. Pereira, Leonardo C. Drews, Paulo L. J. FURG - Federal University of Rio Grande NAUTEC - Intelligent robotics and automation group Rio Grande Brazil UFPEL - Federal University of Pelotas CDTEC - Technological Development Center Pelotas Brazil

ISBN: (纸本)9798350315387

Unmanned vehicles are increasingly popular and their demand increases annually. Since their invention in the 1950s, these vehicles have been widely used in various applications which include environmental monitoring. Oil spill in the oceans is an example of a serious problem for marine ecosystems that can be investigated with these vehicles. Currently, government authorities are analyzing satellite images to detect oil spots, but when oil submerges in the ocean becomes imperceptible by satellites. This oil can be silently moving to coastal regions, without containment maneuvers. Unmanned vehicles may be an effective alternative to remote sensing in these cases. In this scenario, this work presents a new design of a hybrid vehicle to move on the ocean surface and be able to submerge water, called the Mergulhão Bot. The robot was mechanically modeled using OnShape software, simulations were performed using ROS, Gazebo, and the UUV Simulator package. © 2023 IEEE.

关键词： Oil spills

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Mergulhão Bot: Design and Prototyping of a Low-Cost Hybrid Vehicle for Oil Leakage Detection

Mergulhão Bot: Design and Prototyping of a Low-Cost Hybrid ...

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IEEE Latin American robotics Symposium, LARS

作者： Guilherme C. De Oliveira Enrique F. Garcia Julio Cesar S. Dos Santos Adir A. Pedroso Leonardo C. Pereira Paulo L. J. Drews NAUTEC - Intelligent robotics and automation group FURG - Federal University of Rio Grande Rio Grande Brazil CDTEC - Technological Development Center UFPEL - Federal University of Pelotas Pelotas Brazil

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Cross-view and Cross-domain Underwater Localization based on Optical Aerial and Acoustic Underwater Images

arXiv

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arXiv 2022年

作者： Dos Santos, Matheus M. De Giacomo, Giovanni G. Drews, Paulo L.J. Botelho, Silvia S.C. Intelligent Robotics and Automation Group - NAUTEC Center for Computational Science - C3 Universidade Federal do Rio Grande - FURG Rio Grande Brazil

Cross-view image matches have been widely explored on terrestrial image localization using aerial images from drones or satellites. This study expands the cross-view image match idea and proposes a cross-domain and cross-view localization framework. The method identifies the correlation between color aerial images and underwater acoustic images to improve the localization of underwater vehicles that travel in partially structured environments such as harbors and marinas. The approach is validated on a real dataset acquired by an underwater vehicle in a marina. The results show an improvement in the localization when compared to the dead reckoning of the vehicle. © 2022, CC BY-NC-ND.

关键词： Antennas

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Deterministic and Stochastic Analysis of Deep Reinforcement Learning for Low Dimensional Sensing-based Navigation of Mobile Robots

arXiv

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arXiv 2022年

作者： Grando, Ricardo B. de Jesus, Junior C. Kich, Victor A. Kolling, Alisson H. Guerra, Rodrigo S. Drews, Paulo L.J. The Technological University of Uruguay Uruguay NAUTEC - Intelligent Robotics and Automation Group Center for Computational Science Federal University of Rio Grande - FURG RS Brazil The Universidade Federal de Santa Maria - UFSM RS Brazil

Deterministic and Stochastic techniques in Deep Reinforcement Learning (Deep-RL) have become a promising solution to improve motion control and the decision-making tasks for a wide variety of robots. Previous works showed that these Deep-RL algorithms can be applied to perform mapless navigation of mobile robots in general. However, they tend to use simple sensing strategies since it has been shown that they perform poorly with a high dimensional state spaces, such as the ones yielded from image-based sensing. This paper presents a comparative analysis of two Deep-RL techniques - Deep Deterministic Policy Gradients (DDPG) and Soft Actor-Critic (SAC) - when performing tasks of mapless navigation for mobile robots. We aim to contribute by showing how the neural network architecture influences the learning itself, presenting quantitative results based on the time and distance of navigation of aerial mobile robots for each approach. Overall, our analysis of six distinct architectures highlights that the stochastic approach (SAC) better suits with deeper architectures, while the opposite happens with the deterministic approach (DDPG). © 2022, CC BY.

关键词： Mobile robots

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Unsupervised Learning Method for Encoder-Decoder-Based Image Restoration 9th

Unsupervised Learning Method for Encoder-Decoder-Based Image...

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9th Brazilian Conference on intelligent Systems, BRACIS 2020

作者： Mello, Claudio D. Messias, Lucas R. V. Drews-Jr, Paulo Lilles Jorge Botelho, Silvia S. C. NAUTEC - Intelligent Robotics and Automation Group Center for Computational Science - C3 Federal University of Rio Grande - FURG Rio GrandeRS96.203-000 Brazil

ISBN: (纸本)9783030613761

The restoration of a corrupted image is a challenge to computer vision and image processing. In hazy, underwater and medical images, the lack of paired images lead the state of the art to synthesize datasets. The Generative Adversarial Networks (GANs) are widely used in these cases. However, computational cost and training instability are current concerns. We present an unsupervised learning algorithm that does not requires paired dataset to train encoder-decoder-like neural network for image restoration. An encoder-decoder learn to represent its input data in a latent representation and reconstruct then in the output. During the training stage, our algorithm applies the encoder-decoder output image to a degradation block that reinforces its degradation. The degraded and input images are matched using a loss function. After the training process, we obtain a restored image from the decoder. We used ill-exposed images to evaluate and validate our algorithm. © 2020, Springer Nature Switzerland AG.

关键词： Image reconstruction

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A Low-Cost Prototype to Automate Agricultural Sprayers ⁎

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IFAC-PapersOnLine 2020年第2期53卷 15835-15840页

作者： Fabio P. Terra Gabrielle R.A. da Rosa Jardel J.P. Prado Paulo L.J. Drews- Federal Institute of Education Science and Technology (IFSul) Brazil Automation and Intelligent Robotics Group (NAUTEC) Center of Computational Science (C3) Federal University of Rio Grande (FURG) Brazil

The world’s population growth in the last decades demanded a great increase in agricultural production, especially in food. Therefore, in order to reduce possible losses and guarantee productivity, farmers depend more and more on the application of agrochemicals in their crops. This massive use of pesticides represents not only a high cost to the farmers but also a risk to their health, to the environment and even to the safety of the food consumed by the population. In this context, new technologies have been developed to make agricultural spraying more effective, reducing the amount of pesticide applied and dosing its use according to the need of the crop. The recognition of its spatial and temporal variability is treated by Precision Agriculture. In the case of pest management it can be done using variable-rate sprayers or using on/off application with individual nozzle control. Thus, this paper proposes a low-cost prototype in a modular solution to automate existent agricultural sprayers. The solution allows individual nozzle opening, with on/off control, using solenoid valves, pressure and flow sensors, Arduino boards, and smartphone. Additionally, the prototype has a data logger function to store nozzle status and sensor values, allowing future analysis and application reports.

关键词： Precision agriculture Ag 4.0 boom sprayer automation individual nozzle control on/off application Arduino

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Cooperative Training of Triplet Networks for Cross-Domain Matching

Cooperative Training of Triplet Networks for Cross-Domain Ma...

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IEEE Latin American robotics Symposium, LARS

作者： Giovanni G. De Giacomo Matheus M. dos Santos Paulo L.J. Drews-Jr Silvia S. C. Botelho Intelligent Robotics and Automation Group - NAUTEC Center for Computational Science C3 - Universidade Federal do Rio Grande - FURG Rio Grande Brazil

ISBN: (数字)9780738111537

ISBN: (纸本)9781665419857

Recently, Deep Convolutional Neural Networks have been applied to various computer vision problems and achieved state-of-the-art results. Among these, Siamese and Triplet networks have obtained great traction in intra-domain matching. However, it is impossible to directly use these networks in cross-domain problems. Thus, this paper proposes a data-driven approach for cross-domain matching of complex data that do not share similar features. A pair of triplet networks are trained with a new cooperative approach to perform Deep Metric Learning. In order to validate our proposed method, we apply it to a cross-domain image matching problem that aims to assist with underwater robot localization. We train a pair of networks using our methodology on a dataset composed of acoustic and segmented aerial images and evaluate it on a dataset acquired in another location. Our results show that our method is able to achieve up to 83% accuracy in matching acoustic and segmented aerial images.

关键词： Training Image segmentation Sonar Neural networks Acoustics Convolution Pipelines

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Perfect Storm: DSAs Embrace Deep Learning for GPU-Based Computer Vision

Perfect Storm: DSAs Embrace Deep Learning for GPU-Based Comp...

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Tutorials of the Brazilian Symposium on Computer Graphics and Image Processing (SIBGRAPI TUTORIALS)

作者： Marcelo Pias Silvia Botelho Paulo Drews Intelligent Robotics and Automation Group (NAUTEC) Federal University of Rio Grande (FURG)

This paper explores Domain-Specific Deep Learning Architectures for GPU Computer Vision through a "brainstorming" approach on selected hands-on topics in the area. We intend to discuss Deep Neural Networks (DNNs) to image classification problems through tools, frameworks and data pipelines commonly used to train and deploy DNNs in GPUs and Domain-Specific Architectures (DSAs).

关键词： Machine learning Computer architecture Graphics processing units Biological neural networks Data mining Task analysis

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Semiotics Applied to Human-Robot Interaction in Welding Processes

Semiotics Applied to Human-Robot Interaction in Welding Proc...

引用

IEEE Latin American robotics Symposium, LARS

作者： Patrick Baldez Mateus Schot Danubia Espíndola Silvia S.C. Botelho Bruna Guterres Luciane B. Soares Intelligent Robotics and Automation Group - Nautec Federal University of Rio Grande - FURG Federal University of Rio Grande Rio Grande Brazil

ISBN: (数字)9781728142685

ISBN: (纸本)9781728142692

Teleoperation of robotic systems requires efficient and effective human-robot interactions. The present work presents the development of a semiotic-based human-robot interaction interface through the identification of the signs classes presented in the original interface of the robotic welding equipment BUG-O MDS-1005. Among the hardware equipments necessary to construct the proposed interface there are a Raspberry Pi 3 with a 7 inch touchscreen display, an Odroid XU4 and an Arduino Uno. In order to enable users to make use of an intuitive system that meets industry standards, HumanRobot Interaction (HRI) technologies are used. Furthermore, the proposed human-robot interaction interface provides new functionalities enhancing not only the system usability but also the operator productivity during welding processes.

关键词： Welding Inspection Semiotics Service robots Weaving Task analysis

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Underwater Sonar and Aerial Images Data Fusion for Robot Localization

Underwater Sonar and Aerial Images Data Fusion for Robot Loc...

引用

International Conference on Advanced robotics (ICAR)

作者： Matheus M. Dos Santos Giovanni G. De Giacomo Paulo L. J. Drews Silvia S. C. Botelho Intelligent Robotics and Automation Group NAUTEC Center for Computational Science C3 Universidade Federal do Rio Grande FURG Rio Grande Brazil

Autonomous underwater navigation is a challenging problem because of the limitations imposed by aquatic environments. Among them, the use of Global Positioning System (GPS) is severely limited. Thus, we propose the use of sensor fusion to improve underwater localization in partially structured environments. We sustain our proposal explores the benefits of aerial images, such as georeferencing, to improve underwater navigation with a multibeam forward looking sonar. Our methodology combines state-of-the-art approaches such as Deep Neural Networks and Adaptive Monte Carlo Localization to fuse data from different image domains. The obtained results show a significant improvement over traditional odometry for underwater localization.

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