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Iberian robotics Conference (ROBOT)

作者： Dylan Denizon Sedat Dogru Lino Marques Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

In this work, we aim to improve the performance of an underactuated anthropomorphic tendon driven robotic gripper with two fingers by allowing the stiffness of the finger joints to change as a function of the object shape. An optimization problem requiring that the gripper apply a constant total contact force with a minimum actuation force was formulated using a Genetic Algorithm (GA) approach in order to obtain a set of designs with geometrical parameters such as position and size of various elements of the fingers as well as stiffness of the joints. The optimization process was also repeated for the fixed stiffness case to compare the two approaches, demonstrating that the addition of variable stiffness allowed to achieve the same grasp with less actuation force.

关键词： Torque Shape Friction Force Grasping Grippers Joining processes Robots Tendons Genetic algorithms

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Exploiting 3D Grids for Indoor SLAM in Featureless Scenarios

Exploiting 3D Grids for Indoor SLAM in Featureless Scenarios

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Luís Garrote Ulisses Reverendo Urbano J. Nunes Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra Portugal

ISBN: (数字)9798350352344

ISBN: (纸本)9798350352351

Accurate multi-sensor localization is a challenging task in the navigation of AMRs. Precise localization strategies are essential for AMRs to be able to perform with safety their missions in their surrounding environments. This work proposes a novel ROS-based modular 3D grid-based particle filter-based framework that can be used for Simultaneous Localization and Mapping (SLAM) or as a standalone robust localization strategy. The framework uses odometry and 3D LiDAR data as inputs for localization and SLAM. To further improve localization and representation alignment, a pose refinement stage is employed using Levenberg-Marquardt minimization. The refinement stage considers keypoints in the environment to improve localization and uses the raw 3D point cloud for map maintenance. A pyramid-like 3D grid resolution is used to aid the refinement of the representation, improving pose estimates in featureless scenarios. Experimental validation was carried out with data acquired using an in-house platform, in a set of indoor and semi-structured scenarios comprised of critical featureless areas. The obtained results highlight the robustness of the proposed framework in both SLAM and localization tasks. The code (ROS package) is made available in a GitHub repository 1 .

关键词： Location awareness Point cloud compression Three-dimensional displays Simultaneous localization and mapping Navigation Robustness Safety

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Haptic System for Handwriting Training and Classification

Haptic System for Handwriting Training and Classification

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International Conference on Control, Automation, robotics and Vision (ICARCV)

作者： João P. Ferreira António P. Coimbra Manuel Crisóstomo Department of Electrical Engineering Superior Institute of Engineering of Coimbra Portugal Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Nowadays, handwriting still plays an important role in our lives, even though we live in a progressively more digital era. Therefore, losing the ability to write, whether because of physical or psychological complications, can become a huge handicap. The system described herein aims, in a first stage, to enable handwriting training through haptic guidance. The implementation was performed in Visual Studio and with the use of OpenHaptics toolkit. The second stage consists in character classification, written using a haptic joystick. The pre-eminent procedure for digit classification consists in the use of Histogram of Oriented Gradients coupled with a multiclass Support Vector Machine (HOG-SVM) whereas for letters the strategy involves using a Convolutional Neural Network (CNN).

关键词： Training Support vector machines Visualization Accuracy Shape Writing Time measurement Haptic interfaces Trajectory Convolutional neural networks

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Soft Sensors for Industrial Applications: Comparison of Variables Selection Methods and Regression Models

Soft Sensors for Industrial Applications: Comparison of Vari...

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2023 International Conference on Control, Automation and Diagnosis, ICCAD 2023

作者： Leiria, Joana Salles, Rodrigo Mendes, Jerome Sousa, Pedro University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Coimbra3030-290 Portugal Oncontrol Technologies Rua Cidade Poitiers Coimbra3000-108 Portugal

ISBN: (纸本)9798350347074

The digitalization of industrial environments has enabled the development of tools that make the production process more efficient and safer. In this sense, the Soft Sensor (SS) plays a fundamental role. Through historical data and indirect measurements, it is possible to estimate the value of important variables that are difficult to measure. This paper presents the SS development process: data collection and pre-processing, variable selection, model selection for SS implementation, model training and testing, and performance evaluation. The selection of variables was made with the help of Pearson Correlation, Mutual Information, and fastTracker algorithm techniques. For the implementation of SS have been tested several models: Multiple Linear Regression, Ridge Regression, Least Absolute Shrinkage and Selection Operator, Elastic Net, Support Vector Regression and Gaussian Mixture Models. Four datasets were used to test the development of the SS. © 2023 IEEE.

关键词： Correlation methods

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Integrated Data-Driven and Model-Based Trade-off Analysis of Sensor Suite System for Autonomous Ground Vehicle Navigation

Integrated Data-Driven and Model-Based Trade-off Analysis of...

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IEEE International Symposium on systems engineering (ISSE)

作者： Sai Sandeep Damera Praveen Kumar M. S John S. Baras Department of Electrical and Computer Engineering Institute for Systems Research (ISR) at the University of Maryland College Park MD USA

ISBN: (数字)9798350353723

ISBN: (纸本)9798350353730

In recent years, the deployment of robots for commercial applications has surged, emphasizing the need to incorporate autonomy into these systems. The widespread adoption of autonomous robots across diverse fields thus necessitates a rigorous design framework. To address this, we recently proposed an Integrated Data-Driven and Model-Based systems engineering (IDDMBSE) framework—an extension of our traditional Model-Based systems engineering (MBSE). Our IDDMBSE framework aims to enable trusted autonomy in autonomous systems. In this work, we outline a methodology for trade-off analysis within the IDDMBSE framework, supporting the development of autonomous robots based on functional, performance, and programmatic requirements. The method comprises two key components: 1) Model-Based Optimization (MBO) - Leveraging domain-specific knowledge, MBO facilitates efficient simulation-based design, and 2) Data-Driven Evaluation (DDE) - Decoupling DDE from MBO allows effective exploration and statistical analysis of the design space. We demonstrate the utility of the proposed framework by using it to perform design space exploration and trade-off analysis for the design of the sensor suite of an autonomous ground robot. Our results show how a classical design problem that is often deemed intractable using model-based methods and unscalable using data-driven methods can benefit through the use of our hybrid framework.

关键词： Space vehicles Analytical models Statistical analysis Navigation Robot sensing systems Land vehicles Space exploration Optimization Autonomous robots

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Two-Stream Architecture with Contrastive and Self-Supervised Attention Feature Fusion for Error-related Potentials Classification

Two-Stream Architecture with Contrastive and Self-Supervised...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Luís Garrote João Perdiz Mine Yasemin Gabriel Pires Urbano J. Nunes Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Portugal Polytechnic Institute of Tomar Portugal

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Error-related potentials (ErrPs) extracted from electroencephalographic signals hold potential for application in Brain-Machine Interfaces, in contexts such as robot teleoperation or shared control in assistive platforms. Due to difficulties in signal classification, in part caused by its non-stationary and noisy nature, their use has not been fully realized *** work proposes a new approach to ErrP classification based on a two-stream deep learning architecture with three training stages. Its first stage is a self-supervised autoencoder architecture with a multi-head attention layer providing relevant latent features. The second stage comprises a supervised contrastive learning approach considering two backbone networks, where one inherits weights from the first stage and the other is updated by considering the feature embeddings distribution. The final stage comprises supervised classification, where the two backbones are fused and used to classify the input EEG signal. At the end of the three stages, a data-driven two-stream ErrP model is ***-five variants of the proposed approach using the Deep Convolutional Network, Shallow Convolutional Network and EEGNet backbones were tested in an ablation study and benchmarked against a large number of classical classification methods, using data from the BNCI dataset intended to assess cross subject generalization capabilities. The proposed approach obtained the best results overall, highlighting the approach’s capabilities in capturing relevant representations of the EEG signal.

关键词： Training Electric potential Deep architecture Contrastive learning Feature extraction Brain modeling Electroencephalography Noise measurement Convolutional neural networks Robots

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Exploiting Object-based and Segmentation-based Semantic Features for Deep Learning-based Indoor Scene Classification

arXiv

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arXiv 2024年

作者： Pereira, Ricardo Garrote, Luís Barros, Tiago Lopes, Ana Nunes, Urbano J. University of Coimbra Institute of Systems and Robotics Department of Electrical and Computer Engineering Portugal Polytechnic Institute of Tomar Portugal

Indoor scenes are usually characterized by scattered objects and their relationships, which turns the indoor scene classification task into a challenging computer vision task. Despite the significant performance boost in classification tasks achieved in recent years, provided by the use of deep-learning-based methods, limitations such as inter-category ambiguity and intracategory variation have been holding back their performance. To overcome such issues, gathering semantic information has been shown to be a promising source of information towards a more complete and discriminative feature representation of indoor scenes. Therefore, the work described in this paper uses both semantic information, obtained from object detection, and semantic segmentation techniques. While object detection techniques provide the 2D location of objects allowing to obtain spatial distributions between objects, semantic segmentation techniques provide pixel-level information that allows to obtain, at a pixel-level, a spatial distribution and shape-related features of the segmentation categories. Hence, a novel approach that uses a semantic segmentation mask to provide Hu-momentsbased segmentation categories' shape characterization, designated by Segmentation-based Hu-Moments Features (SHMFs), is proposed. Moreover, a three-main-branch network, designated by GOS2F2App, that exploits deep-learning-based global features, object-based features, and semantic segmentation-based features is also proposed. GOS2F2App was evaluated in two indoor scene benchmark datasets: SUN RGB-D and NYU Depth V2, where, to the best of our knowledge, state-of-the-art results were achieved on both datasets, which present evidences of the effectiveness of the proposed approach. © 2024, CC BY.

关键词： Object detection

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Costmap-based Local Motion Planning using Deep Reinforcement Learning

Costmap-based Local Motion Planning using Deep Reinforcement...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Luís Garrote João Perdiz Urbano J. Nunes Department of Electrical and Computer Engineering and the Institute of Systems and Robotics University of Coimbra Portugal

Local motion planning is an essential component of autonomous robot navigation systems as it involves generating collision-free trajectories for the robot in real-time, given its current position, the map of the environment and a goal. Considering an a priori goal path, computed by a global planner or as the output of a mission planning approach, this paper proposes a Two-Stream Deep Reinforcement Learning strategy for local motion planning that takes as inputs a local costmap representing the robot’s surrounding obstacles and a local costmap representing the nearest goal path. The proposed approach uses a Double Dueling Deep Q-Network and a new reward model to avoid obstacles while trying to maintain the lateral error between the robot and the goal path close to zero. Our approach enables the robot to navigate through complex environments, including cluttered spaces and narrow passages, while avoiding collisions with obstacles. Evaluation of the proposed approach was carried out in an in-house simulation environment, in five scenarios. Double and Double Dueling architectures were evaluated; the presented results show that the proposed strategy can correctly follow the desired goal path and, when needed, avoid obstacles ahead and recover back to following the goal path.

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Adaptive inter-intradomain alignment network with class-aware sampling strategy for rolling bearing fault diagnosis

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Science China(Technological Sciences) 2023年第10期66卷 2862-2870页

作者： GAO QinHe HUANG Tong ZHAO Ke SHAO HaiDong JIN Bo LIU ZhiHao WANG Dong National Key Discipline Laboratory of Armament Launch Theory&Technology Rocket Force University of EngineeringXi’an 710025China Key Laboratory of Road Construction Technology&Equipment Ministry of Education Chang’an UniversityXi’an 710054China College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China Institute of Systems and Robotics Department of Electrical and Computer EngineeringUniversity of CoimbraCoimbra 3030-290Portugal

Existing unsupervised domain adaptation approaches primarily focus on reducing the data distribution gap between the source and target domains,often neglecting the influence of class information,leading to inaccurate alignment *** by this observation,this paper proposes an adaptive inter-intra-domain discrepancy method to quantify the intra-class and inter-class discrepancies between the source and target ***,an adaptive factor is introduced to dynamically assess their relative *** upon the proposed adaptive inter-intradomain discrepancy approach,we develop an inter-intradomain alignment network with a class-aware sampling strategy(IDAN-CSS)to distill the feature *** classaware sampling strategy,integrated within IDAN-CSS,facilitates more efficient *** multiple transfer diagnosis cases,we comprehensively demonstrate the feasibility and effectiveness of the proposed IDAN-CSS model.

关键词： unsupervised domain adaptation inter-class domain discrepancy intra-class domain discrepancy class-aware sampling strategy

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Monocular Person Localization with Lidar Fusion for Social Navigation

Monocular Person Localization with Lidar Fusion for Social N...

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European Conference on Mobile Robots (ECMR)

作者： Sedat Dogru Carlos A. Silva Lino Marques Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

Smooth social navigation requires not only detection of the people around the robot, but also accurate localization of the people, a process difficult to achieve with a single sensing modality. Hence, literature has focused on various fusion approaches, such as RGB-D, ROI based lidar vision fusion, or Artificial Neural Network (ANN) based lidar vision fusion. However, monocular photogrammetry has always been ignored in the literature. In this work, we propose a fusion approach based on monocular positon estimation and lidar, and show the effectivenes of the approach both on a public dataset and a purpose built dataset with different cameras.

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