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2023 IEEE International Symposium on Sensorless Control for electrical Drives, SLED 2023

作者： Shim, Jaehoon Lee, Sangwon Jeon, Daesung Ha, Jung-Ik Seoul National University Department of Electrical and Computer Engineering Seoul Korea Republic of HD Hyundai Robotics Co. Ltd. Youngin-si Korea Republic of

ISBN: (纸本)9798350335422

In analyzing dynamic multi-joint robots, the accuracy of model-based robot dynamics is significantly affected by various forces that are not adequately modeled in the joints. Among these factors, joint friction torque plays a crucial role. Numerous studies have focused on modeling joint friction, and this paper proposes joint friction models for multi-joint robots based on a data-driven approach. The key ideas are summarized into two aspects. First, comprehensive data acquisition is essential to utilize the data-driven model. This study covers a method to obtain data that includes comprehensive information which is insufficiently addressed in previous research. Second, previous studies using neural networks have only considered partial joint information. This paper proposes a model that utilizes joint velocity, torque, and temperature information together to develop a comprehensive friction model. The proposed friction torque models are verified using a real-world 6-axis collaborative robot. © 2023 IEEE.

关键词： Data acquisition

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Survey of Disturbance Observer-based Controller and Related Challenging Problems

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Journal of Institute of Control, robotics and Systems 2024年第4期30卷 363-372页

作者： Park, Gyunghoon Back, Juhoon School of Electrical and Computer Engineering University of Seoul Korea Republic of School of Robotics Kwangwoon University Korea Republic of

Handling disturbance and model uncertainty is a long-standing but troublesome problem in control design. Accordingly, the disturbance observer (DOB) has received keen interest over decades because it is an easy-to-use tool for robustifying control systems. The core of the DOB-based controller (DOBC) is to estimate and compensate for the combined effect of disturbance and plant uncertainty, and diverse design philosophies have been suggested. In this survey-style paper, we first introduce several representative DOBCs that have been widely employed in the literature, including unknown input observer-based control, extended state observer-based control, inversion DOBC, and nonlinear DOBC, and focus on the rationale behind the estimation of uncertain factors. The state-of-the-art in both the theory and application of these DOBCs are then discussed. Specifically, it is shown that the DOBC theory can contribute to not only classical robust control problems but also emerging research topics, such as multi-agent systems, safety-critical control, and cyber-security. Finally, some challenging issues related to the DOBC are listed, which call for further investigation in the future. © ICROS 2024.

关键词： Controllers

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Optimal view point and kinematic control for grape stem detection and cutting with an in-hand camera robot

Optimal view point and kinematic control for grape stem dete...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Sotiris Stavridis Zoe Doulgeri Automation & Robotics Lab School of Electrical & Computer Engineering Aristotle University of Thessaloniki Greece

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this work, a methodology to find the best view of a grape stem and approach angle in order to crop it is proposed. The control scheme is based only on a classified point cloud obtained by the in-hand camera attached to the robot’s end effector without continuous stem tracking. It is shown that the proposed controller finds and reaches the optimal view point and subsequently the stem fast and efficiently, accelerating the overall harvesting procedure. The proposed control scheme is evaluated through experiments in the lab with a UR5e robot with an in-hand RealSense camera on a mock-up vine.

关键词： Point cloud compression Robot vision systems Pipelines Crops Kinematics Cameras End effectors Reliability Intelligent robots

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Improving Grasping Performance of Underactuated Two Finger Robotic Hands Using Variable Stiffness

Improving Grasping Performance of Underactuated Two Finger R...

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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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Multimodal Human Detection Using YOLO and Representation Learning for Robot Perception

Multimodal Human Detection Using YOLO and Representation Lea...

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Iberian robotics Conference (ROBOT)

作者： Kennedy O. S. Mota Diogo S. De Oliveira Luís Garrote Cristiano Premebida Department of Electrical and Computer Engineering University of Coimbra Institute of Systems and Robotics Coimbra Portugal

ISBN: (数字)9798350376364

ISBN: (纸本)9798350376371

This work concentrates on the problem of multisensor people detection using YOLO trained on four distinct modalities: depth and intensity LiDAR-maps, RGB, and ‘thermal’ images. RGB cameras, ubiquitous in this application domain, offer great resolution but struggle with adverse lighting conditions resulting in overexposed or underexposed images which then impact negatively on the performance of the algorithms. Thermal (long-wave infrared) cameras are more resilient against varying light conditions and provide complementary textural features, although with lower resolution when compared to RGB cameras. LiDAR sensors, while having a significantly low resolution, contribute to a physically interpretable mapping of the environment providing precise information regarding size/dimension and location of the objects. The main goal of this work is to tackle people detection using deep-models trained on single and multi-modality representations. To support the experimental part this work introduces a new multimodal dataset (called MID-3K). MID-3K allows the development of data fusion strategies by leveraging four modalities (obtained from three distinct exteroceptive sensors mounted on a mobile robot). Leveraging on a single-modality YOLO framework, we propose a multimodal representation learning approach to improve the baseline performance and to capture more relevant features across all input modalities. The evaluation of the proposed detection pipeline is conducted on the MID-3K dataset, where the reported results are grounded on state-of-the-art performance measures. The new dataset is available in a GitHub repository 1 1 MID-3K dataset: https://***/MID-3K/.

关键词： YOLO Representation learning Training Image resolution Laser radar Robot vision systems Thermal sensors Sensor fusion Cameras Software development management

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A Hybrid EEG-EMG Framework for Humanoid Control using Deep Learning Transformers

A Hybrid EEG-EMG Framework for Humanoid Control using Deep L...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Olikkal, Parthan Pillai, Branesh M. Suthakorn, Jackrit Ali, Habib Vinjamuri, Ramana University of Maryland Baltimore County Vinjamuri lab Department of Computer Science and Electrical Engineering BaltimoreMD21220 United States Mahidol University Center for Biomedical and Robotics Technology Nakorm Pathom Salaya Thailand

ISBN: (纸本)9781665481090

Brain-computer interfaces (BCIs) offer promising solutions for upper limb rehabilitation. Despite advancements in deep learning, traditional models for motor rehabilitation using electroencephalography (EEG) or electromyography (EMG) to control assistive devices require enhancement. This study aims to enhance motor control capabilities by integrating EEG and EMG signals using a Transformer-based deep learning model. Ten able-bodies subjects performed center-out tasks on a low-cost upper limb rehabilitation table, capturing 2D kinematic data, EEG, and EMG signals simultaneously. The tasks varied in complexity across four levels. Preprocessed EEG and EMG signals were fused and given as input to the proposed model, which was evaluated using three performance metrics. Results showed that the EEG-EMG combined model achieved 87.27% accuracy across all the four levels. Furthermore, the model's output successfully controlled a humanoid robot to replicate similar movements. These findings highlight the efficacy of combined EEG-EMG data in improving accuracy and performance in BCI applications, advancing assistive technologies and neurorehabilitation interventions. © 2024 IEEE.

关键词： Anthropomorphic robots

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Multispectral Registration Algorithm Based on Fusion of Infrared and Color Images 16

Multispectral Registration Algorithm Based on Fusion of Infr...

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16th International Conference on Cyber-Enabled Distributed Computing and Knowledge Discover, CyberC 2024

作者： Wan, Teng Du, Shaoyi Zhang, Qiang Qi, Ying Li, Ce Northwest Normal University The College of Computer Science and Engineering Lanzhou China Xi'an Jiaotong University The Institute of Artificial Intelligence and Robotics The College of Artificial Intelligence Xi'an China Lanzhou University of Technology The College of Electrical and Information Engineering Lanzhou China

ISBN: (纸本)9798331506896

Multispectral data fusion is a fundamental research in the fields of artificial intelligence and computer vision, which provides technical support and theoretical basis for remote sensing, autonomous driving, and medical imaging. In this paper, we present a robust registration algorithm to align RGB-D scans. In colored point clouds, limited by the principle of point cloud collection and generation, noise, and outliers are common and may cause registration failure. Therefore, the core of our method is to apply color information and kernel function to remove the influence of noise and outliers on the alignment accuracy. In the beginning, for the first time, we innovatively use the Cauchy kernel as the energy function of the point set registration to assign different weights to inliers, noise, and outliers to maximize their differentiation. Combined with the maximization optimization strategy, noise and outliers are removed to achieve robust registration. Secondly, since RGB-D data contain both shape and color information, the trimmed bidirectional color distance is applied to reduce the probability of noise and outliers being selected as the matching point pairs when establishing the correspondences. Moreover, the overall formulation is solved by the iterative optimization method. Experiments demonstrate that our algorithm is more robust and accurate than previous point set registration algorithms, especially when aligning point sets containing noise and outliers. © 2024 IEEE.

关键词： Image registration

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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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Human-Inspired Anticipatory Motion Planning for Robotic Manipulators in Contact Tasks

Human-Inspired Anticipatory Motion Planning for Robotic Mani...

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IEEE International Conference on robotics and Biomimetics

作者： Malak Slim Naseem Daher Imad H. Elhajj Electrical and Computer Engineering Department Vision and Robotics Lab American University of Beirut Lebanon

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

In this work, we present a novel solution aimed at improving robotic manipulators' performance in contact tasks. Inspired by the human motor control system, which relies on a feedforward mechanism to anticipate and plan movements based on the physical properties of the target environment, our approach plans the robot's motion during the reaching phase, prior to contact. To validate our approach, we conducted experiments using the KUKA youBot arm in two distinct environments, represented by soft and hard materials. Results showed that the robot exhibited compliant behavior, with an average reduction of 71% in overshoot, 60% in rise-time, and 68% steady-state error of the force control response during contact.

关键词： Robot motion Motor drives computer vision Estimation Manipulators Planning Steady-state Force control Feedforward systems Robots

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A Design for Improvement of Visual SLAM in Dynamic Environments Using Feature-Point Removal of Moving Persons

A Design for Improvement of Visual SLAM in Dynamic Environme...

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2023 International Automatic Control Conference, CACS 2023

作者： Song, Kai-Tai Meng, Ching-Hao National Yang Ming Chiao Tung University Institute of Electrical and Control Engineering College of Electrical and Computer Engineering Hsinchu886-3-5731865 Taiwan National Yang Ming Chiao Tung University Graduate Degree Program of Robotics College of Engineering Hsinchu Taiwan

ISBN: (纸本)9798350306354

This paper presents a design to improve the robustness of visual SLAM(vSLAM). A processing step of feature-removal is added to the tracking thread of the conventional ORB-SLAM2 algorithm to improve the localization accuracy of a mobile robot in an environment with moving persons. Instance segmentation and motion tracking are intergrated to identify motion state of people in an image. ORB feature points belonging to moving persons are removed for further processing of the vSLAM pipeline. The advantage of this method is that the vSLAM can remove feature points of moving people, while retain those belonging to static people in the environment, which improves the accuracy of robot pose estimation. The improved ORB-SLAM2 algorithm has been implemented in a NVIDIA Xavier embedded system, which is integrated to a mobile robot. In practical robot navigation experiments, the average positioning error of the proposed method is within 4cm for 22.4m travel distance. Compared with conventional ORB-SLAM2, the average accuracy of our vSLAM method improves 97% in a dynamic environment with moving people. © 2023 IEEE.

关键词： Mobile robots

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