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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Dennis Kundrat Alfred Schell Jelle Bijlsma Giulio Dagnino Robotics and Mechatronics University of Twente Enschede The Netherlands Fraunhofer IMTE Fraunhofer Research Institution for Individualized and Cell-Based Medical Engineering Lübeck Germany

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Increasing interest surrounds the use of robotic and computer technologies for precise endovascular interventions. However, a limitation in current robotic procedures is the reliance on 2D fluoroscopy for surgical navigation and lack of haptic guidance. In addressing this, we present an improved guidance framework for CathBot, our MR-compatible endovascular robot. This includes visual guidance through fluoroscopy and MRI, along with vision-based haptic guidance. The framework enables real-time acquisition and processing of fluoroscopy and MRI images, integrated into the robotic platform’s control architecture for enhanced guidance. Assessment experiments show the potential of this approach, also marking a crucial step towards MR-guided robot-assisted interventions, aiming to reduce ionizing radiation exposure, enabling functional imaging and enhance the overall intervention workflow.

关键词： Visualization Three-dimensional displays Magnetic resonance imaging Pose estimation Process control Software Real-time systems Haptic interfaces Robots Remote control

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A Gravity Compensation Strategy for On-ground Validation of Orbital Manipulators

A Gravity Compensation Strategy for On-ground Validation of ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Marco De Stefano Ria Vijayan Andreas Stemmer Ferdinand Elhardt Christian Ott German Aerospace Center (DLR) Institute of Robotics and Mechatronics Wessling Germany TU Wien and German Aerospace Center (DLR)

The on-ground validation of orbital manipulators is a challenging task because the robot is designed for a gravity-free operational environment, but it is validated under the effect of gravity. As a consequence, joint torque limits can be easily reached in certain configurations when gravity is actively compensated by the joints. Hence, the workspace for on-ground testing is restricted. In this paper, an optimal strategy is proposed for achieving gravity compensation of an orbital manipulator arm on ground. The strategy minimizes the joint torques acting on the manipulator by solving an optimization problem and it computes the necessary forces to be tracked by an external carrier. Hence, full gravity compensation is achieved for the orbital manipulator. Experimental results validate the effectiveness of the method on the DLR CAESAR space robot, which uses a cable suspended system as external carrier to track the desired gravity compensation force, resulting from the proposed method.

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Continuous learning with random memory for object detection in robotic applications

Continuous learning with random memory for object detection ...

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2021 International Conference "Nonlinearity, Information and robotics", NIR 2021

作者： Nenakhov, Ivan Mazhitov, Ruslan Artemov, Kirill Zabihifar, SeyedHassan Semochkin, Aleksandr Kolyubin, Sergey ITMO University Biomechatronics and Energy-Efficient Robotics Lab St. Petersburg Russia PJSC Sberbank Sber Robotics Laboratory Moscow Russia Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (纸本)9781665424066

If the robot is to interact with environment designed for humans it has to be able to cope with new objects in it's surrounding, and not only to classify but also effectively localize objects in its reach based on visual sensing. We cannot predict all objects the robot will face. So it needs to learn them while operating, without an external update of software. One of the promising approaches to handle this task is using detectors based on convolution neural networks (CNN) with continuous learning setup. However, there is a well-known problem of catastrophic forgetting (CF) that negatively affects accuracy of neural networks, the main algorithms in object recognition nowadays. Moreover, we need to reach a trade off between training time (because it is unacceptable if the robot trains for hours) and good acccuracy of detection. In this paper we review some of the latest approaches in the field and adapt one of SOtA methods in classification task to detection task. Particularly, we adapt YOLOv5 to work in scenario with continuous learning by adding random memory mechanism from [1] and applying layer freezing in order to increase training speed. We benchmark the proposed method using iCubWorld Transformations dataset and report results with significantly improved metrics and inference speed. © 2021 IEEE.

关键词： Object detection

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FaceEngine: A Tracking-Based Framework for Real-Time Face Recognition in Video Surveillance System

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SN Computer Science 2024年第5期5卷 609页

作者： Imran, Ahsan Ahmed, Riad Hasan, Md Mehedi Ahmed, M. Helal Uddin Azad, A.K.M. Alyami, Salem A. Department of Robotics and Mechatronics Engineering University of Dhaka Dhaka 1000 Bangladesh Department of Computer Science and Engineering BRAC University Dhaka 1212 Bangladesh Department of Management Information Systems University of Dhaka Dhaka 1000 Bangladesh Department of Mathematics and Statistics College of Science Imam Mohammad Ibn Saud Islamic University (IMSIU) Riyadh 11432 Saudi Arabia

The escalating concern over worldwide security and criminal activities has led to the emergence and significance of closed-circuit television video surveillance systems as an essential tool for diverse security purposes. These systems are extensively implemented and serve a crucial function in the surveillance and upkeep of security. The predominant purpose of video surveillance systems is to gather data primarily for evidentiary purposes subsequent to the occurrence of a criminal incident. The demand for video surveillance systems capable of autonomously monitoring and promptly identifying criminals or intruders in real-time is steadily increasing. Nevertheless, the existing facial recognition methods pose difficulties in reliably identifying individuals who are in motion within a video frame. Moreover, conventional approaches necessitate a substantial quantity of photographs in order to achieve precise recognition following the acquisition of an individual’s facial pattern. In order to tackle these concerns, we developed the implementation of input optimisation algorithms alongside a novel framework for real-time face recognition in the context of video surveillance. The input optimization algorithms, integrated with adaptive thresholding techniques, effectively reduce the need for manual outlier removal by actively identifying outliers for each specific case. The application of this optimisation strategy has demonstrated a substantial enhancement in both the efficiency and precision of our system in comparison to alternative baseline methodologies. Through the use of a reduced set of input image, our system is capable of attaining a heightened degree of improvements. Specifically, employing tracking and temporal voting techniques enables our system to accomplish a real-time face recognition accuracy of 90.91%. The findings of this study suggest that our approach has the potential to be a valuable tool in various applications that necessitate rapid and precise fac

关键词： Computer vision Deep learning Face recognition Image processing Tracking Video surveillance

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Foreword

Lecture Notes in Networks and Systems

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Lecture Notes in Networks and Systems 2023年 630 LNNS卷 v页

作者： Szewczyk, Roman Zieliński, Cezary Kaliczyńska, Malgorzata Bučinskas, Vytautas Warsaw Poland Faculty of Mechanics Department of Mechatronics Robotics and Digital Manufacturing Vilnius Gediminas Technical University Vilnius Lithuania

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A differential geometric description of thermodynamics in continuum mechanics with application to Fourier-Navier-Stokes fluids

arXiv

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arXiv 2022年

作者： Califano, Federico Rashad, Ramy Stramigioli, Stefano Robotics and Mechatronics Department University of Twente Enschede7522 NH Netherlands

A description of thermodynamics for continuum mechanical systems is presented in the coordinate-free language of exterior calculus. First, a careful description of the mathematical tools that are needed to formulate the relevant conservation laws is given. Second, following an axiomatic approach, the two thermodynamic principles will be described, leading to a consistent description of entropy creation mechanisms on manifolds. Third, a specialisation to Fourier-Navier-Stokes fluids will be carried through. Copyright © 2022, The Authors. All rights reserved.

关键词： Thermodynamics

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A Learning-based Controller for Multi-Contact Grasps on Unknown Objects with a Dexterous Hand

A Learning-based Controller for Multi-Contact Grasps on Unkn...

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

作者： Dominik Winkelbauer Rudolph Triebel Berthold Bäuml DLR Institute of Robotics & Mechatronics Germany Learning AI for Dextrous Robots Lab (***) Technical University of Munich Germany Karlsruhe Institute of Technology (KIT) Germany

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Existing grasp controllers usually either only support finger-tip grasps or need explicit configuration of the inner forces. We propose a novel grasp controller that supports arbitrary grasp types, including power grasps with multi-contacts, while operating self-contained on before unseen objects. No detailed contact information is needed, but only a rough 3D model, e.g., reconstructed from a single depth image. First, the external wrench being applied to the object is estimated by using the measured torques at the joints. Then, the torques necessary to counteract the estimated wrench while keeping the object at its initial pose are predicted. The torques are commanded via desired joint angles to an underlying joint-level impedance controller. To reach real-time performance, we propose a learning-based approach that is based on a wrench estimator- and a torque predictor neural network. Both networks are trained in a supervised fashion using data generated via the analytical formulation of the controller. In an extensive simulation-based evaluation, we show that our controller is able to keep 83.1% of the tested grasps stable when applying external wrenches with up to 10 N. At the same time, we outperform the two tested baselines by being more efficient and inducing less involuntary object movement. Finally, we show that the controller also works on the real DLR-Hand II, reaching a cycle time of 6 ms. Website: ***/grasping

关键词： Solid modeling Torque Three-dimensional displays Power measurement Neural networks Robot sensing systems Real-time systems Torque measurement Impedance Intelligent robots

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Comparison of Facial Expression and Cerebral Blood Flow Sensors for Emotion Estimation Using Communication Robots 23

Comparison of Facial Expression and Cerebral Blood Flow Sens...

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Hasegawa, Naoya Hirahara, Rin Kurokawa, Shohei Takahashi, Yoshihiko Kindai University Technical College Department of Comprehensive Engineering - Mechanical Systems Course 7-1 Kasugaoka Mie Nabari518-0459 Japan Kanagawa Institute of Technology Department of Robotics and Mechatronics 1030 Shimo-Ogino Kanagawa Atsugi243-0292 Japan

ISBN: (纸本)9788993215274

In this study, a communication system that quantifies human emotions and provides feedback control was fabricated for a communication robot. The robot can provide suggestions to users and relay transmissions during human-to-human communication. Based on the emotions of the listener;the speed, words, and tone of the speaker are selected. Furthermore, the sensor was adopted to estimate emotions using facial expressions. To investigate its reliability, the results were compared with those from cerebral blood flow sensors which have been reported in numerous studies. There are several methods for measuring cerebral blood flow;however near-infrared spectroscopy (NIRS) sensors are used by placing them on either side of the subject's forehead to determine the right and left brains activity based on the flow rate of hemoglobin in the blood. The right-brain is dominant in spatial cognition, including visual and auditory perception, whereas the left-brain is dominant in language comprehension, reasoning, and relating information to knowledge in memory. The experimental results of the face sensors and the NIRS sensors are determined when a soap bubble robot ejects bubbles at the subject. © 2023 ICROS.

关键词： Infrared devices

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An Analysis of Robotic Dog’s Machine Learning-Based Detection for Pedestrians and Vehicles

An Analysis of Robotic Dog’s Machine Learning-Based Detecti...

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Intelligent Computing and robotics (ICICR), International Conference on

作者： M. Hassan Tanveer Aiden Kovarovics Charles Koduru Razvan Cristian Voicu Carrington Chun Ghulam Mahdi Dept. of Robotics and Mechatronics Kennesaw State University Marietta GA USA Kimberly Clark Coorporation Roswell Innovation Center Atlanta GA USA

ISBN: (数字)9798350372274

ISBN: (纸本)9798350372281

In this paper, we discuss the deployment of an innovative autonomous inspection system to facilitate data collection, specifically focusing on pedestrian and vehicle counts within transportation infrastructure. Utilizing bio-inspired robots, such as a robot dog, we explore new horizons in data gathering that supersede conventional methods. The robot dog, with its ability to navigate multiple terrains, acts as the primary data collection agent, ensuring accuracy and comprehensiveness. These technologies together pave the way for real-time monitoring of pedestrian and vehicle flow, offering a holistic view of transportation usage and patterns. By integrating this data with Intelligent Transportation Systems (ITS), the paper highlights the potential for creating smarter and more efficient transportation networks. This contemporary approach to data collection aligns with the rigorous standards and guidelines set forth by entities like AASHTO, emphasizing the role of automated systems in shaping the future of transportation management and infrastructure monitoring.

关键词： Pedestrians Navigation Transportation Dogs Inspection Data collection Robot sensing systems

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Know your limits! Optimize the robot’s behavior through self-awareness

Know your limits! Optimize the robot’s behavior through sel...

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IEEE-RAS International Conference on Humanoid Robots

作者： Esteve Valls Mascaró Dongheui Lee Autonomous Systems Technische Universität Wien (TU Wien) Vienna Austria German Aerospace Center Institute of Robotics and Mechatronics (DLR) Wessling Germany

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

As humanoid robots transition from labs to realworld environments, it is essential to democratize robot control for non-expert users. Recent human-robot imitation algorithms focus on following a reference human motion with high precision, but they are susceptible to the quality of the reference motion and require the human operator to simplify its movements to match the robot’s capabilities. Instead, we consider that the robot should understand and adapt the reference motion to its own abilities, facilitating the operator’s task. For that, we introduce a deep-learning model that anticipates the robot’s performance when imitating a given reference. Then, our system can generate multiple references given a highlevel task command, assign a score to each of them, and select the best reference to achieve the desired robot behavior. Our Self-AWare model (SAW) ranks potential robot behaviors based on various criteria, such as fall likelihood, adherence to the reference motion, and smoothness. We integrate advanced motion generation, robot control, and SAW in one unique system, ensuring optimal robot behavior for any task command. For instance, SAW can anticipate falls with 99.29% accuracy.

关键词： Robot motion Adaptation models Accuracy Natural languages Humanoid robots Kinematics Trajectory Robots Physics Self-aware

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