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检索条件"机构=Computer Science and Robotics"

共 7838 条记录，以下是511-520 订阅

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Robust Stability Degree Maximization Cable Vertical Stabilization Systems

Robust Stability Degree Maximization Cable Vertical Stabiliz...

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International Ural Conference on Measurements (UralCon)

作者： Alexander Sobol Sergey Gayvoronskiy Tatiana Ezangina School of computer Science & Robotics National Research Tomsk Polytechnic University Tomsk Russia

We are examining a system that stabilizes a cable's verticality using interval parameters. We have developed a third-degree characteristic polynomial with interval coefficients to achieve optimal performance. The system's stability degree determines its speed, so our goal is to maximize its robust stability degree. We construct possible vertex critical root diagrams for a third-degree system to accomplish this. These diagrams display the critical poles of the system on a single vertical line. We then determine the inverse images of these poles among the characteristic polynomial coefficients polyhedron vertices. By maximizing the robust stability degree in the obtained test vertices of each critical root diagram, we can ensure the system's maximum performance.

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Visuo-Haptic Mixed Reality for Interactive Therapy With Temperature Stimuli: A Preliminary Study

Visuo-Haptic Mixed Reality for Interactive Therapy With Temp...

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Jabrail Chumakov Manuela Chessa Nikhil Deshpande Advanced Robotics Istituto Italiano di Tecnologia (IIT) Genova Italy School of Computer Science University of Genova Genova Italy School of Computer Science University of Nottingham Nottingham UK

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

We introduce a novel Mixed Reality (MR) approach incorporating thermal feedback alongside traditional haptic and immersive visual stimuli to enhance virtual reality (VR) based interactive therapy. In this research, the system leverages the WEART TouchDIVER TM haptic glove, providing accurate tac-tile and temperature sensations at the fingertips, allowing a more intuitive and immersive MR interaction experience. The preliminary evaluation of the proposed approach was done through a visuo-thermo-haptic experiment aimed at determining how quickly and accurately users are able to categorize objects at various temperatures. The study involves 15 subjects, each performing 12 trials to grasp objects across five different tem-peratures. The results indicate that user discrimination between objects is most accurate at ambient temperature and decreases progressively when exposed to very hot, hot, very cold, and cold temperatures, respectively. The experimental findings demon-strate that incorporating thermal feedback in conjunction with visual and haptic stimuli can significantly enhance MR-based therapy techniques by providing subjects with a more realistic, immersive, and engaging experience.

关键词： Temperature sensors Surveys Visualization Accuracy Medical treatment Mixed reality Neural engineering User experience Haptic interfaces Sorting

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Modal Controllers Design of Interval Systems With Aperiodic Degree of Stability Based on the Method of Dominant Poles

Modal Controllers Design of Interval Systems With Aperiodic ...

引用

International Conference on Control Systems, Mathematical Modelling, Automation and Energy Efficiency (SUMMA)

作者： Tatiana Ezangina Sergey Gayvoronskiy Alexander Sobol School of Computer Science & Robotics National Research Tomsk Polytechnic University Tomsk Russia

The article deals with the problem of determining the parameterizations of a linear controller that provides aperiodic transient processes in a system with interval parameters. To cope with the above mentioned issue, a decomposition approach is proposed. It is based on dividing the characteristic polynomial of the system into a dominant one, which determines the dominant real pole, and a free one, which specifies the location of the remaining poles. In order to arrange the free poles at the maximum distance from the dominant one, the conditions for the maximum degree of dominance are specified. In this paper, a technique is described that has been developed for the modal design of linear controllers. Its application is considered through a numerical example.

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MechTac: A Multifunctional Tendon-Linked Optical Tactile Sensor for In/Out-the-Field-of-View Perception with Deep Learning 49

MechTac: A Multifunctional Tendon-Linked Optical Tactile Sen...

引用

49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Lu, Zhenyu Yue, Tianqi Zhao, Zhou Si, Weiyong Wang, Ning Yang, Chenguang University of the West of England Bristol Robotics Laboratory Bristol United Kingdom University of Bristol Department of Engineering Mathematics Bristol United Kingdom School of Computer Science Central China Normal University Wuhan China

ISBN: (纸本)9798350331820

Tactile sensors can be used for motion detection and object perception in robot manipulation. The contact detection within the camera's visual inspection area has been well-developed, but perception outside the field of view of the camera is overlooked. In this paper, we present a new tendon-linked tactile sensor, MechTac, to achieve perceptions inside and outside the field of view. The MechTac is an evolution of the typical TacTip sensor with the following two advantages. 1) The ability to provide perception outside the field of view. This is achieved by using a network of braided tendons to transfer deformation from the blind perception regions (TacSide) to the visual areas (TacTip). 2) The tactility of the TacSide and TacTip is reflected by the movements of multiple papillae pins and visible markers on the pin tips on the inner surface of the TacTip. The pins and markers are differentially sensitive to various touch features, which is similar to the differentiated perceptual ability of humans. TacTip is more sensitive to small touches, corresponding to the fingertip, while the TacSide is less sensitive but has a larger perceptual area, corresponding to the middle part of the finger. Moreover, we propose a new deep learning method to decompose the mixed information affected by the TacSide and the TacTip. A modified DenseNet121 was specifically designed for object perception at the TacTip and localization at the TacSide. The experimental results show that prediction accuracy reaches about 98% for object perception or localization and over 99% for the case requiring two functions. © 2023 IEEE.

关键词： Cameras

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MOTPose: Multi-object 6D Pose Estimation for Dynamic Video Sequences using Attention-based Temporal Fusion

MOTPose: Multi-object 6D Pose Estimation for Dynamic Video S...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Arul Selvam Periyasamy Sven Behnke Autonomous Intelligent Systems Group Computer Science Institute VI – Intelligent Systems and Robotics – and the Center for Robotics and the Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Germany

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Cluttered bin-picking environments are challenging for pose estimation models. Despite the impressive progress enabled by deep learning, single-view RGB pose estimation models perform poorly in cluttered dynamic environments. Imbuing the rich temporal information contained in the video of scenes has the potential to enhance models’ ability to deal with the adverse effects of occlusion and the dynamic nature of the environments. Moreover, joint object detection and pose estimation models are better suited to leverage the co-dependent nature of the tasks for improving the accuracy of both tasks. To this end, we propose attention-based temporal fusion for multi-object 6D pose estimation that accumulates information across multiple frames of a video sequence. Our MOTPose method takes a sequence of images as input and performs joint object detection and pose estimation for all objects in one forward pass. It learns to aggregate both object embeddings and object parameters over multiple time steps using cross-attention-based fusion modules. We evaluate our method on the physically-realistic cluttered bin-picking dataset SynPick and the YCB-Video dataset and demonstrate improved pose estimation accuracy as well as better object detection accuracy.

关键词： Deep learning Accuracy Fuses Aggregates Pose estimation Video sequences Object detection

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Efficient-Net ASPP Deep Network for Malignant Ultrasound Breast Cancer Segmentation

Efficient-Net ASPP Deep Network for Malignant Ultrasound Bre...

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2023 IEEE International Conference on Emerging Trends in Engineering, sciences and Technology, ICES and T 2023

作者： Azam, Komal Azam, Muhammad Ashir Qureshi, Muhammad Ali Khan, Khan Bahadar Azam, Muhammad Adeel College Women University The Government Sadiq Department of Computer Science Bahawalpur Pakistan The Islamia University of Bahawalpur The Faculty of Engineering Bahawalpur Pakistan Istituto Italiano di Tecnologia Department of Advanced Robotics Genoa Italy

ISBN: (纸本)9781665455602

Worldwide, breast cancer is one of the main causes of mortality among women. Only through early recognition of symptoms is it possible to limit the incidence of premature deaths. Utilizing standard deep-learning semantic segmentation, tumors are automatically segmented. It requires minimal human contact. The standard methods are less efficient and precise in accuracy. In this paper, we propose an Efficient-Net based Atrous Spatial Pyramid Pooling (Efficient-Net ASPP) model for efficient and improved segmentation. The proposed model is comprised of an EfficientNetV2B3 backbone with dilated convolutional, skip connection, max pooling, and an ASSP block. A publicly available Malignant Breast Cancer Ultrasound (MBCU) subset image dataset was investigated in our study. Several baseline segmentation methods were compared with our proposed method. The quantitative benchmark analysis shows the proposed model for segmenting MBCU images achieved 70% intersection over union (IOU) and a 62% score on the Dice Similarity Coefficient (DSC) metrics. The proposed network improves MBCU image segmentation by 14% on DSC and 18% on IOU when compared to baseline models. © 2023 IEEE.

关键词： Ultrasonics

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'Thanks for the Practice!': LLM-Powered Social Robot as Tandem Language Partner at University 20

'Thanks for the Practice!': LLM-Powered Social Robot as Tand...

引用

20th Annual ACM/IEEE International Conference on Human-Robot Interaction, HRI 2025

作者： Ashok, Ashita Bruno, Barbara Helf, Tamara Berns, Karsten University of Kaiserslautern-Landau Department of Computer Science Kaiserslautern Germany Institute of Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798350378931

Large language models (LLMs), when integrated into social robots, have the potential to transform robot-assisted language learning by offering personalized, interactive communication. However, there is limited research exploring their potential to simultaneously reduce anxiety and enhance language-speaking skills among international university students, who often feel anxious when speaking a foreign language. This study addresses this gap by evaluating the impact of a humanoid robot powered by the OpenChat-3.5 LLM as a tandem partner for German language learning. Using a between-subjects design with 22 multilingual participants, two interaction conditions were tested: immersive (German-only) and bilingual (German-English). Our findings indicate that participants in the immersive mode reported experiencing significantly reduced perceived judgment by the robot compared to the bilingual mode. Although female participants showed a trend of greater improvement in learning gain, no significant gender differences were found. Open-ended feedback highlighted the need for enhanced contextual responses, slower speech rate, faster response times, and error corrections to enhance language speaking support. This study aims to advance social robots for learning by demonstrating the usage of generative AI in creating non-judgmental language practice scenarios. © 2025 IEEE.

关键词： Social robots

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An Augmented Reality Measurement Tool for the da Vinci Research Kit

An Augmented Reality Measurement Tool for the da Vinci Resea...

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International Symposium on Medical robotics (ISMR)

作者： Shiyue Wang Jing-Fen Wang Yubin Koh Haoying Zhou Anton Deguet Peter Kazanzides Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Purdue University West Lafayette IN USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

We present an augmented reality surgical robotics console for the da Vinci Research Kit (dVRK) that integrates a real-time distance measurement tool to enhance spatial awareness during robotic-assisted surgical procedures through an interactive system. The system integrates an RViz-based interface with ROS topics, enabling virtual cursor visualization and interactive controls for precise measurements on tissue or phantom models. It supports two distinct measurement approaches: (1) a virtual measurement capability, where the operator uses the two Master Tool Manipulators (MTMs) to drive virtual 3D measurement cursors in the stereo display (a “masters as mice” mode), and (2) a physical measurement tool, where the user teleoperates one or more Patient Side Manipulators (PSMs) to touch two features and report the intervening distance. We conduct a user study on virtual (MTM) and teleoperation (PSM) modes to assess performance and accuracy and gather user feedback. The results show a Root Mean Square Error (RMSE) of 2.19 mm to 7.25 mm across different modes and tasks. This work underscores the importance of such systems for enhancing accuracy and usability in endoscopic surgery, where spatial awareness and intraoperative assessment are critical. Code is available at https://***/capricieuxV/RVinci_***.

关键词： Visualization Three-dimensional displays Medical robotics Accuracy Surgery Manipulators Real-time systems Usability Root mean square Augmented reality

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A Modified Feature Selection Algorithm for Intrusion Detection System Based on Student Psychology-Based Optimization with Explainable AI

A Modified Feature Selection Algorithm for Intrusion Detecti...

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2023 IEEE Asia-Pacific Conference on computer science and Data Engineering, CSDE 2023

作者： Evan, Md. Raisul Islam Motiur Rahman, Mohammad Tanzir Mehedi, Sk. Nazib, Abdullah Islam, Rafiqul Rahman, Ziaur Mawlana Bhashani Science and Technology University Computer Science and Engineering Tangail1902 Bangladesh Mawlana Bhashani Science and Technology University Information and Communication Technology Tangail1902 Bangladesh School of Electrical Engineering and Robotics Queensland University of Technology BrisbaneQLD Australia School of Computing Mathematics and Engineering Charles Sturt University Bathurst Australia School of Computer Science Queensland University of Technology BrisbaneQLD Australia

ISBN: (纸本)9798350341072

Intrusion Detection Systems (IDS) play a vital role in safeguarding network security by monitoring and identifying both normal and intrusive activities. However, constructing an effective IDS from extensive datasets poses a significant challenge. The process of feature selection from these datasets is crucial, as it not only enhances accuracy but also reduces the time required to build the model. In this paper, we employ a modified version of the Student Psychology-Based Optimization (SPBO) algorithm to identify the most relevant features set for IDS. Following feature selection, various machine learning algorithms are employed for multi-level classification. To evaluate our approach, we utilize the NLS-KDD dataset. Our proposed algorithm surpasses several state-of-the-art feature selection methods in terms of accuracy, precision, recall, Fl-score, and finally faster convergence. Specif-ically, our proposed bio-inspired SPBO algorithm combined with the Random Forest (RF) algorithm achieves a comparable accuracy rate of 99.81 %. To ensure better comprehensibility, we employ two widely used Explainable Artificial Intelligence (XAI) techniques: SHapley Additive exPlanations (SHAP) and Local In-terpretable Model-Agnostic Explanations (LIME). These methods provide insightful explanations for our model's decisions, further enhancing its interpretability. © 2023 IEEE.

关键词： Feature Selection

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Informative Sensor Planning for a Single-Axis Gimbaled Camera on a Fixed-Wing UAV

Informative Sensor Planning for a Single-Axis Gimbaled Camer...

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IEEE International Conference on Automation science and Engineering (CASE)

作者： Aditya Parandekar Brady Moon Nayana Suvarna Sebastian Scherer Birla Institute of Technology and Science Sancoale Goa India Robotics Institute School of Computer Science at Carnegie Mellon University Pittsburgh PA USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Uncrewed Aerial Vehicles (UAVs) are a leading choice of platforms for a variety of information-gathering applications. Sensor planning can enhance the efficiency and success of these types of missions when coupled with a higher-level informative path-planning algorithm. This paper aims to address these data acquisition challenges by developing an informative non-myopic sensor planning framework for a single-axis gimbal coupled with an informative path planner to maximize information gain over a prior information map. This is done by finding reduced sensor sweep bounds over a planning horizon such that regions of higher confidence are prioritized. This novel sensor planning framework is evaluated against a predefined sensor sweep and no sensor planning baselines as well as validated in two simulation environments. In our results, we observe an improvement in the performance by 21.88% and 13.34% for the no sensor planning and predefined sensor sweep baselines respectively.

关键词： computer aided software engineering Automation Data acquisition Autonomous aerial vehicles Cameras Planning

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