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作者： Schöne, Sandra Pol, Nirmal Kahrs, Lueder A. Medical Computer Vision and Robotics Lab University of Toronto Toronto Canada University of Stuttgart Stuttgart Germany Medical Computer Vision and Robotics Lab Institute of Biomedical Engineering University of Toronto Toronto Canada Department of Mathematical and Computational Sciences University of Toronto Mississauga Mississauga Canada

To automate surgical (sub-)tasks in robotic surgery, the knowledge of the exact pose of the instrument is mandatory. The application of Optical Coherence Tomography (OCT) to the problem of pose measurement appears promising due to its advantages of 3D imaging and micron-scale resolution. To investigate this, 175 image sequences of the EndoWrist Round Tip Scissor Tool were acquired with an OCT system. The images differ in the opening angles of the scissor blades and the rotation angles of the entire instrument about its central axis. These image sequences were further processed through computer vision methods of the individual images followed by point cloud generation. For pose estimation, an Iterative Closest Point algorithm was implemented to register the acquired point clouds to reference point clouds created from the instrument CAD file. The implemented algorithm was able to determine the opening angle with an overall error of 2 ± 1.3 and the rotation angle with a standard deviation between several runs of 0.6 ±2.8. However, the overall processing time of (39 ± 17)s on a standard PC leaves room for further investigations. © 2024 by Walter de Gruyter Berlin/Boston.

关键词： Optical coherence tomography

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Energy Efficient Hardware Acceleration of Neural Networks with Power-of-Two Quantisation

Energy Efficient Hardware Acceleration of Neural Networks wi...

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International Conference on computer vision and Graphics, ICCVG 2022

作者： Przewlocka-Rus, Dominika Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Deep neural networks virtually dominate the domain of most modern vision systems, providing high performance at a cost of increased computational complexity. Since for those systems it is often required to operate both in real-time and with minimal energy consumption (e.g., for wearable devices or autonomous vehicles, edge Internet of Things (IoT), sensor networks), various network optimisation techniques are used, e.g., quantisation, pruning, or dedicated lightweight architectures. Due to the logarithmic distribution of weights in neural network layers, a method providing high performance with significant reduction in computational precision (for 4-bit weights and less) is the Power-of-Two (PoT) quantisation (and therefore also with a logarithmic distribution). This method introduces additional possibilities of replacing the typical for neural networks Multiply and ACcumulate (MAC—performing, e.g., convolution operations) units, with more energy-efficient Bitshift and ACcumulate (BAC). In this paper, we show that a hardware neural network accelerator with PoT weights implemented on the Zynq UltraScale + MPSoC ZCU104 SoC FPGA can be at least 1.4x more energy efficient than the uniform quantisation version. To further reduce the actual power requirement by omitting part of the computation for zero weights, we also propose a new pruning method adapted to logarithmic quantisation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： System-on-chip

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Traffic Sign Classification Using Deep and Quantum Neural Networks

Traffic Sign Classification Using Deep and Quantum Neural Ne...

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International Conference on computer vision and Graphics, ICCVG 2022

作者： Kuros, Sylwia Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (纸本)9783031220241

Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic signs

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Explicit Model Predictive Control Design based on Constrained Zonotope Propagation 7

Explicit Model Predictive Control Design based on Constraine...

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7th Scientific School: Dynamics of Complex Networks and their Applications, DCNA 2023

作者： Jnadi, Ali Khusainov, Ramil Nedelchev, Simeon Savin, Sergei Innopolis University Institute of Robotics and Computer Vision Innopolis Russia Innopolis University Center for Technologies in Robotics and Mechatronics Components Innopolis Russia

ISBN: (纸本)9798350305746

Zonotopes have become a widely used tool in control design, demonstrating their effectiveness in various applications ranging from state estimation to explicit model predictive control. However, the traditional use of zonotopes is limited by a set certain geometric shapes that restrict the range of state sets that can be considered. This paper proposes a novel approach to control design using constrained zonotopes that can encode arbitrary polytopes. We present a propagation scheme and an affine approximation of the exact control law for this representation, and discuss issues related to center identification for constrained zonotopes. A fast method for detecting set membership is also proposed based on lifting the constrained zonotopes into a higher dimensional space. The proposed method extends the range of shapes that can be considered in control design and offers a promising alternative to the limitations of regular zonotopes. It has the potential for wide-ranging impact in various areas of control, providing a valuable tool for solving complex control problems. © 2023 IEEE.

关键词： Convex optimization

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Reward Planning for Underactuated Robotic Systems: A Study on Pendubot with Parameters Uncertainty 7

Reward Planning for Underactuated Robotic Systems: A Study o...

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7th Scientific School: Dynamics of Complex Networks and their Applications, DCNA 2023

作者： Ibrahim, Sinan Khusainov, Ramil Jnadi, Ali Ahsan Kazmi, S.M. Innopolis University Institute of Robotics and Computer Vision Russia Innopolis University Center for Technologies in Robotics and Mechatronics Components Russia Computer Science and Creative Technologies Bristol United Kingdom

ISBN: (纸本)9798350305746

There is a burgeoning interest in the field of Reinforcement Learning (RL) across various domains, including robotics. Traditional control approaches for robotic systems, such as semi-definite programming (SDP) or mixed integer programming (MIP), typically rely on the assumption of a known and deterministic model of the environment. In contrast, this study aims to introduce a novel RL method for planning the reward function specifically tailored for underactuated robotic systems with parameter uncertainty. We propose a method with a single RL agent to address the challenge of swing up and balance of a Pendubot system with uncertain parameters. The ultimate objective is to enhance the system's ability to adapt and perform reliably in the face of varying uncertainties. © 2023 IEEE.

关键词： Reinforcement learning

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Simulation Study for Robot-based Single Point Incremental Forming

Simulation Study for Robot-based Single Point Incremental Fo...

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2023 International Conference on Industrial Engineering, Applications and Manufacturing, ICIEAM 2023

作者： Shaker, Walid K. Klimchick, Alexandr Innopolis University Institute of Robotics and Computer Vision Innopolis Russia University of Lincoln School of Computer Science Lincoln United Kingdom

ISBN: (纸本)9781665475952

The present study investigates the toolpath definition for the numerical simulation of the Single Point Incremental Forming (SPIF). Since the toolpath definition greatly influences the geometrical accuracy of the formed part, substantial research has been taken to properly generate the toolpath. In this paper, two different toolpath strategies were defined: Z-level, and spiral toolpath. A robot program was created using RoboDK to simulate the SPIF process and to test the generated toolpath over a designed workpiece of a truncated cone. The paper also shows how the SPIF numerical simulation was conducted on Abaqus/CAE software. The results demonstrated that the toolpath was properly designed;however, 0.5% relative error was found between the desired and the simulated cone depth due to the SPIF geometric error. To improve the overall accuracy of the SPIF, a combined stiffness-based and closed loop control algorithm is proposed. © 2023 IEEE.

关键词： Numerical models

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Using deep learning for triple-negative breast cancer classification in DCE-MRI 16

Using deep learning for triple-negative breast cancer classi...

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16th International Workshop on Breast Imaging, IWBI 2022

作者： Vidal, Joel Martí, Robert Computer Vision and Robotics Institute University of Girona Girona Spain

ISBN: (纸本)9781510655843

Triple-negative is one of the most aggressive type of breast cancer for which is also difficult to find an effective treatment. An early diagnosis and a fast and specific treatment are shown to be key aspects for a better prognosis. Current diagnosis of these cases are based on performing a biopsy. This study proposes a non-invasive medical imaging predication method, based on a deep learning architecture, to automatically classify triple-negative tumors in DCE-MRI images. Results are evaluated on an extensive public dataset for different normalizations, data augmentations, learning rates and batch sizes, reaching a state-of-the-art AUC of 0.68. © 2022 SPIE.

关键词： Diseases

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Autonomous Landing of an Unmanned Aerial Vehicle on a Mobile Platform 7

Autonomous Landing of an Unmanned Aerial Vehicle on a Mobile...

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7th International Conference on Automation, Control and Robots, ICACR 2023

作者： Dalang, Felix Sihitshuwam Kulathunga, Geesara Khusainov, Ramil Innopolis University Robotics and Computer Vision Innopolis Russia Innopolis University Center of Autonomous Technologies Innopolis Russia Robotics Innopolis University Innopolis Russia

ISBN: (纸本)9798350302882

Landing an Unmanned Aerial Vehicle (UAV) on a mobile vehicle, has been an active research area due to its much needed applicability in numerous directions such as landing an aerial vehicle on the international space station or a ship at sea. Such research is conducted mainly in two directions: control-based and vision-based. vision-based approaches require sensing from cameras that detect features, such as visual markers, on the mobile vehicle to estimate and track the mobile vehicle pose till the landing pose. The visual markers are usually utilized for feature detection, which most mobile vehicles might not have. This paper proposes a new approach for achieving autonomous landing of a UAV by generating constrained uniform B-spline trajectories assuming known pose of both the UAV and the UGV. Then applying PID controller to regulate velocity set point to land the UAV on the mobile vehicle. The overall effectiveness of the proposed approach is demonstrated using ROS/Gazebo simulations, with a UAV (Px4 enabled IRIS) and a mobile vehicle (Jackal from ClearPath robotics). Landing tasks for static and dynamic cases are carried out and the findings and limitations are stated thereafter. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Quantum Annealing for the Mechanics of Robot Inspectors on Electrical Transmission Lines' Conductors

Quantum Annealing for the Mechanics of Robot Inspectors on E...

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2024 International Conference on Control, robotics and Informatics, ICCRI 2024

作者： Salloum, Hadi Bahrami, Mohammad Reza Matevosian, Artem Gamershmidt, Sofia Jnadi, Ali Mazzara, Manuel Innopolis University Faculty of Computer Science and Engineering Innopolis Russia Innopolis University Cyber-physical systems lab Innopolis Russia Institute of Robotics and Computer Vision Innopolis University Innopolis Russia

ISBN: (纸本)9798350380309

This paper explores the integration of quantum computing, specifically quantum annealing, into robotics for inspecting electrical transmission lines. By using quantum annealing's computational power, we address the dynamics of robot inspectors on transmission line conductors, enhancing the computational efficiency of managing the movements of robot inspectors on transmission line conductors. The use of these robots is crucial for performing efficient and safe inspections. We discuss the mathematical foundations of quantum annealing, transforming differential equations governing robot dynamics into Ising model Hamiltonians suitable for quantum annealing devices. Through experiments using D-Wave's 'Advantage' quantum annealer, we demonstrate the viability of quantum annealing in solving complex robotics problems, paving the way for advanced applications in the field. © 2024 IEEE.

关键词： Ising model

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MRI Breast tissue segmentation using nnU-Net for biomechanical modeling

arXiv

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arXiv 2024年

作者： Pooyan, Melika Awwad, Hadeel García, Eloy Martí, Robert Institute of Computer Vision and Robotics University of Girona Spain

Integrating 2D mammography with 3D magnetic resonance imaging (MRI) is crucial for improving breast cancer diagnosis and treatment planning. However, this integration is challenging due to differences in imaging modalities and the need for precise tissue segmentation and alignment. This paper addresses these challenges by enhancing biomechanical breast models in two main aspects: improving tissue identification using nnU-Net segmentation models and evaluating finite element (FE) biomechanical solvers, specifically comparing NiftySim and FEBio. We performed a detailed six-class segmentation of breast MRI data using the nnU-Net architecture, achieving Dice Coefficients of 0.94 for fat, 0.88 for glandular tissue, and 0.87 for pectoral muscle. The overall foreground segmentation reached a mean Dice Coefficient of 0.83 through an ensemble of 2D and 3D U-Net configurations, providing a solid foundation for 3D reconstruction and biomechanical modeling. The segmented data was then used to generate detailed 3D meshes and develop biomechanical models using NiftySim and FEBio, which simulate breast tissue's physical behaviors under compression. Our results include a comparison between NiftySim and FEBio, providing insights into the accuracy and reliability of these simulations in studying breast tissue responses under compression. The findings of this study have the potential to improve the integration of 2D and 3D imaging modalities, thereby enhancing diagnostic accuracy and treatment planning for breast cancer. © 2024, CC BY-SA.

关键词： Magnetic resonance imaging

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