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Viscoelastic modeling based force control framework for enhancing operation safety of soft tissue for orthopedic surgical robots

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Computers in Biology and Medicine 2024年 183卷 109291-109291页

作者： Xia, Guangming Jiang, Zifeng Yao, Bin Dai, Yu Institute of Robotics and Automatic Information Systems Nankai University Tianjin300350 China Viterbi School of Engineering University of Southern California Los AngelesCA90007 United States

In robot-assisted laminectomy, precise control of touch force on the spinal cord during the dissection of ossified ligaments is critical to prevent dura mater tearing and ensure surgical safety. However, the complex viscoelastic characteristics of tissue at the lesion site pose a significant challenge for accurately applying touch force. This paper proposes a safe touch force servo framework based on tissue model identification and preoperative optimization of the force controller. First, a soft tissue model is established using elastic and viscoelastic elements, and the transfer function from tissue deformation to touch force is derived. Subsequently, the path and parameters of a safe pre-touch are designed, and force information required for tissue model identification is obtained through the pre-touch experiment. The model order and specific parameter values are then identified using a differential evolution algorithm. Next, force control simulation is conducted based on the identified model, and a specifically designed loss function is introduced to achieve preoperative tuning of the force controller. Finally, step force control experiments on sheep spines validate the effectiveness of the proposed method. This paper provides a safe and available touch motion control framework that can be expandable to impose precise force on various vulnerable soft tissues, with the potential for widespread application. © 2024

关键词： Robotic surgery

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Adaptive Geodesic Flow Kernel Transfer for Many-Task Optimization

Adaptive Geodesic Flow Kernel Transfer for Many-Task Optimiz...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Dai, Yang-Tao Liu, Xiao-Fang Zhan, Zhi-Hui Zhong, Jinghui Zhang, Jun Institute of Robotics and Automatic Information Systems College of Artificial Intelligence Nankai University Tianjin China School of Computer Science and Engineering South China University of Technology Guangzhou China Hanyang University Ansan South Korea Nankai University Tianjin China

ISBN: (纸本)9781665430654

Many-task optimization problems (MaTOP) involve more than three tasks, which can be solved simultaneously via knowledge transfer by utilizing complementary information of different tasks. Due to the biases between tasks, relevant tasks are usually selected for knowledge transfer to avoid negative effects. There are two challenging issues, i.e., source task selection and inter-task knowledge transfer. To address these issues, this paper proposes an adaptive geodesic flow kernel transfer method (AGFKTM) for MaTOP. In AGFKTM, multiple source tasks are selected based on both the similarity between tasks and the performance of tasks. In this way, similar and well-performed tasks are selected with a high priority. In addition, an adaptive geodesic flow kernel is constructed to implement knowledge transfer, in which the adopted subspaces along the geodesic flow path are adaptively controlled. Particularly, the transferred solutions are used to generate new ones using mutation operators. Integrating the AGFKTM into differential evolution, a new algorithm named AGFKT-DE is put forward. Experimental results on GECCO20MaTOP benchmark show that the new algorithm outperforms state-of-the-art algorithms. © 2023 IEEE.

关键词： differential evolution evolutionary computation geodesic flow kernel knowledge transfer many-task optimization

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Retinal disorder diagnosis based on hybrid deep learning models

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Multimedia Tools and Applications 2025年 1-25页

作者： Sedik, Ahmed El-Shafai, Walid El-Hag, Noha A. El-Banby, Ghada M. Abd El-Samie, Fathi E. Smart Systems Engineering Laboratory College of Engineering Prince Sultan University Riyadh11586 Saudi Arabia Department of the Robotics and Intelligent Machines Faculty of Artificial Intelligence Kafrelsheikh University Kafr el-Sheikh Egypt Computer Science Department Prince Sultan University Riyadh11586 Saudi Arabia Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt The Higher Institute of Commercial Sciences Algarbia Al Mahalla Al Kubra31951 Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Anomaly detection from medical images is badly needed for automated diagnosis. For example, medical images obtained with several modalities, such as magnetic resonance (MR) and confocal microscopy, need to be classified for anomaly detection. The MR images may contain tumors that need to be detected with anomaly detection techniques. In MR imaging, brain tumors are represented as areas composed of abnormal cells that need to be detected. On the other hand, confocal microscopy can be used for retinal imaging. Hence, there is a need to apply classification techniques for diagnosing diseases of the eye, such as Diabetic Retinopathy (DR), which leads to exudates in different regions of the retina. The detection of exudates is necessary for the diagnosis of DR. In this paper, two models are presented for the classification of MR and retinal images. Much research has been done in this area, but it did not achieve high accuracy of classification in an acceptable time. In addition, the previous models did not consider both types of image modalities considered in this paper. The suggested models are Convolutional Neural Network (CNN) and Convolutional Long Short-Term Memory (Conv-LSTM). They are used to classify normal and abnormal cases of both brain tumor and DR diseases. The CNN model is composed of six convolutional layers and six max-pooling layers. A soft-max activation function takes the classification decision. The proposed Conv-LSTM model is composed of three stages: pre-processing, feature extraction, and classification. The deep learning model performance is better than those of traditional networks. The CNN gives an accuracy of 94.26% for DR classification and 95.8% for brain tumor classification, while the Conv-LSTM gives the maximum accuracy of 96.41% for DR classification and 97.4% for brain tumor classification. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Confocal microscopy

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A Parallel and Asynchronous Blob Shape Descriptor Implemented in CMOS Technology 33

A Parallel and Asynchronous Blob Shape Descriptor Implemente...

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33rd IEEE International Conference on Microelectronics, MIEL 2023

作者： Bogacki, P. Dlugosz, M. Talaska, T. Dlugosz, R. Aptiv Services Poland Kraków30-399 Poland Agh University of Science and Technology Institute of Telecommunications Faculty of Computer Science Electronics and Telecommunications Kraków30-059 Poland Poznan University of Technology Faculty of Control Robotics and Electrical Engineering Institute of Automation and Robotics Division of Signal Processing and Electronic Systems Poznan60-965 Poland Bydgoszcz University of Science and Technology Faculty of Telecommunication Computer Science and Electrical Engineering Bydgoszcz85-796 Poland

ISBN: (纸本)9798350347760

The paper presents a family of novel light blob shape descriptors for use in selected active safety algorithms used in Advanced Driver Assistance systems (ADAS). One of the motivations was to obtain a descriptor that would ensure low computational complexity. This makes it easy to implement both in software and hardware. One assumption is that the location of the center of a given light spot is approximately known. The principle of its operation is then to count white pixels in selected directions, starting from this central point. A key issue here is an efficient way of determining indexes of particular pixels belonging to the image patch, as well as the location of points representing places where the white area turns into black. In the case of a hardware implementation, this can be done using a parallel circuit operating in asynchronous mode, without the need for a control clock. © 2023 IEEE.

关键词： Pixels

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Real-Time Pose Estimation of Rats Based on Stereo Vision Embedded in a Robotic Rat

Real-Time Pose Estimation of Rats Based on Stereo Vision Emb...

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

作者： Xiaowen Guo Guanglu Jia Mohamed Al-Khulaqui Zhe Chen Toshio Fukuda Qing Shi Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Ministry of Education Beijing China Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China

In this paper, we propose a system for real-time rat pose estimation based on stereo vision. The system is dedicated to robot-rat interaction research. First, we design a lightweight, high-resolution network (RRKDNet) for keypoint detection of the rat. The network is trained on a dataset of rat images, which are captured by the robotic rat in first-person view. Second, based on the keypoint detection results, the pose of the rat is obtained by stereo vision model calculation and robot coordinate transformation. At last, we complete a real-time simulation experiment to reproduce the pose of the rat and the robotic rat. The system has been subjected to a series of experiments and the results demonstrate that our network performs better in speed and performance than similar networks. Compared to similar networks, our network has about one-third the number of parameters, while the detection rate increases by 45.25% (the detection rate is 71.57%). The inference speed (34.42 FPS with dual model simultaneous inference) is also faster. The validation error is only 13.85 pixels on the homemade dataset, which is lower than all backbones in Deeplabcut (a toolbox more frequently used for rat keypoint detection). Thus, this work is a significant step in the autonomous intelligent interaction between robots and rats.

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CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face Anti-Spoofing 18

CPL-CLIP: Compound Prompt Learning for Flexible-Modal Face A...

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18th IEEE International Joint Conference on Biometrics, IJCB 2024

作者： Zhang, Ying Zhu, Xiangyu Liu, Ajian Lin, Xun Wan, Jun Zhang, Jing Lei, Zhen Beihang University School of Computer Science and Engineering China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation China University of Chinese Academy of Sciences School of Artificial Intelligence China Macau University of Science and Technology School of Computer Science and Engineering China Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation China

ISBN: (纸本)9798350364132

Face anti-spoofing (FAS) is pivotal in safeguarding the integrity of face recognition systems. Flexible-modal FAS utilizes multi-modal data and trains a unified model adaptable to any single-modal testing scenario. This innovation addresses the shortcomings of conventional multi-modal FAS approaches, which typically demand separate model training and deployment for each modality. However, existing flexible-modal FAS approaches activate specific network branches based on the modality of the tested sample. This not only increases the model's parameters but also necessitates the provision of the image's modality for testing, thereby constraining deployment flexibility. To address the issue, we present Compound Prompt Learning CLIP (CPL-CLIP), a novel method for flexible-modal FAS. This approach capitalizes on a learned textual prompt that is nearly independent of modality, thus bolstering class-based classification across arbitrary modalities. Specifically, our CPL-CLIP introduces a Dual-Branch Prompt (DBP), consisting of class and modal prompts that describe and guide classification, where each prompt is composed of learnable vectors and fixed templates. To further render the class prompt as modality-agnostic as possible, a Cosine Similarity Loss (CSL) is proposed to facilitate the maximal separation of the class prompt from the modality prompt. With only the class prompt utilized during testing, CPL-CLIP enables deployment in diverse modal testing scenarios without the necessity of the test image's modality to be known. Extensive experiments demonstrate CPL-CLIP's superiority over existing methods on several flexible-modal FAS benchmarks. © 2024 IEEE.

关键词： Face recognition

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Application of Deep Learning Method to Estimate Bottomhole Pressure Dynamics of Oil Wells 32

Application of Deep Learning Method to Estimate Bottomhole P...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Cheng, Haibo Li, Shichao Zeng, Peng Vyatkin, Valeriy State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China Luleå University of Technology Electrical and Space Engineering Department of Computer Science Luleå Sweden Aalto University Department of Electrical Engineering and Automation Helsinki Finland

ISBN: (纸本)9798350399714

Surrogate models, which have become an effective and popular method to close loop reservoir management problems, use a data-driven approach to predict dynamic injection and production wells parameters and optimize waterflooding development. In this study, a deep learning-based surrogate model method is proposed to estimate bottomhole pressure (BHP) of production wells in waterflooding reservoirs. Bidirectional long short-term memory (BiLSTM) network, as an efficient deep learning approach, is applied to BHP estimation using fluctuation data. Extended Fourier amplitude sensitivity test (EFAST) method is employed to analyse the influence of different input factors on BHP dynamics, and a reduced dataset is rebuilt to predict BHP parameter based on BiLSTM-EFAST algorithm. The estimation results are tested on a dataset from Volve oilfield in North Sea, and compared with other deep learning methods. The test results indicate that the proposed method can achieve higher prediction accuracy. A reduced dataset-based approach provides a new attempt to reduce model complexity and improve calculation speed for big data-driven surrogate model in oil and gas industry. © 2023 IEEE.

关键词： Long short-term memory

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Design of a High Speed 360-degree Panoramic Video Acquisition System Based on FPGA and USB 3.0

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IEEE Sensors Journal 2023年 1-1页

作者： Lyu, Congyi Peng, Jianqing Zhou, Weiguo Yang, Shanshan Liu, Yunhui Intelligent Robotics Institute Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Mechatronical Engineering Beijing Institute of Technology Beijing China

This paper presents a FPGA-based hardware architecture for capturing real-time panoramic images and the parallel implementation of an image stitching algorithm based on the scale-invariant feature transform (SIFT) features. First, the system transfers the images captured by five CMOS sensors to the designed FPGA board through the FMC adapter plate. Then, the system employs the FPGA-based parallel computing to extract the SIFT features and detect the best matching region and points for image registration. Finally, in order to obtain the panoramic image with smooth changes at the junctions, images of the adjacent images are fused. Using the designed FPGA platform makes it possible to extract the SIFT and stitch the images in real-time, which is not realized in most panoramic imaging systems. The panoramic system, developed using the Xilinx Zynq®-7000 all programmable SOC, can capture video of 5*640*480 images at the speed of 60 fps. To enable high-speed image transfer from the imaging system to the host computer, the USB3.0 interface with the transmission speed of 359MB/s has been developed. The real-time efficiency of the parallel system will be demonstrated by the simulations and experiments. IEEE

关键词： Real time systems

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Implicit Shape Model Trees: Recognition of 3-D Indoor Scenes and Prediction of Object Poses for Mobile Robots

arXiv

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arXiv 2023年

作者： Meißner, Pascal Dillmann, Rüdiger School of Engineering University of Aberdeen United Kingdom Humanoids and Intelligence Systems Lab Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Germany

We present an approach for mobile robots to recognize scenes in object arrangements distributed across cluttered environments. Recognition is enabled by intertwining the robot’s search for objects and the assignment of found objects to scenes. Our scene model called"Implicit Shape Model (ISM) trees" allows these two tasks to be solved jointly. This article presents novel algorithms for ISM trees to recognize scenes and predict poses of searched objects. We define scenes as object sets in which some objects are connected via 3-D spatial relations. In previous work, we recognized scenes with single ISMs. However, single ISMs are prone to false positives. As a remedy, we have developed ISM trees, a hierarchical model consisting of multiple ISMs. This article contributes a recognition algorithm that now enables the use of ISM trees for scene recognition. ISM trees should be ideally generated from human demonstrations of object arrangements. As a suitable algorithm was not available, we introduce such a generation algorithm. In line with the active vision paradigm, we combined scene recognition and object search in previous work. However, an efficient algorithm was lacking to make this combination effective. Physical experiments show that this is now overcome with a new algorithm achieving efficient combination through predicted object poses. © 2023, CC BY.

关键词： Mobile robots

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Target Defense with Multiple Defenders and an Agile Attacker via Residual Policy Learning

arXiv

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arXiv 2025年

作者： Tao, Jiyue Shen, Tongsheng Zhao, Dexin Zhang, Feitian Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering and the State Key Laboratory of Turbulence and Complex Systems Peking University Beijing100871 China National Innovation Institute of Defense Technology Beijing100071 China

The target defense problem involves intercepting an attacker before it reaches a designated target region using one or more defenders. This letter focuses on a particularly challenging scenario in which the attacker is more agile than the defenders, significantly increasing the difficulty of effective interception. To address this challenge, we propose a novel residual policy framework that integrates deep reinforcement learning (DRL) with the force-based Boids model. In this framework, the Boids model serves as a baseline policy, while DRL learns a residual policy to refine and optimize the defenders’ actions. Simulation experiments demonstrate that the proposed method consistently outperforms traditional interception policies, whether learned via vanilla DRL or fine-tuned from force-based methods. Moreover, the learned policy exhibits strong scalability and adaptability, effectively handling scenarios with varying numbers of defenders and attackers with different agility levels. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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