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IEEE International Conference on Mechatronics and Automation

作者： Xiulong Cui Jizhuang Fan Zhihui Dong Yue Ou Jie Zhao The State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

In this paper, we made an improved design on the traditional quadruped robot structure and developed a new small wheel-legged mobile robot structure. It has four legged configurations, which can be selected according to different situations, improving the mobility and operability of the robot. In wheeled mode, the robot can adjust its center of gravity according to actual needs. Including the rotational degree of freedom of the wheel, each leg has four degrees of freedom. Each leg adopts a series structure and moves the knee drive motor to the fuselage position using a parallel four-link mechanism and is symmetrically arranged with the hip drive motor. A wheel is installed at the end of the shank, and a DC brushless motor is built inside it, enabling the robot to have the function of wheeled motion. This article introduces the details of the wheel-legged robot, emphasizing the innovative design of the physical structure and the diversity of deformation. The forward kinematics was analyzed. Based on the full-elbow configuration (a type of legged configuration), a cycloidal motion optimization simulation was completed for the single leg structure. Simulation experiment has proven that the optimized robot’s single leg motion is smoother. Finally, we conducted a typical experiment using an embedded controller to control single leg motion. The experimental results verify that the optimized cycloidal motion of the robot meets the desired motion effect.

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A Sequential Design Framework of Robot Leg for High Locomotion Performance*

A Sequential Design Framework of Robot Leg for High Locomoti...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Bin Qi Songyuan Zhang Yili Fu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Leg length, joint maximum torque/speed, motor model and transmission ratio are the most concerning parameters in the leg mechanism design of the legged-robot. The first two parameters are related to the locomotion performance, and the last two determine the actuator’s performance. At the beginning of the design, it is difficult to intuitively grasp the relationship between the leg length, the maximum torque of the joint and the locomotion performance of the legged-robot. When selecting the motor and determining the transmission ratio, these factors impact the control bandwidth and the locomotion performance. Considering the above-mentioned practical problems in the design, this paper proposes a systematic and two-stage sequential leg design framework for the legged-robot, which provides the design guidance in the early stage and the selection of the actuator motor and the determination of the transmission ratio in the later stage. Furthermore, the analytic solution of nonlinear constraint’s gradient was derived. Finally, a case study shows the application of the proposed framework. This work shows that finding an optimal set of design parameters among multiple objectives is not always possible. During design process, it is necessary to weigh the relationship between multiple indexes in many aspects.

关键词： Legged locomotion Actuators Torque systematics Process control Indexes Task analysis

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Aerodynamic Analysis and Deployment Mechanism of A Foldable Mars Quadrotor

Aerodynamic Analysis and Deployment Mechanism of A Foldable ...

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IEEE International Conference on Mechatronics and Automation

作者： Kaijie Zhu Dewei Tang Qiquan Quan Kaiyi Wang Yachao Dong Zongquan Deng State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

The Mars quadrotor is a vertical take-off and landing aerial platform developed to enhance the efficiency of Mars exploration. The four-blade rotor has a high thrust coefficient due to its high solidity, which enables the quadrotor to navigate the Martian atmospheric environment efficiently. This paper proposes a payload-carrying Mars quadrotor featuring four four-bladed rotors that can fold along the rotor arms. The computational fluid dynamics (CFD) method is employed to perform numerical hovering simulations, exploring the propulsion capability and aerodynamic efficiency of rotors with different diameters. The mass distribution and hovering time are estimated when the quadrotor consists of the rotors with the highest figure of merit. The results demonstrate that a larger rotor diameter of the quadrotor yields greater payload capacity and shorter hovering time. During the launch, the quadrotor is folded and constrained within an envelope to protect it from vibrations and shocks. A lifting mechanism with shape-constraint slides is utilized for quadrotor deployment.

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ATTENTION-AWARE LAPAROSCOPIC IMAGE DESMOKING NETWORK WITH LIGHTNESS EMBEDDING AND HYBRID GUIDED EMBEDDING

arXiv

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arXiv 2024年

作者： Liu, Ziteng Zhu, Jiahua Liu, Bainan Liu, Hao Gao, Wenpeng Fu, Yili School of Life Science and Technology Harbin Institute of Technology Harbin150080 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150080 China State Key Laboratory of Robotics Shenyang Institute of Automation Shenyang110016 China

This paper presents a novel method of smoke removal from the laparoscopic images. Due to the heterogeneous nature of surgical smoke, a two-stage network is proposed to estimate the smoke distribution and reconstruct a clear, smoke-free surgical scene. The utilization of the lightness channel plays a pivotal role in providing vital information pertaining to smoke density. The reconstruction of smoke-free image is guided by a hybrid embedding, which combines the estimated smoke mask with the initial image. Experimental results demonstrate that the proposed method boasts a Peak Signal to Noise Ratio that is 2.79% higher than the state-of-the-art methods, while also exhibits a remarkable 38.2% reduction in run-time. Overall, the proposed method offers comparable or even superior performance in terms of both smoke removal quality and computational efficiency when compared to existing state-of-the-art methods. This work will be publicly available on http://***/wpgao. Copyright © 2024, The Authors. All rights reserved.

关键词： Smoke

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A Positioning Method Based on Kalman Filter for FAST Feed Support Cable Inspection Robot

A Positioning Method Based on Kalman Filter for FAST Feed Su...

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Mechatronics and robotics Engineering (ICMRE), International Conference on

作者： Xiangyu Sun Gangfeng Liu Xuehe Zhang Changle Li Jie Zhao State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

In this paper, a positioning system developed for cable inspection robot system which works on feed support cables of Five-hundred-meter Aperture Spherical radio Telescope (FAST) is introduced. The positioning system consists of wheeled odometers and a Real-tme kinematic (RTK) based Global Navigation Satellite system (GNSS). Firstly, the cable with fixed ends and free suspension in the middle is modeled, and the influence of wind on the cable in FAST site is analyzed. Then, the data of the four odometers on the robot are processed, and the odometer information is fused with GNSS information through Kalman filtering. Then the best position estimation of the cable inspection robot is determined. A simulation is finally established to verify the proposed robot positioning method. The result shows that the method can work pretty well under the real working situation.

关键词： Vibrations Global navigation satellite system Analytical models Solid modeling Inspection Mathematical models Feeds

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Data Augmentation for Improving CNNs in Medical Image Classification

Data Augmentation for Improving CNNs in Medical Image Classi...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Yi Ren Zengmin He Yang Deng Bo Huang State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Medical image is essential for physicians to diagnose diseases. And convolutional neural networks (CNNs) have gained momentum for computer-aided diagnosis (CAD) medical image. However, there are still challenges in CNNs related to a lack of data and class-imbalanced datasets. Data augmentation is utilized to address the issues mentioned above. In this study, the Pseudo-color enhancement algorithm (OCEAN and TWILIGHT), common linear transform, CLAHE enhancement, and K-means clustering algorithm were applied to pneumonia X-ray images for data augmentation. Thus, five processed datasets were finally obtained. The results indicated that five data-augmentation methods effectively improved the performance of CNNs for the pneumonia X-ray images binary classification detection, in which the CLAHE enhancement and OCEAN enhancement methods had the accuracy and F1 Score with 97.42%, 97.19%, and 97.42%, 98.36%, respectively. DenseNet121 showed the best classification performance of four CNNs utilized in this study, as evidenced by the area under the receiver operating characteristic (ROC) curves (AUC). To verify the generalizability of the two enhancement methods, CLAHE and OCEAN enhancement methods were applied to the Magnetic Resonance Imaging (MRI) dataset of Alzheimer's disease, which had the accuracy and F1 Score with 98.15% , 98.15%, and 97.22%, 97.68%, respectively. Therefore, these simple and efficient data-augmentation methods effectively improve the performance of CNNs for diagnosing medical image, which can provide theoretical reference for physicians to diagnose the above two diseases, and advance the application of clinical medicine.

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Research and Introduction of the Process Flow of 3D-IC Thermo-Compression Intelligent Collective Bonding Equipment

Research and Introduction of the Process Flow of 3D-IC Therm...

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International Conference on (ICEPT) Electronic Packaging technology

作者： Bin Zhou Jianxin Qiao Longqiu Li Yunkang Su Dapeng Liu Haichuan Shi Wentao Huang State Key Laboratory of Robotics and System Key Laboratory for Microsystems and Microstructure Manufacturing Harbin Institute of Technology Harbin China School of Mechanical and Electrical Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350353808

ISBN: (纸本)9798350353815

Three-dimensional integrated (3D IC) technologies have attracted substantial academic interest due to their enhanced performance and reduced interfacial dimensions. Conventional 3D packaging for chips of four layers must be stacked and bonded four and three times, respectively, bringing about the low reliability of bumps and productivity. Additionally, the decreasing size and spacing of solder bumps present challenges for defect detection. This paper proposes a collective intelligent bonding equipment for flip-chip 3D packaging. The equipment includes a bonding head system, downward vision, chip shift table, upward vision, bonding zone, in-situ monitoring system, and substrate movement platform. The bonding head system, downward vision, chip shift table, upward vision, and bonding zone complete the collective bonding. The defect detection is accomplished by the in-situ monitoring system. The chips need to be stacked four times, and the time of bonding is once. Meanwhile, during the bonding process, the defects can be monitored in real-time by the in situ monitoring system based on active infrared technology. Four layers in chips can be simultaneously bonded, and alignment accuracy is ±15μm@3sigma. The breakage, misalignment, and other defects can be monitored during the bonding progress. The introduction of this equipment will enhance productivity while ensuring bump reliability. Moreover, its successful development holds significant importance for the advancement and manufacturing of semiconductor packaging equipment domestically.

关键词： Productivity Three-dimensional displays Machine vision Packaging Reliability Bonding Soldering

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An Effective Rotor-Shape Optimization Method for IPM Motor with 1/2 Slot per Phase per Pole

An Effective Rotor-Shape Optimization Method for IPM Motor w...

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International Conference on Applied Superconductivity and Electromagnetic Devices, ASEMD

作者： Guodong Yu Shuqi Sun Junlong Li Lijun Xiao Yongxiang Xu Jibin Zou State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Although IPM motor with 1/2 slot per phase per pole has a relatively less rotor loss in comparison with other fractional slot concentrated winding (FSCW), the problems of THD of back-EMF and cogging torque are worse, which limits its application in electric vehicles and other industry fields. This paper proposes an effective rotor-shape optimization method to suppress both THD of back-EMF and cogging torque for improving torque ripple problem. Different from the prevalent rotor optimization methods, the proposed rotor-shape optimization method associates both shape function optimization of rotor surface with PM shaping, which can effectively decrease optimization parameters, and enhance optimization efficiency. To clarify the optimization strategy, the back-EMF and cogging torque for this PM motor with 1/2 slot per phase per pole are analyzed firstly. Then, the proposed optimization method is presented in detail. Finally, the optimized IPM motor is prototyped and its experimental results verifies the effectiveness of the proposed optimization method.

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Optimizing Dataset Configuration for Improving Convolutional Neural Networks Performance in Chest X-ray Image Classification

Optimizing Dataset Configuration for Improving Convolutional...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Zengmin He Yi Ren Honggang Wang State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Chest X-ray (CXR) is essential for physicians to diagnose lung diseases in clinical medicine. With the development of computer and deep learning techniques, creating CXR datasets to train convolutional neural networks (CNNs) has become a popular research topic. However, there are still challenges in creating datasets due to class imbalance and the ratio setting of datasets. This study investigated the impact of class imbalance and the ratio of training, validation, and test sets on CNNs classification performance by optimizing the dataset configuration. This was achieved by directly modifying the ratio, applying oversampling based on the adaptive contrast enhancement (ACE) algorithm, and random undersampling to balance the dataset classes, followed by modifying the ratio again. Therefore, seven datasets were obtained, which were utilized to individually train four CNNs based on transfer learning and fine-tuning techniques. Evaluation metrics based on the confusion matrix were utilized to demonstrate the enhanced classification performance of CNNs. The results indicated that at least 17% of the accuracy of CNNs trained by the dataset with modified ratios was improved as compared with the dataset with an unreasonable initial configuration. Additionally, the overall evaluation metrics of CNNs were further improved by balancing the dataset classes and modifying the ratios. ChexNet demonstrated the best classification performance among the four CNNs, as evidenced by the area under the receiver operating characteristic (ROC) curves (AUC). Furthermore, ChexNet was trained by a class balanced dataset with a ratio of 7:2:1, resulting in the best evaluation metrics, including F1 Score, balanced accuracy, accuracy, recall, specificity, and precision, which were 97.81%, 97.78%, 97.78%, 98.73%, 96.84%, and 96.89%, respectively. Therefore, optimizing dataset configuration can effectively improve the performance of CNNs, providing empirical guidance for researchers in creating dat

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GCA-Net: A Global Context Aggregation Network for Effective Optical Flow

GCA-Net: A Global Context Aggregation Network for Effective ...

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Annual Conference of Industrial Electronics Society

作者： Tao Xie Jinghan Gao Ke Wang Ruifeng Li State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

Optical flow seeks to estimate the per-pixel 2D motion between two frames by identifying corresponding pixels. Current flow estimators typically involve per-pixel feature extraction, multi-scale 4D correlation volume construction, and iterative flow field updates through a Conv-GRU module. However, the locality of convolutional features in these methods renders the calculated correlations vulnerable to different noises. In addition, the Conv-GRU module of these methods is only executed with a convolution layer, which is incapable of exploiting context clues from larger window sizes even inside the image being queried itself, thus raising it more difficult for the model to process images with challenging regions, e.g., textureless areas. To this end, we introduce GCA-Net, a global context aggregation network for credible yet effective optical flow estimation. More specifically, we propose a highly efficient multi-scale transformer (MSFormer) layer which enables the per-pixel feature to aggregate long-range information from other features, hence building more accurate 4D correlation volumes. Besides, we develop an attention-enhanced Conv-GRU block (AttGRU) that can incorporate information alongside a larger context window even within itself empowering our network to estimate optical flow in even the most challenging regions. Experimentally, we demonstrate that GCA-Net outperforms previous state-of-the-art methods by large margins on Sintel (Final) and KITTI 2015 (background and foreground) benchmarks.

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