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HIT-Hawk and HIT-Phoenix: Two kinds of flapping-wing flying robotic birds with wingspans beyond 2 meters

Biomimetic Intelligence & robotics 2021年第1期1卷 13-25页

作者： Erzhen Pan Hui Xu Han Yuan Jianqing Peng Wenfu Xu The School of Mechanical Engineering and Automation Harbin Institute of TechnologyShenzhen 518055China The State Key Laboratory of Robotics and System Harbin Institute of TechnologyHarbin 150001China

Inspired by large and medium-sized birds,two kinds of flapping-wing flying robots with wingspans beyond 2 meters were *** have the appearance of a hawk and a phoenix respectively,so they are called HIT-Hawk and *** this paper,the bionic concept,theoretical analysis,design and manufacturing are introduced in ***,the flight principle and characteristics of large and medium-sized birds were ***,the aerodynamics was modeled based on the thin airfoil theory,and the main design basis was ***,the mechanical structures of HIT-Hawk and HIT-Phoenix were designed to ensure the lateral and longitudinal stability and have optimized flight ***,an autonomous flight control method was proposed and realized in highly integrated on-onboard controller;it satisfies the strict restrictions on mass,size,power and ***,the prototypes were fabricated and verified through practical flight *** wingspans of these two flapping wing aircrafts are 2.0 m and 2.3 m respectively,the take-off weights are 1.15 kg and 0.86 kg,and the maximum stable endurance is 65 min(with battery of 3S LiPo,4300 mAh)and 8 min(with battery of 3S LiPo,800 mAh).Their wind resistance can both reach level *** with the small and micro flapping-wing aerial vehicles that mimic insects or small birds,they both have strong load capacity,strong wind resistance and long endurance.

关键词： Bionic robots Flapping-wing flying Unmanned aerial vehicle Robotic bird

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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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A Novel Gait Pattern Recognition Method Based on LSTM-CNN for Lower Limb Exoskeleton

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Journal of Bionic Engineering 2021年第5期18卷 1059-1072页

作者： Chao-feng Chen Zhi-jiang Du Long He Yong-jun Shi Jia-qi Wang Wei Dong State Key Laboratory of Robotics and System Harbin Institute of Technology(HIT)Harbin 150001China China South Industries Group Corporation Weapon Equipment Research InstituteBeijing 102202China

This paper describes a novel gait pattern recognition method based on Long Short-Term Memory(LSTM)and Convolutional Neural Network(CNN)for lower limb *** Inertial Measurement Unit(IMU)installed on the exoskeleton to collect motion information,which is used for LSTM-CNN *** article considers five common gait patterns,including walking,going up stairs,going down stairs,sitting down,and standing *** the LSTM-CNN model,the LSTM layer is used to process temporal sequences and the CNN layer is used to extract *** optimize the deep neural network structure proposed in this paper,some hyperparameter selection experiments were carried *** addition,to verify the superiority of the proposed recognition method,the method is compared with several common methods such as LSTM,CNN and *** results show that the average recognition accuracy can reach 97.78%,which has a good recognition eff ***,according to the experimental results of gait pattern switching,the proposed method can identify the switching gait pattern in time,which shows that it has good real-time performance.

关键词： Lower limb exoskeleton Gait pattern recognition LSTM-CNN Recognition accuracy Real-time performance

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Synthesized colonoscopy dataset from high-fidelity virtual colon with abnormal simulation

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Computers in Biology and Medicine 2025年 186卷 109672-109672页

作者： He, Dongdong Liu, Ziteng Yin, Xunhai 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 Chinese Academy of Sciences Shenyang110016 China Department of Gastroenterology The First Affiliated Hospital of Harbin Medical University Harbin150001 China

With the advent of the deep learning-based colonoscopy system, the need for a vast amount of high-quality colonoscopy image datasets for training is crucial. However, the generalization ability of deep learning models is challenged by the limited availability of colonoscopy images due to regulatory restrictions and privacy concerns. In this paper, we propose a method for rendering high-fidelity 3D colon models and synthesizing diversified colonoscopy images with abnormalities such as polyps, bleeding, and ulcers, which can be used to train deep learning models. The geometric model of the colon is derived from CT images. We employed dedicated surface mesh deformation to mimic the shapes of polyps and ulcers and applied texture mapping techniques to generate realistic, lifelike appearances. The generated polyp models were then attached to the inner surface of the colon model, while the ulcers were created directly on the inner surface of the colon model. To realistically model blood behavior, we developed a simulation of the blood diffusion process on the colon's inner surface and colored vertices in the traversed region to reflect blood flow. Ultimately, we generated a comprehensive dataset comprising high-fidelity rendered colonoscopy images with the abnormalities. To validate the effectiveness of the synthesized colonoscopy dataset, we trained state-of-the-art deep learning models on it and other publicly available datasets and assessed the performance of these models in abnormal classification, detection, and segmentation. Notably, the models trained on the synthesized dataset exhibit an enhanced performance in the aforementioned tasks, as evident from the results. © 2025 Elsevier Ltd

关键词： Digital elevation model

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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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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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Robust Iterative Learning Control of Dual-Driven Crossbeam system 49

Robust Iterative Learning Control of Dual-Driven Crossbeam S...

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

作者： Liu, Yanbin Che, Kai Liu, Jianing Wang, Jue Pan, Huihui Sun, Weichao School of Astronautics Harbin Institute of Technology Harbin China Aeronautics Computing Technique Research Institute Aviation Industry Corporation of China Xi'an China Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin China

ISBN: (纸本)9798350331820

Dual-drive crossbeam system has been an significant part of high-end equipment, whose synchronous accuracy and robustness determine the performance if equipment. Thus, a robust iterative learning control method is proposed, which is based on the cross-coupling model. Robust term is to deal with the non-repetitive disturbance and iterative learning term is to compensate the unknown dynamics by repeating the same task. Finally, different control methods are conducted to verify the effectiveness of the proposed method. © 2023 IEEE.

关键词： Robust control

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A grid-assisted 3D printing method for magnetically driven micro soft robot 9

A grid-assisted 3D printing method for magnetically driven m...

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9th International Conference on Advanced Manufacturing technology and Materials Science, AMTMS 2021

作者： Gao, Jian Gao, Peng Rong, Weibin Li, Dongjie Wang, Lefeng State Key Laboratory of Robotics and System Harbin Institute of Technology Heilongjiang Harbin150001 China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education Harbin University of Science and Technology Heilongjiang Harbin150080 China

The magnetically driven micro soft robot is one of the research hotspots in the field of micro robots. A 3D printing method for manufacturing micro soft robots with the help of auxiliary grids is proposed. The shape flexibility of the micro soft robots manufactured by the micro 3D printing method can be improved, and internal structures composed of different materials inside the micro robots can be printed using this method. The technical details of the grid-assisted 3D printing process are introduced in this article. Micro soft robots with specific patterns were 3D printed, and the deformation and movement capabilities of the robots were verified in magnetically driven motion experiments. The experimental results prove that the grid-assisted 3D printing technology can not only manufacture magnetically driven micro soft robots, but also adjust the internal structure and motion performance of the robots. © 2022 institute of Physics Publishing. All rights reserved.

关键词： 3D printers

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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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A Genderless Docking Mechanism with Passive Locking and High Rotation Misalignment Tolerance for Modular Space Robots

A Genderless Docking Mechanism with Passive Locking and High...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Chen, Jian Wen, Xiaodong Wang, Hao Gong, Wei Deng, Zongquan Yu, Haitao Harbin Institute of Technology State Key Laboratory of Robotics and System Harbin China Tianjin Institute of Aerospace Mechanical and Electrical Equipment Tianjin Key Laboratory of Aerospace Intelligent Equipment Technology Tianjin China

ISBN: (纸本)9781665481090

Modular robots are gaining increasing attention in space services. The docking mechanism plays a critical role in performing self-healing and self-reconfiguration of modular robots. It is beneficial for the docking mechanism to operate with high efficiency, homogeneity and high misalignment tolerances. To meet these requirements, this paper presents a novel genderless docking mechanism. The construction of the docking mechanism is designed with features of passive locking and single-sided electromagnetic unlocking. To demonstrate the advantages of passive locking, the working processes of the axial and circumferential locking mechanisms are described in detail. Based on theoretical calculations and simulations, it is shown that the docking mechanism can provide fast connections or disconnections with a degree of translational and angular misalignment tolerances. This docking mechanism is intended for future applications in modular space robots or unstructured environment exploration robots. © 2022 IEEE.

关键词： Modular robots

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