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Static Force-Based Modeling and Parameter Estimation for a Deformable Link Composed of Passive Spherical Joints With Preload Forces

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IEEE/CAA Journal of Automatica Sinica 2021年第11期8卷 1817-1826页

作者： Gaofeng Li Dezhen Song Lei Sun Shan Xu Hongpeng Wang Jingtai Liu Nankai University Tianjin 300350China Humanoids and Human Centered Mechatronics Research Line Italian Institute of Technology16163 GenovaItaly Department of Computer Science and Engineering Texas A&M UniversityCollege StationTexas 77843 USA Institute of Robotics and Automatic Information System Nankai UniversityTianjin 300350China Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China

To balance the contradiction between higher flexibility and heavier load bearing capacity,we present a novel deformable manipulator which is composed of active rigid joints and deformable *** deformable link is composed of passive spherical joints with preload forces between socket-ball *** estimate the load bearing capacity of a deformable link,we present a static force-based model of spherical joint with preload force and analyze the static force propagation in the deformable *** yields an important result that the load bearing capacity of a spherical joint only depends on its radius,preload force,and static friction *** further develop a parameter estimation method to estimate the product of preload force and static friction *** experimental results validate our model.80.4%of percentage errors on the maximum payload mass prediction are below 15%.

关键词： Deformable link parameters identification spherical joint with preload force static force-based model

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Evolutionary Morphology Towards Overconstrained Locomotion via Large-Scale, Multi-Terrain Deep Reinforcement Learning

Evolutionary Morphology Towards Overconstrained Locomotion v...

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ASME/IFToMM International Conference on Reconfigurable Mechanisms and Robots, ReMAR

作者： Yenan Chen Chuye Zhang Pengxi Gu Jianuo Qiu Jiayi Yin Nuofan Qiu Guojing Huang Bangchao Huang Zishang Zhang Hui Deng Wei Zhang Fang Wan Chaoyang Song Department of Mechanical and Energy Engineering Southern University of Science and Technology (SUSTech) Shenzhen China SUSTech School of Design Department of System Design and Intelligent Manufacturing SUSTech. Shenzhen Key Lab of Intelligent Robotics & Flexible Manufacturing Systems SUSTech. SUSTech. SUSTech Institute of Robotics

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

While the animals' Fin-to-Limb evolution has been well-researched in biology, such morphological transformation remains under-adopted in the modern design of advanced robotic limbs. This paper investigates a novel class of overconstrained locomotion from a design and learning perspective inspired by evolutionary morphology, aiming to integrate the concept of ‘intelligent design under constraints’–hereafter referred to as constraint-driven design intelligence–in developing modern robotic limbs with superior energy efficiency. We propose a 3D-printable design of robotic limbs parametrically reconfigurable as a classical planar 4-bar linkage, an overconstrained Bennett linkage, and a spherical 4- bar linkage. These limbs adopt a co-axial actuation, identical to the modern legged robot platforms, with the added capability of upgrading into a wheel-legged system. Then, we implemented a large-scale, multi-terrain deep reinforcement learning framework to train these reconfigurable limbs for a comparative analysis of overconstrained locomotion in energy efficiency. Results show that the overconstrained limbs exhibit more efficient locomotion than planar limbs during forward and sideways walking over different terrains, including floors, slopes, and stairs, with or without random noises, by saving at least 22% mechanical energy in completing the traverse task, with the spherical limbs being the least efficient. It also achieves the highest average speed of 0.85m/s on flat terrain, which is 20% faster than the planar limbs. This study paves the path for an exciting direction for future research in overconstrained robotics leveraging evolutionary morphology and reconfigurable mechanism intelligence when combined with state-of-the-art methods in deep reinforcement learning.

关键词： Couplings Legged locomotion Technological innovation Education Noise Morphology Stairs

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Single Image De-Raining by Multi Scale Fourier Transform Network

SSRN

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SSRN 2024年

作者： Zheng, Chaobing Yao, Yao Ying, Wenjian Wu, Shiqian Institute of Robotics and Intelligent Systems School of Information Science and Engineering Wuhan University of Science and Technology 947 Heping Avenue Wuhan430081 China Division of Mechanical Manufacturing and Intelligent Transportation Beijing Institute of Metrology Beijing100029 China College of Weapon Naval University of Engineering Wuhan430033 China

It is challenging to remove rain-steaks from a single image because the rain steaks are spatially varying in the rainy image. While data-driven rain removal algorithms have demonstrated promising results, they still face limitations, such as a heavy reliance on data and limited interpretability. This paper introduces a novel approach for single-image de-raining guided by Fourier Transform prior knowledge. Our method leverages inherent frequency domain information to effectively reduce rain streaks and restore image clarity. Initially, using Fourier transform to decompose the rainy image into amplitude and phase components, where raindrops are predominantly present in the amplitude image. Subsequently, data-driven algorithms are applied separately to process the amplitude and phase components. The Fourier inverse transform is then utilised to obtain enhanced features with improved clarity. In the final step, a multi-scale neural network incorporating a different scale attention mechanism is designed to enhance the processed features, thereby improving the algorithm's robustness. Experimental results demonstrate that the proposed algorithm significantly outperforms state-of-the-art methods, both qualitatively and quantitatively. This innovative approach combines the power of the Fourier Transform with data-driven techniques, providing a more interpretable and effective solution for single-image de-raining. © 2024, The Authors. All rights reserved.

关键词： Rain

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An Encoder-Decoder Model Based On Spiking Neural Networks For Address Event Representation Object Recognition

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Du, Sichun Zhu, Haodi Zhang, Yang Hong, Qinghui Hunan University College of Computer Science and Electronic Engineering Changsha418002 China Shenzhen University Computer Vision Institute School of Computer Science and Software Engineering National Engineering Laboratory for Big Data System Computing Technology Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China

Address event representation (AER) object recognition task has attracted extensive attention in neuromorphic vision processing. The spike-based and event-driven computation inherent in the spiking neural network (SNN) provides an energy-saving solution for AER object recognition. However, SNN with spike timing dependent plasticity (STDP) learning rule has not achieved satisfying AER object recognition performance. This work proposes an SNN-based encoder-decoder model to improve the recognition performance of AER objects. An STDP-based locally connected spiking neural network (LC-SNN) is proposed as an encoder to extract rich spatiotemporal features from AER event flows more flexibly. After the encoder extracts and learns primary features, we propose a fully connected spiking neural network (FC-SNN) based on the reward-modulated spike-timing-dependent plasticity (R-STDP) learning rule as a decoder to learn higher-level features for classification. In addition, we improved the winner-take-all (WTA) mechanisms and R-STDP learning rule in the decoder based on the reward and punish decision, enabling the network to perform better. The experiments are performed on the N-MNIST, MNIST-DVS, and the DVS Gesture datasets, improving the accuracy of the best existing plasticity-based SNN by 0.19%, 0.27% and 1.35%, respectively. © 2016 IEEE.

关键词： Decoding

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Saliency-SLAM: saliency prediction for visual SLAM

Saliency-SLAM: saliency prediction for visual SLAM

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Tianjin University-IET Electrical & information engineering Doctoral Forum (TJU IET 2023)

作者： S. Jin Q. Meng Institute of Robotics and Autonomous Systems Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System School of Electrical and Information Engineering Tianjin University Tianjin People's Republic of China

This paper proposes a novel approach for visual simultaneous localization and mapping (vSLAM), called Saliency-SLAM. This method effectively emulates the visual attention mechanism by integrating a saliency prediction model. In contrast to conventional vSLAM methods, which treat all feature points as equally significant, salient feature points are given greater importance in Saliency-SLAM. Therefore, we construct a novel saliency dataset specifically for vSLAM task to train a saliency prediction model by integrating multi-level visual information. Then, we propose to use the salient value of predicted result as the weight of significance for the feature points during the optimization process. Exhaustive experiments have been conducted on the KITTI dataset, demonstrating that our proposed method outperforms existing approaches in terms of localization accuracy.

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A Robust Camera-LiDAR Fusion Framework for 3D Object Detection in High-Dust Environments 22

A Robust Camera-LiDAR Fusion Framework for 3D Object Detecti...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Mingyuan Liu, Wentao Zhou, Bin Wang, Zhangyu Liu, Runsen Wang, Hanyu School of Transportation Science and Engineering Ministry of Industry and Information Technology Beihang University Beijing China Sate Key Lab of Intelligent Transportation System Ministry of Industry and Information Technology Beihang University Beijing China The Key Laboratory of Autonomous Transportation Technology for Special Vehickes Ministry of Industry and Information Technology Beihang University Beijing China Technology and Development Center of Hefei Metro Research Institute Co. Ltd. Hefei Metro Research Institute Co. Ltd Hefei China

ISBN: (纸本)9798331527471

The fusion of camera and LiDAR features in the Bird's Eye View (BEV) perspective has become a prevalent solution for 3D detection in autonomous driving due to its simplicity and efficiency. However, in challenging environments like mining areas with dusty roads, existing BEV-based fusion networks struggle due to dust occlusion and misidentification of dust as obstacles. To address this, we propose a robust fusion framework that integrates fine-grained depth supervision and channel-wise attention mechanisms. Combined with a temporal multi-frame mechanism, our framework effectively mitigates issues caused by dust occlusion and misidentification. We validated our method's detection accuracy on a self-constructed mining road dataset, achieving 89.6% mAP, surpassing BEVFusion's 88.3% mAP. Tests on sensor occlusion and failure further demonstrated its robustness in adverse conditions typical of unstructured road scenarios. © 2024 IEEE.

关键词： Mine dust

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ExpTopo: Explicit Topological Modeling for Bronchus Segmentation 22

ExpTopo: Explicit Topological Modeling for Bronchus Segmenta...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Zhao, Mingyue Qiu, Xiaolan Zhou, S.Kevin School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China (USTC) China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research Ustc China Suzhou Key Laboratory of Microwave Imaging Processing and Application Technology Suzhou Aerospace Information Research Institute China Key Laboratory of Precision and Intelligent Chemistry University of Science and Technology of China (USTC) China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology China

ISBN: (纸本)9798331520526

Airway extraction is paramount in the early diagnosis and treatment of respiratory diseases. As a tree-like structure, both topological-aware learning and voxel-wise classification are equally crucial for the airway. However, existing methods demonstrate insufficient topological learning, emphasizing only the supervision of individual key topological points. Consequently, this paper proposes a Explicit Topological Modeling (ExpTopo) approach to aid airway segmentation. It explicitly introduces topological metric space learning based on semantic segmentation, enhancing the model's structural perception by implementing global skeleton-level sparse topological learning (STL) and local voxel-level dense topological perception (DTP). Extensive experimental results demonstrate that the algorithm achieves competitive performance at both the topological and voxel levels. Code will be available in https://***/MorineZ/ExpTopo. © 2025 IEEE.

关键词： Airway extraction dense topological perception explicit topological modeling sparse topological learning

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Towards combating frequency simplicity-biased learning for domain generalization 24

Towards combating frequency simplicity-biased learning for d...

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Proceedings of the 38th International Conference on Neural information Processing systems

作者： Xilin He Jingyu Hu Qinliang Lin Cheng Luo Weicheng Xie Siyang Song Muhammad Haris Khan Linlin Shen Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University and Shenzhen Institute of Artificial Intelligence and Robotics for Society and Guangdong Provincial Key Laboratory of Intelligent Information Processing University of Exeter Mohamed bin Zayed University of Artificial Intelligence

ISBN: (纸本)9798331314385

Domain generalization methods aim to learn transferable knowledge from source domains that can generalize well to unseen target domains. Recent studies show that neural networks frequently suffer from a simplicity-biased learning behavior which leads to over-reliance on specific frequency sets, namely as frequency shortcuts, instead of semantic information, resulting in poor generalization performance. Despite previous data augmentation techniques successfully enhancing generalization performances, they intend to apply more frequency shortcuts, thereby causing hallucinations of generalization improvement. In this paper, we aim to prevent such learning behavior of applying frequency shortcuts from a data-driven perspective. Given the theoretical justification of models' biased learning behavior on different spatial frequency components, which is based on the dataset frequency properties, we argue that the learning behavior on various frequency components could be manipulated by changing the dataset statistical structure in the Fourier domain. Intuitively, as frequency shortcuts are hidden in the dominant and highly dependent frequencies of dataset structure, dynamically perturbating the over-reliance frequency components could prevent the application of frequency shortcuts. To this end, we propose two effective data augmentation modules designed to collaboratively and adaptively adjust the frequency characteristic of the dataset, aiming to dynamically influence the learning behavior of the model and ultimately serving as a strategy to mitigate shortcut learning. Code is available at https://***/C0notSilly/AdvFrequency.

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Automatic Route Selection of Railway Station Based on Genetic Algorithm 12

Automatic Route Selection of Railway Station Based on Geneti...

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12th International Conference on Transportation and Traffic engineering, ICTTE 2023

作者： Yang, Kun Ma, Liang Guo, Jin Yang, Yang Yang, Ming School of Information Science and Technology Southwest Jiaotong University Chengdu China The Center of National Railway Intelligent Transportation System Engineering and Technology Beijing China Institute of Computing Technologies China Academy of Railway Sciences Corporation Limited Beijing China Institute of Signal & Communication Design CRSC Research & Design Institute Group Co. Ltd Beijing China

ISBN: (纸本)9781839539954

To realize the automatic execution of the train station operation plan and ensure the safety, efficiency and orderliness of the station operations, the key is to realize the automatic selection of the routes required by the station operation plan. The operation routes corresponding to the station operation plans were analysed and the network topology of the station yard through a meta-mode was built taking a certain station as the background. A mathematical model of automatic route selection of station operations, with the objective of minimizing the total delay time of all operations and the total travel time of the routes, considering resource conflicts between operation routes and the constraint conditions of the operation plans. The route selection problem was abstracted as a 0-1 integer model, and a heuristic optimization algorithm suitable for solving the model was proposed based on a genetic algorithm. The results through example analysis show that the algorithm can get the optimal solution in a short time, which meets the real-time requirements of route selection;Different delay factors and travel coefficients will get different optimal route schemes, and the route scheme obtained by focusing on delay factors is better. © The Institution of engineering & Technology 2023.

关键词： Genetic algorithms

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Balance Multi-Head Attention based on Software and Hardware Co-design 9

Balance Multi-Head Attention based on Software and Hardware ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Xu, Dian Hu, Wei Liu, Fang Fan, Zimeng Shi, Qingsong Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science Wuhan China Wuhan University Department of Information Engineering Wuhan Institute of City School of Computer Science Wuhan China Zhejiang University College of Computer Science Hangzhou China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

Recently, the Transformer-based models have achieved leading results in many research areas such as natural language processing and computer vision. However, since Transformer-based models have a huge computational complexities, optimization for Transformer is the focus of current research. the core of Transformer models is the attention module, and in this paper we propose a software and hardware co-designed attention module, which reduces the computational units of the multi-head attention module by balancing the The inference time of the attention module is reduced by balancing the computational units of multiple attention modules. At the same time, we design the corresponding structure on FPGA, which accelerate 14.6x inference time in comparison with the original attention module implementation on GPU. We applied this attention module to multiple visual Transformer models and tested the accuracy difference between the balanced multi-head attention module-based model and the original model on CIFAR-10, and obtained a result of 80.46%, which lower from the original version by 0.18%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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