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43rd International Conference on Computer-Aided Design, ICCAD 2024

作者： Zhou, Yifei Kämpfe, Thomas Ni, Kai Amrouch, Hussam Zhuo, Cheng Yin, Xunzhao Zhejiang University Hangzhou China Fraunhofer IPMS Dresden Germany Department of Electrical Engineering University of Notre Dame Notre Dame United States Technical University of Munich TUM School of Computation Information and Technology Munich Institute of Robotics and Machine Intelligence Munich Germany Key Laboratory of Collaborative Sensing and Autonomous Unmanned Systems of Zhejiang Province China

ISBN: (纸本)9798400710773

Compute-in-memory (CiM) emerges as a promising solution to solve hardware challenges in artificial intelligence (AI) and the Internet of Things (IoT), particularly addressing the"memory wall" issue. By utilizing nonvolatile memory (NVM) devices in a crossbar structure, CiM efficiently accelerates multiply-accumulate (MAC) computations, the crucial operations in neural networks and other AI models. Among various NVM devices, Ferroelectric FET (FeFET) is particularly appealing for ultra-low-power CiM arrays due to its CMOS compatibility, voltage-driven write/read mechanisms and high ION/IOFF ratio. Moreover, subthreshold-operated FeFETs, which operate at scaling voltages in the subthreshold region, can further minimize the power consumption of CiM array. However, subthreshold-FeFETs are susceptible to temperature drift, resulting in computation accuracy degradation. Existing solutions exhibit weak temperature resilience at larger array size and only support 1-bit. In this paper, we propose TReCiM, an ultra-low-power temperature-resilient multibit 2FeFET-1T CiM design that reliably performs MAC operations in the subthreshold-FeFET region with temperature ranging from 0°C to 85°C at scale. We benchmark our design using NeuroSim framework in the context of VGG-8 neural network architecture running the CIFAR-10 dataset. Benchmarking results suggest that when considering temperature drift impact, our proposed TReCiM array achieves 91.31% accuracy, with 1.86% accuracy improvement compared to existing 1-bit 2T-1FeFET CiM array. Furthermore, our proposed design achieves 48.03 TOPS/W energy efficiency at system level, comparable to existing designs with smaller technology feature sizes. © 2024 Copyright is held by the owner/author(s).

关键词： MISFET devices

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Variation of sEMG with Muscle Fatigue for Lower Limb Stroke Patient During Walking

Variation of sEMG with Muscle Fatigue for Lower Limb Stroke ...

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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Shang, Wenze Huang, Ruisen Liao, Wei-Hsin Wu, Xinyu Gao, Fei Southern University of Science and Technology Shenzhen518005 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong-Hong Kong-Macao Joint Laboratory of Human-Machine Intelligence-Synergy Systems Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong999077 Hong Kong

ISBN: (纸本)9781665481090

SEMG signal is widely used and explored in control strategies of powered assistive human-robot interaction systems due to its non-invasive nature and ability to estimate motion intention well. However, prolonged use of the sEMG-based systems leads to muscle fatigue, contributing to unstable sEMG signals and regarding the performance of sEMG-based control strategies. In this study, three subjects, including two healthy subjects and one stroke subject, participated in our experiment, and the time domain, frequency domain, and complex features extracted from their sEMG signals were utilized to evaluate muscle fatigue. The results indicate that sEMG from the stroke side of the stroke subject contains more non-linear and chaotic parts, and the statistical differences between different trials are more significant, indicating that stroke has a more significant impact on the stroke side of the stroke subject. This study suggests that RMS in the stroke side of stroke subjects is less than that in others, whereas FuzzyEn in the stroke side of stroke subjects is greater than that in others, indicating that stroke leads to different motion patterns and more susceptibility to muscle fatigue. © 2024 IEEE.

关键词： Human robot interaction

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SLAM-Based Joint Calibration of Differential RSS Sensor Array and Source Localization 49

SLAM-Based Joint Calibration of Differential RSS Sensor Arra...

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

作者： Fu, Linya Qiao, Xu Huang, Shoudong Mao, Guoqiang Lin, Zhiyun Li, Youfu Kong, He Robotics Institute Uni. of Technology Sydney Sydney Australia Xidian University State Key Laboratory of Integrated Services Networks China City University of Hong Kong Department of Mechanical Engineering China Southern Uni. of Sci. and Tech. Shenzhen Key Laboratory of Control Theory and Intelligent Systems Shenzhen China Southern Uni. of Sci. and Tech. Guangdong Prov. Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China

ISBN: (纸本)9798350331820

Sensor arrays generating differential received signal strength (DRSS) measurements have found many applications in robotics. However, accurate calibration of these sensor arrays remains a challenge. Most existing methods are impractical in that they assume to know signal source positions or certain parameters (i.e., path loss exponent), and try to estimate the others. In this paper, we adopt graph simultaneous localization and mapping (SLAM) as a general framework for jointly estimating the source positions and parameters of the DRSS sensor array. Our contributions are twofold. On the one hand, by using a Fisher information matrix approach, we conduct a systematic observability analysis of the corresponding SLAM setup for the calibration problem. On the other hand, we propose an effective procedure to select the initial value which is fed to Levenberg-Marquardt iterations for further improving optimization accuracy and convergence. Extensive simulation and hardware experiments show that the proposed method renders high-quality calibration results. All the codes and data are publicly available at https://***/SUSTech2022/DRSS-sensor-array-calibration. © 2023 IEEE.

关键词： Calibration

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Data engineering and AI-Powered Skin Cancer Identification for Healthcare Applications 28th

Data Engineering and AI-Powered Skin Cancer Identification f...

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28th International Conference on Knowledge Based and Intelligent information and engineering systems, KES 2024

作者： Ahmed, Imran Ahmad, Misbah Chehri, Abdellah Jeon, Gwanggil School of Computing and Information Science Anglia Ruskin University Cambridge United Kingdom Animal and Agriculture Department Hartpury University Gloucester United Kingdom Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Canada Department of Embedded Systems Engineering Incheon National University Incheon Korea Republic of

Skin cancer diagnosis, a critical task in the medical domain, can be revolutionized through the application of advanced deep-learning techniques. This work investigates the efficacy of Convolutional Neural Networks (CNNs) in the automated classification of skin cancer. The process begins with a comprehensive explanation of key CNN layers: Conv2D, MaxPool2D, Dropout, and Dense. The Conv2D layers employ learnable filters that transform localized image segments, while MaxPool2D contributes to downsampling, effectively reducing computational cost and overfitting risk. Integrating these layers enables the network to capture local and global characteristics, which is crucial for accurate classification. Adding Dropout layers enhances generalization and mitigates overfitting by introducing randomness during training. ReLU activation functions infuse non-linearity, and the Flatten layer facilitates the transition to fully connected layers. The proposed CNN architecture is meticulously designed considering filter counts, kernel sizes, and pooling dimensions. The trained model demonstrates promising performance by utilizing the HAM10000 dataset, encompassing diverse skin lesion images across seven classes. The CNN model's parameters and architecture are systematically presented, offering insights into its design rationale. The model undergoes optimization with the Adam optimizer and annealing techniques to facilitate convergence. The model's effectiveness is evaluated on validation and test datasets, demonstrating an accuracy of 78.55% and 76.49%, respectively, for skin cancer classification. Data augmentation strategies are introduced to enhance model generalization further. The results underscore CNN's potential as a robust tool for automating skin cancer diagnosis, aligning with the broader trend of leveraging deep learning for medical image analysis © 2024 The Authors.

关键词： Convolutional neural networks

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Experimental Characterization and Comparison of Three Typical Omnidirectional Mobile Robots

Experimental Characterization and Comparison of Three Typica...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Zhuolun Li Jiajun Long Ximan Zhang Chengyuan Gao Shixing Jiang Zheng Zhu Zhenzhong Jia Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology (SUSTech) Shenzhen China

Omnidirectional vehicles (ODVs) or mobile robots are widely used in various industries due to their ability to move and rotate in narrow spaces without the need for multiple adjustments of moving direction or stopping. There are multiple types of ODVs, including those with standard wheels and those with special wheels such as Swedish wheels. Comprehensive characterization of the ODV's overall performance, which is very important when selecting specific configurations in the robot design process, is still missing in the literature. This paper qualitatively summarizes the comprehensive characteristics of three typical omnidirectional mobile robots in indoor scenarios through theoretical analysis and physical experiments. We compare the robot's performance through six representative evaluation metrics (maneuverability, efficiency, etc.) and express the results in a radar chart. The results of this study can assist designers in designing omnidirectional robots more effectively for specific application scenarios.

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Fast Hip Joint Moment Estimation with A General Moment Feature Generation Method

arXiv

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arXiv 2024年

作者： Zhang, Yuanwen Xiong, Jingfeng Xian, Haolan Chen, Chuheng Chen, Xinxing Fu, Chenglong Leng, Yuquan The Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China The Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China

The hip joint moment during walking is a crucial basis for hip exoskeleton control. Compared to generating assistive torque profiles based on gait estimation, estimating hip joint moment directly using hip joint angles offers advantages such as simplified sensing and adaptability to variable walking speeds. Existing methods that directly estimate moment from hip joint angles are mainly used for offline biomechanical estimation. However, they suffer from long computation time and lack of personalization, rendering them unsuitable for personalized control of hip exoskeletons. To address these challenges, this paper proposes a fast hip joint moment estimation method based on generalized moment features (GMF). The method first employs a GMF generator to learn a feature representation of joint moment, namely the proposed GMF, which is independent of individual differences. Subsequently, a GRU-based neural network with fast computational performance is trained to learn the mapping from the joint kinematics to the GMF. Finally, the predicted GMF is decoded into the joint moment with a GMF decoder. The joint estimation model is trained and tested on a dataset comprising 20 subjects under 28 walking speed conditions. Results show that the proposed method achieves a root mean square error of 0.1180 ± 0.0021 Nm/kg for subjects in test dataset, and the computation time per estimation using the employed GRU-based estimator is 1.3420 ± 0.0031 ms, significantly faster than mainstream neural network architectures, while maintaining comparable network accuracy. These promising results demonstrate that the proposed method enhances the accuracy and computational speed of joint moment estimation neural networks, with potential for guiding exoskeleton control. Copyright © 2024, The Authors. All rights reserved.

关键词： Exoskeleton (robotics)

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Opportunistic Communication in Robot Teams

Opportunistic Communication in Robot Teams

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IEEE International Conference on robotics and Automation (ICRA)

作者： Daniel Mox Kashish Garg Alejandro Ribeiro Vijay Kumar General Robotics Automation Sensing and Perception (GRASP) Laboratory University of Pennsylvania Philadelphia PA USA Electrical and Systems Engineering Department University of Pennsylvania Philadelphia PA USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this paper we present a new approach to Mobile Infrastructure on Demand (MID) where a dedicated team of robots creates and sustains a wireless network that satisfies the communication requirements of a different team of task-oriented robots seeking to coordinate their actions in the absence of existing communication infrastructure. Different from previous works, our approach forgoes heuristics for network performance such as algebraic-connectivity or network flow optimizations and instead positions communication support robots to directly maximize the probability of packet delivery by the underlying opportunistic routing protocol. Our system is task agnostic and practical to implement and operate on robots equipped with off-the-shelf WiFi radios. We demonstrate this through a set of experiments showing our MID system maintaining the delivery of critical mission data in a situational awareness setting and enabling foraging robots to effectively coordinate their actions during multi-robot exploration.

关键词： Robot kinematics Wireless networks Routing protocols Mobile robots Communication networks Task analysis Robots

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D3PRefiner: A Diffusion-based Denoise Method for 3D Human Pose Refinement

arXiv

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arXiv 2024年

作者： Yan, Danqi Gao, Qing Qian, Yuepeng Chen, Xinxing Fu, Chenglong Leng, Yuquan Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities China Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen518055 China School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen518107 China

Three-dimensional (3D) human pose estimation using a monocular camera has gained increasing attention due to its ease of implementation and the abundance of data available from daily life. However, owing to the inherent depth ambiguity in images, the accuracy of existing monocular camera-based 3D pose estimation methods remains unsatisfactory, and the estimated 3D poses usually include much noise. By observing the histogram of this noise, we find each dimension of the noise follows a certain distribution, which indicates the possibility for a neural network to learn the mapping between noisy poses and ground truth poses. In this work, in order to obtain more accurate 3D poses, a Diffusion-based 3D Pose Refiner (D3PRefiner) is proposed to refine the output of any existing 3D pose estimator. We first introduce a conditional multivariate Gaussian distribution to model the distribution of noisy 3D poses, using paired 2D poses and noisy 3D poses as conditions to achieve greater accuracy. Additionally, we leverage the architecture of current diffusion models to convert the distribution of noisy 3D poses into ground truth 3D poses. To evaluate the effectiveness of the proposed method, two state-of-the-art sequence-to-sequence 3D pose estimators are used as basic 3D pose estimation models, and the proposed method is evaluated on different types of 2D poses and different lengths of the input sequence. Experimental results demonstrate the proposed architecture can significantly improve the performance of current sequence-to-sequence 3D pose estimators, with a reduction of at least 10.3% in the mean per joint position error (MPJPE) and at least 11.0% in the Procrustes MPJPE (P-MPJPE). Copyright © 2024, The Authors. All rights reserved.

关键词： Diffusion

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RAVE: A Framework for Radar Ego-Velocity Estimation

RAVE: A Framework for Radar Ego-Velocity Estimation

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International Conference on Multisensor Fusion and Integration for Intelligent systems (MFI)

作者： Vlaho-Josip Štironja Luka Petrović Juraj Peršić Ivan Marković Ivan Petrović Laboratory for Autonomous Systems and Mobile Robotics (LAMOR) University of Zagreb Faculty of Electrical Engineering and Computing Zagreb Croatia Calirad d.o.o. Zagreb Croatia

ISBN: (数字)9798350368031

ISBN: (纸本)9798350368048

State estimation is an essential component of autonomous systems, usually relying on sensor fusion that integrates data from cameras, LiDARs and IMUs. Recently, radars have shown the potential to improve the accuracy and robustness of state estimation and perception, especially in challenging environmental conditions such as adverse weather and low-light scenarios. In this paper, we present a framework for ego-velocity estimation, which we call RAVE, that relies on 3D automotive radar data and encompasses zero velocity detection, outlier rejection, and velocity estimation. In addition, we propose a simple filtering method to discard infeasible egovelocity estimates. We also conduct a systematic analysis of how different existing outlier rejection techniques and optimization loss functions impact estimation accuracy. Our evaluation on three open-source datasets demonstrates the effectiveness of the proposed filter and a significant positive impact of RAVE on the odometry accuracy. Furthermore, we release an opensource implementation of the proposed framework for radar ego-velocity estimation accompanied with a ROS interface.

关键词： Accuracy Three-dimensional displays Systematics Filtering Radar Trajectory Odometry State estimation Optimization Automotive engineering

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A Novel Bio-Inspired Quadruped Crawling Robot with Movable Waist

A Novel Bio-Inspired Quadruped Crawling Robot with Movable W...

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

作者： Ruiqin Wang Hao Wu Jian S. Dai The School of Mechanical Engineering Hebei University of Technology Tianjin China School of Mechanical Engineering Hebei University of Technology Tianjin China The Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems Southern University of Science and Technology Shenzhen China Mechanisms and Robotics Centre for Robotics Research King's College London UK

ISBN: (数字)9798350395969

ISBN: (纸本)9798350395976

Traditional quadruped robots usually have rigid and fixed torso, which limits their mobility and flexibility. Robots with movable waist can improve the flexibility and environmental adaptability of robots. In this paper, a novel bionic crawling robot with movable waist is proposed. The movable waist innovatively adopts an anti-parallelogram mechanism. At the same time, the waist adopts a double-layer structure design to increase the stability and bearing capacity of the robot. The kinematics model of the robot is derived based on the D-H parameter method. Bas ed on the Monte Carlo method, the toe workspace of the leg is obtained in Matlab software. The results show that due to the torsion of the movable waist, the toe workspace is significantly expanded, and the robot can achieve faster and smoother motion. A prototype was made based on 3D printing technology for experimental verification.

关键词： Legged locomotion Monte Carlo methods Robot kinematics Prototypes Three-dimensional printing Stability analysis Software

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