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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Qi, Yinhe Pan, Meng Jin, Zhi School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Technology Guangzhou510006 China Guangdong Provincial Key Laboratory of Robotics and Digital Intelligent Manufacturing Technology Guangzhou510535 China

ISBN: (纸本)9783030893699

Most existing single image deraining networks are trained in a supervised way, which relies on paired images including one clean image and one rain image. In most cases, the rain images are synthesized from the clean ones manually to obtain sufficient paired images. However, not only huge time costs but expert knowledge are needed to ensure the synthesized images are realistic enough. In addition, the superior performance of these deraining networks trained on manually synthesized rain images is hard to be maintained when testing on real rain images. To address these issues, we propose a scene adaptive asymmetric CycleGAN (SAA-CycleGAN) which transfers clean images to their rainy counterparts automatically so that adequate realistic rain images can be obtained for training deraining networks in a supervised way. Moreover, SAA-CycleGAN can both remove rain from rainy images and synthesize rain on clean images benefiting from the cycle consistency strategy. Since the information is not symmetric during the rain synthesis process and the deraining process, the generators are designed with different architecture accordingly for these two processes. Comprehensive experiments show that the SAA-CycleGAN is able to synthesize more lifelike rain images and achieve similar deraining performance compared with the state-of-the-art deraining methods. © 2021, Springer Nature Switzerland AG.

关键词： Rain

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Lateral Walking Gait Phase Detection with Data Optimization for a Hip Exoskeleton in Resistance Exercise

Lateral Walking Gait Phase Detection with Data Optimization ...

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IEEE Workshop on Advanced robotics and its Social Impacts, ARSO

作者： Wujing Cao Xinbo Zhu Rui Xu Changyu Li Hongliu Yu Mingming Zhang Haoyong Yu Worawarit Kobsiriphat Xinyu Wu Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society and SIAT-CUHK Joint Laboratory of Robotics and Intelligent Systems. College of Automation Engineering Nanjing University of Aeronautics and Astronautics Jining Zhongke Intelligent Technology Co. Ltd. University of Shanghai for Science and Technology Southern University of Science and Technology Department of Biomedical Engineering National University of Singapore. National Metal and Materials Technology Center Thailand

ISBN: (数字)9798350344639

ISBN: (纸本)9798350344646

The accurate detection of lateral walking gait phases is essential for the effective implementation of hip exoskeleton systems in lateral resistance walking exercises. However, limitations in hardware, such as memory and computing power, in the microcontrollers of wearable devices, significantly impact the size and training speed of the lateral walking gait phase detection model, thus affecting the exoskeleton system. This study proposes a data optimization algorithm that utilizes K-means clustering combined with commonly used machine learning algorithms, including Random Forests (RF), Support Vector Machines (SVM), and k-Nearest Neighbors (KNN), to reduce both the training time and size of the model. With the implementation of this algorithm, the training time and model size of RF, SVM, and KNN-based models are reduced by 89.6%, 99.8%, and 97.9%, and 89.6%, 92.7%, and 95.2% respectively. The corresponding gait phase prediction accuracy experiences only a slight decrease of 1.6%, 1.7%, and 2.8% respectively. This method ensures a sufficiently high accuracy in detecting lateral walking gait phases while simultaneously achieving higher efficiency and a smaller model size.

关键词： Legged locomotion Training Support vector machines Solid modeling Machine learning algorithms Accuracy Exoskeletons

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Ultrafast dynamics in perovskite-based optoelectronic devices

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Cell Reports Physical science 2023年第9期4卷

作者： Gong, Shaokuan Huang, Yuling Yu, Xuemeng Hu, Qiushi Liu, Jingjing Meng, Jiazhi Wen, Yifan Chen, Xihan Shenzhen Key Laboratory of Intelligent Robotics and Flexible Manufacturing Systems SUSTech Energy Institute for Carbon Neutrality Department of Mechanical and Energy Engineering Southern University of Science and Technology Guangdong Shenzhen 518055 China

Halide-perovskite-based materials are rising stars for optoelectronic applications. The key for optoelectronic device development is the photogenerated carrier dynamics, which directly describes the motion of photogenerated electrons and holes. A fundamental understanding of the carrier dynamics can help researchers gain deeper insight into the light-matter interaction. The generation and movement of charge carriers can be on the order of femto- and pico-second (ultrafast) timescales, which pose challenges for detection methods. Recent development in transient spectroscopic techniques enable the understanding of charge-carrier dynamics. In this perspective, we introduce several ultrafast spectroscopic methods that investigate total carrier dynamics, hot-carrier cooling dynamics, interfacial carrier transport dynamics, and carrier spin relaxation dynamics in perovskite optoelectronics. Through these investigations, we provide our perspective on the future development of perovskite-based optoelectronics. © 2023 The Author(s)

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Toward AI-Powered Edge Intelligence for Object Detection in Self-Driving Cars: Enhancing IoV Efficiency and Safety

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IEEE Internet of Things Journal 2025年第11期12卷 16990-16997页

作者： Imran Ahmed Misbah Ahmad Muftooh Ur Rehman Siddiqi Abdellah Chehri Gwangill Jeon School of Computing and Information Science Anglia Ruskin University Cambridge U.K. Centre for Machine Vision Bristol Robotics Laboratory University of the West of England Bristol U.K. Department of Animal and Agriculture Hartpury University Gloucester U.K. College of Engineering and Physical Sciences Aston University Birmingham U.K. Department of Mathematics and Computer Science Royal Military College of Canada Kingston ON Canada Department of Embedded Systems Engineering Incheon National University Incheon South Korea

In the rapidly advancing field of intelligent transportation systems, integrating artificial intelligence (AI) with edge computing presents a promising way to enhance the safety and efficiency of the Internet of Vehicles (IoV). This study explores and presents a deep learning-based object detection model within an edge computing framework which aims to facilitate real time object detection in self driving cars. Using an urban traffic scenarios-based dataset, our research shows the ability of the model to accurately detect and classify various objects important for autonomous driving. The YOLOv8 model is used in this work due to its optimal balance between accuracy and computational efficiency. This model has also demonstrated its worth by achieving good performance results, including an average precision of 0.79, a recall of 0.62, and an F1-score of 0.69. The results are demonstrated by a detailed confusion matrix, highlighting the model’s effectiveness in complex driving environments and underscoring its reliability for in-vehicle deployment. By implementing AI directly on edge devices within vehicles, our approach might be helpful in significantly reducing latency, boosting decision-making speed, and enhancing data privacy by minimising dependence on cloud processing. The findings not only support the model’s capabilities but also illustrate the practical benefits of edge intelligence in autonomous vehicles. These benefits, such as faster decision making and improved data privacy, contribute effectively to the IoV infrastructure. This study marks a substantial step toward recognizing the possibility of AI-enhanced edge computing in driving the next generation of autonomous vehicle technology.

关键词： Computational modeling Object detection Edge computing Image edge detection Artificial intelligence Computer architecture Data models Accuracy Servers Safety

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Wheel-Terrain Contact Geometry Estimation using the Aside-Wheel RGB-D Sensor over Soft Terrain

Wheel-Terrain Contact Geometry Estimation using the Aside-Wh...

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robotics, Automation and Artificial Intelligence (RAAI), IEEE International Conference on

作者： Pengxuan Zhana Chen Yao Longteng Hu Feng Xue Peichen Wang Zheng Zhu Zhenzhong Jia Department of Mechanical and Energy Engineering Southern University of Science and Technology Shenzhen China Department of Mechanical and Energy Engineering Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Southern University of Science and Technology (SUSTech) Shenzhen China SUSTech Guang-dong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Shenzhen China Harbin of Institute of Technology (HIT) Harbin China College of Engineering University of Michigan Ann Arbor Michigan USA School of Electronics Harbin Institute of Technology Harbin China

ISBN: (数字)9798350307566

ISBN: (纸本)9798350307573

Wheeled mobile robots (WMRs) have exhibited notable competitiveness in various applications, including geo-graphic and planetary exploration. Nonetheless, when traversing on soft terrains, their wheels might encounter sinkage and slippage, leading to mission failure. Consequently, real-time estimation of contact parameters such as sinkage and slip ratio holds significant importance for rover operations. In this paper, we introduce a novel algorithm that employs only one aside-wheel RGB-D camera for the simultaneous detection of wheel sinkage and slip ratio on deformable terrains in real-time. We estimate sinkage through the depth module by processing point cloud data, and we estimate slip ratio through the color module with a sequence of image processing steps. Utilizing only one single camera reduces errors arising from sensor signal alignment and enhances detection accuracy. With a single-wheel testbed, we simulate wheel motion under different slip ratios and carry out numerous experiments to validate the effectiveness of the proposed algorithm. Our algorithm attains a detection frequency of up to 20Hz and exhibits outstanding precision. The relative sinkage error remains below 4%, while the relative slippage error is confined within 0.8%.

关键词： Accuracy Image color analysis Robot vision systems Wheels Estimation Cameras Real-time systems

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

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

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Haibo Cheng Shichao Li Peng Zeng Valeriy Vyatkin 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 Department of Computer Science Electrical and Space Engineering Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Automation Aalto University Helsinki Finland

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.

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A Piecewise Monotonic Gait Phase Estimation Model for Controlling a Powered Transfemoral Prosthesis in Various Locomotion Modes

arXiv

引用

arXiv 2022年

作者： Chen, Xinxing Chen, Chuheng Wang, Yuxuan Yang, Bowen Ma, Teng Leng, Yuquan Fu, Chenglong Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems Shenzhen518055 China Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities Southern University of Science and Technology Shenzhen518055 China Department of Biomedical Engineering National University of Singapore Singapore

Gait phase-based control is a trending research topic for walking-aid robots, especially robotic lower-limb prostheses. Gait phase estimation is a challenge for gait phase-based control. Previous researches used the integration or the differential of the human's thigh angle to estimate the gait phase, but accumulative measurement errors and noises can affect the estimation results. In this paper, a more robust gait phase estimation method is proposed using a unified form of piecewise monotonic gait phase-thigh angle models for various locomotion modes. The gait phase is estimated from only the thigh angle, which is a stable variable and avoids phase drifting. A Kalman filter-based smoother is designed to further suppress the mutations of the estimated gait phase. Based on the proposed gait phase estimation method, a gait phase-based joint angle tracking controller is designed for a transfemoral prosthesis. The proposed gait estimation method, the gait phase smoother, and the controller are evaluated through offline analysis on walking data in various locomotion modes. And the real-time performance of the gait phase-based controller is validated in an experiment on the transfemoral prosthesis. © 2022, CC BY.

关键词： Controllers

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Learn to Coordinate: a Whole-Body Learning from Demonstration Framework for Differential Drive Mobile Manipulators

Learn to Coordinate: a Whole-Body Learning from Demonstratio...

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IEEE International Conference on systems, Man and Cybernetics

作者： Yuqiang Yang Darong Huang Chen Chen Chao Zeng Yanong He Chenguang Yang Automation Science and Engineering South China University of Technology Guangzhou China Application Innovate Laboratory Huawei Technologies Co. Ltd Shenzhen China Technical Aspects of Multimodal Systems Universitat Hamburg Hamburg Germany Bristol Robotics Laboratory University of the West of England Bristol UK

This paper proposes a whole-body learning from demonstration (LfD) framework that enables differential drive mobile manipulators to learn coordination working and disturbance rejection. First, an efficient kinesthetic teaching method is devised based on the weighted least-norm (WLN) inverse kinematics solution and an admittance controller, which facilitates human users to guide the mobile manipulator to perform tasks. Second, we propose a whole-body LfD framework through Gaussian Process, which endows the mobile manipulator's skill learning process with features of large-scale convergence, coordination working and disturbance rejection, after just a few human demonstrations. The proposed learning framework also allows for human-in-the-loop correction when the whole-body is conducting a task. Finally, the effectiveness of the proposed framework is verified via two simulations and a pick-and-place experiment. Supplementary video for this paper is available in github † † https://***/yuqiang-yang/SMC2023-Video.

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Extraction of Key Foreground Information from Visual Feedback Images for Contact Micromanipulation in Liquid Environment

Extraction of Key Foreground Information from Visual Feedbac...

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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Jiancong Chen Huaping Wang Kailun Bai Kaijun Lin Qing Shi Tao Sun Qiang Huang Toshio Fukuda Intelligent Robotics Institute School of Mechatronical Engineering Beijing Institute of Technology Beijing China Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Ministry of Education Beijing China Science and Technology on Space Physics Laboratory Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China

Contact micromanipulation for cells is an important branch in the field of micromanipulation. Limited by the size of the sensor, it is difficult to integrate the sensor in the macroscopic scene into the micromanipulator. So in most operations, images are often the only reliable source of information. The first prerequisite for realizing automated micromanipulation is to be able to extract key information from images. The occlusion phenomenon will inevitably occur in the contact micromanipulation. Although it is possible to design a specific algorithm to identify the target edge information in the occlusion state according to the characteristics of the operating environment and the end-effector. But there is still no universal image processing method to solve this problem. In this paper, we propose an image processing function based on a composite deep learning network structure to solve this problem. Our algorithm is divided into two steps:In the first step, we input the original image into the target detection network to get the position information of the target and end-effector, and cut the region of interest from the original image according to this information. In the second step, we preprocess these candidate sub-images containing key foreground information, and then input them into the image segmentation network to obtain the contour information of the end-effector and target. We designed a cell aspiration experiment based on the digital holographic microscope imaging system to validate our algorithm. In future work, we will continue to improve the algorithm to have better robustness and generalization.

关键词： Visualization Image segmentation Liquids Micromanipulators Microscopy Object detection End effectors

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CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination

CPnP: Consistent Pose Estimator for Perspective-n-Point Prob...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Guangyang Zeng Shiyu Chen Biqiang Mu Guodong Shi Junfeng Wu School of Data Science Chinese University of Hong Kong Shenzhen P. R. China Key Laboratory of Systems and Control Institute of Systems Science Beijing P. R. China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing P. R. China The Australian Center for Field Robotics School of Aerospace Sydney Mechanical and Mechatronic Engineering The University of Sydney Sydney Shenzhen Institute of Artificial Intelligence and Robotics for Society (AIRS) Shenzhen P. R. China

The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named CPnP, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations—it has $O(n)$ time complexity. Simulations and real dataset tests show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time.

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