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arXiv 2022年

作者： Deng, Huiying Wang, Guangming Feng, Zhiheng Jiang, Chaokang Wu, Xinrui Miao, Yanzi Wang, Hesheng Advanced Robotics Research Center AI Research Institute School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

As one of the important tasks in the field of robotics and machine vision, LiDAR/visual odometry provides tremendous help for various applications such as navigation, location, etc. In the existing methods, LiDAR odometry shows superior performance, but visual odometry is still widely used for its price advantage. Conventionally, the task of visual odometry mainly rely on the input of continuous images. However, it is very complicated for the odometry network to learn the epipolar geometry information provided by the images. In this paper, the concept of pseudo-LiDAR is introduced into the odometry to solve this problem. The pseudo-LiDAR point cloud back-projects the depth map generated by the image into the 3D point cloud, which changes the way of image representation. Compared with the stereo images, the pseudo-LiDAR point cloud generated by the stereo matching network can get the explicit 3D coordinates. Since the 6 Degrees of Freedom (DoF) pose transformation occurs in 3D space, the 3D structure information provided by the pseudo-LiDAR point cloud is more direct than the image. Compared with sparse LiDAR, the pseudo-LiDAR has a denser point cloud. In order to make full use of the rich point cloud information provided by the pseudo-LiDAR, a projection-aware dense odometry pipeline is adopted. Most previous LiDAR-based algorithms sampled 8192 points from the point cloud as input to the odometry network. The projection-aware dense odometry pipeline takes all the pseudo-LiDAR point clouds generated from the images except for the error points as the input to the network. While making full use of the 3D geometric information in the images, the semantic information in the images is also used in the odometry task. The fusion of 2D-3D is achieved in an image-only based odometry. Experiments on the KITTI dataset prove the effectiveness of our method. To the best of our knowledge, this is the first visual odometry method using pseudo-LiDAR. Copyright © 2022, The Authors. A

关键词： Pipelines

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Autonomous Power Supply Development for Hydrometeorological Monitoring Station

Autonomous Power Supply Development for Hydrometeorological ...

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IEEE International Energy Conference and Exhibition (EnergyCon)

作者： Anatoliy Prystupa Volodymyr Kazymyr Anatolijs Zabaš ta Anatoliy Revko Serhii Stepenko Kateryna Novyk Electrical Engineering Information and Measurement Technologies Department Chernihiv Polytechnic National University Chernihiv Ukraine Information and Computer system Department Chernihiv Polytechnic National University Chernihiv Ukraine Institute of Industrial Electronics and Electrical Engineering Riga Technical University Riga Latvia Electronics Automation Robotics and Mechatronics Department Chernihiv Polytechnic National University Chernihiv Ukraine

This paper presents the results of the investigation of the power supply system of the autonomous monitoring system. In particular, calculations of the system’s daily balance of energy consumption were performed. Also, the question of the influence of the angle of inclination of solar converters on energy generation is considered and the necessity of collecting solar radiation during the whole daylight, not focusing on the maximum amount of energy, is substantiated. This research aimed to develop an algorithm for calculating the power supply system for autonomous monitoring systems. The proposed algorithm takes into account the coordination of diagrams of generation and consumption of energy within the autonomous monitoring station and statistical analysis of information from the NASA website. Experiments have shown the effectiveness of this method. It can be used in future design of power supply systems for autonomous monitoring stations.

关键词： Power measurement Power supplies Statistical analysis NASA Energy measurement Reliability theory Battery charge measurement

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Autonomous inverse encoding guides 4D nanoprinting for highly programmable shape morphing

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International Journal of Extreme Manufacturing 2025年第3期 472-488页

作者： Shuaiqi Ren Zhiang Zhang Ruokun He Jiahao Fan Guangming Wang Hesheng Wang Bing Han Yong-Lai Zhang Zhuo-Chen Ma Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Engineering University of Cambridge Institute of Medical Robotics School of Biomedical Engineering Shanghai Jiao Tong University State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin University

Highly programmable shape morphing of 4D-printed micro/nanostructures is urgently desired for applications in robotics and intelligent systems. However, due to the lack of autonomous holistic strategies throughout the target shape input, optimal material distribution generation,and fabrication program output, 4D nanoprinting that permits arbitrary shape morphing remains a challenging task for manual design. In this study, we report an autonomous inverse encoding strategy to decipher the genetic code for material property distributions that can guide the encoded modeling toward arbitrarily pre-programmed 4D shape morphing. By tuning the laser power of each voxel at the nanoscale, the genetic code can be spatially programmed and controllable shape morphing can be realized through the inverse encoding process. Using this strategy, the 4D-printed structures can be designed and accurately shift to the target morphing of arbitrarily hand-drawn lines under stimulation. Furthermore, as a proof-of-concept, a flexible fiber micromanipulator that can approach the target region through pre-programmed shape morphing is autonomously inversely encoded according to the localized spatial *** strategy may contribute to the modeling and arbitrary shape morphing of micro/nanostructures fabricated via 4D nanoprinting, leading to cutting-edge applications in microfluidics, micro-robotics, minimally invasive robotic surgery, and tissue engineering.

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Query Attack by Multi-Identity Surrogates

arXiv

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arXiv 2021年

作者： Chen, Sizhe Huang, Zhehao Tao, Qinghua Huang, Xiaolin Department of Automation The Institute of Medical Robotics Shanghai Jiao Tong University China MOE Key Laboratory of System Control and Information Processing 800 Dongchuan Road Shanghai200240 China ESAT-STADIUS KU Leuven Belgium

Deep Neural Networks (DNNs) are acknowledged as vulnerable to adversarial attacks, while the existing black-box attacks require extensive queries on the victim DNN to achieve high success rates. For query-efficiency, surrogate models of the victim are used to generate transferable Adversarial Examples (AEs) because of their Gradient Similarity (GS), i.e., surrogates’ attack gradients are similar to the victim’s ones. However, it is generally neglected to exploit their similarity on outputs, namely the Prediction Similarity (PS), to filter out inefficient queries by surrogates without querying the victim. To jointly utilize and also optimize surrogates’ GS and PS, we develop QueryNet, a unified attack framework that can significantly reduce queries. QueryNet creatively attacks by multi-identity surrogates, i.e., crafts several AEs for one sample by different surrogates, and also uses surrogates to decide on the most promising AE for the query. After that, the victim’s query feedback is accumulated to optimize not only surrogates’ parameters but also their architectures, enhancing both the GS and the PS. Although QueryNet has no access to pre-trained surrogates’ prior, it reduces queries by averagely about an order of magnitude compared to alternatives within an acceptable time, according to our comprehensive experiments: 11 victims (including two commercial models) on MNIST/CIFAR10/ImageNet, allowing only 8-bit image queries, and no access to the victim’s training data. The code is available at https://***/Sizhe-Chen/QueryNet. Impact Statement—Deep Neural Networks (DNNs) have been validated as vulnerable, while they are much harder to attack in real-world black-box settings. Existing methods require hundreds of queries on the victim DNN to perform an effective attack. By proposing a new perspective that gradient and prediction similarity are fundamentally different, this paper fosters the query-based attackers via stealing the victim model using neural archite

关键词： Deep neural networks

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Microengineered platforms for characterizing the contractile function of in vitro cardiac models

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Microsystems & Nanoengineering 2022年第1期8卷 29-50页

作者： Wenkun Dou Manpreet Malhi Qili Zhao Li Wang Zongjie Huang Junhui Law Na Liu Craig A.Simmons Jason T.Maynes Yu Sun Department of Mechanical and Industrial Engineering University of TorontoTorontoON M5S 3G8Canada Program in Molecular Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Biochemistry University of TorontoTorontoON M5S 1A8Canada Institute of Robotics and Automatic Information System and the Tianjin Key Laboratory of Intelligent Robotics Nankai UniversityTianjin 300350China School of Mechanical&Automotive Engineering Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China School of Mechatronics Engineering and Automation Shanghai UniversityShanghai 200444China Institute of Biomedical Engineering University of TorontoTorontoON M5S 3G9Canada Translational Biology&Engineering Program Ted Rogers Centre for Heart ResearchTorontoON M5G 1M1Canada Department of Anesthesiology and Pain Medicine University of TorontoTorontoON M5S 1A8Canada Department of Anesthesia and Pain Medicine The Hospital for Sick ChildrenTorontoON M5G 1X8Canada Department of Electrical and Computer Engineering University of TorontoTorontoON M5S 3G4Canada Department of Computer Science University of TorontoTorontoON M5T 3A1Canada

Emerging heart-on-a-chip platforms are promising approaches to establish cardiac cell/tissue models in vitro for research on cardiac physiology,disease modeling and drug cardiotoxicity as well as for therapeutic *** still exist in obtaining the complete capability of in situ sensing to fully evaluate the complex functional properties of cardiac cell/tissue *** to contractile strength(contractility)and beating regularity(rhythm)are particularly important to generate accurate,predictive *** new platforms and technologies to assess the contractile functions of in vitro cardiac models is essential to provide information on cell/tissue physiologies,drug-induced inotropic responses,and the mechanisms of cardiac *** this review,we discuss recent advances in biosensing platforms for the measurement of contractile functions of in vitro cardiac models,including single cardiomyocytes,2D monolayers of cardiomyocytes,and 3D cardiac *** characteristics and performance of current platforms are reviewed in terms of sensing principles,measured parameters,performance,cell sources,cell/tissue model configurations,advantages,and *** addition,we highlight applications of these platforms and relevant discoveries in fundamental investigations,drug testing,and disease ***,challenges and future outlooks of heart-on-a-chip platforms for in vitro measurement of cardiac functional properties are discussed.

关键词： cardiac contractile regularity

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NccFlow: Unsupervised learning of optical flow with non-occlusion from geometry

arXiv

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arXiv 2021年

作者： Wang, Guangming Ren, Shuaiqi Wang, Hesheng The Key Laboratory of System Control and Information Processing of Ministry of Education Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of Marine Intelligent Equipment and System of Ministry of Education Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai200240 China

Optical flow estimation is a fundamental problem of computer vision and has many applications in the fields of robot learning and autonomous driving. This paper reveals novel geometric laws of optical flow based on the insight and detailed definition of non-occlusion. Then, two novel loss functions are proposed for the unsupervised learning of optical flow based on the geometric laws of non-occlusion. Specifically, after the occlusion part of the images are masked, the flowing process of pixels is carefully considered and geometric constraints are conducted based on the geometric laws of optical flow. First, neighboring pixels in the first frame will not intersect during the pixel displacement to the second frame. Secondly, when the cluster containing adjacent four pixels in the first frame moves to the second frame, no other pixels will flow into the quadrilateral formed by them. According to the two geometrical constraints, the optical flow non-intersection loss and the optical flow non-blocking loss in the non-occlusion regions are proposed. Two loss functions punish the irregular and inexact optical flows in the non-occlusion regions. The experiments on datasets demonstrated that the proposed unsupervised losses of optical flow based on the geometric laws in non-occlusion regions make the estimated optical flow more refined in detail, and improve the performance of unsupervised learning of optical flow. In addition, the experiments training on synthetic data and evaluating on real data show that the generalization ability of optical flow network is improved by our proposed unsupervised approach. Copyright © 2021, The Authors. All rights reserved.

关键词： Computer vision

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An efficient recharging task planning method for multi-robot autonomous recharging problem

An efficient recharging task planning method for multi-robot...

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Xu, Jingwen Wang, Jingchuan Chen, Weidong Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728163215

As robots are increasingly employed in the warehouse operation, an important problem that needs to be solved is the Autonomous Recharging Problem (ARP);that is deciding when and where to recharge robots. Former research in ARP is disjoint that only focuses on one of above two questions, considering the high computation time. In this paper, we describe an efficient recharging task planning method to solve ARP and it includes two phases: In the first phase, recharging task planning based on task sequence segmentation is proposed for individual robot, in which both when to recharge and where to recharge are considered to maximize operational efficiency. And it also improves the computation efficiency by segmenting task sequence and planning each recharging task recursively. In the second phase, all robots task sequences with planned recharging tasks are centralized and selected recharging stations of some planned recharging tasks are reallocated, using maximum bipartite graph matching to reduce waiting time for other robots recharging and improve overall task execution efficiency. The proposed method is validated by means of simulation in a real warehouse environment. © 2019 IEEE.

关键词： Computational efficiency

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SN-LiDAR: Semantic Neural Fields for Novel Space-time View LiDAR Synthesis

arXiv

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arXiv 2025年

作者： Chen, Yi Deng, Tianchen Zhao, Wentao Wang, Xiaoning Xi, Wenqian Chen, Weidong Wang, Jingchuan Department of Automation Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200030 China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200030 China Ruijin Hospital Shanghai Jiao Tong University School of Medicine China Renji hospital Shanghai Jiao Tong University School of Medicine China

Recent research has begun exploring novel view synthesis (NVS) for LiDAR point clouds, aiming to generate realistic LiDAR scans from unseen viewpoints. However, most existing approaches do not reconstruct semantic labels, which are crucial for many downstream applications such as autonomous driving and robotic perception. Unlike images, which benefit from powerful segmentation models, LiDAR point clouds lack such large-scale pre-trained models, making semantic annotation time-consuming and labor-intensive. To address this challenge, we propose SN-LiDAR, a method that jointly performs accurate semantic segmentation, high-quality geometric reconstruction, and realistic LiDAR synthesis. Specifically, we employ a coarse-to-fine planar-grid feature representation to extract global features from multi-frame point clouds and leverage a CNN-based encoder to extract local semantic features from the current frame point cloud. Extensive experiments on SemanticKITTI and KITTI-360 demonstrate the superiority of SN-LiDAR in both semantic and geometric reconstruction, effectively handling dynamic objects and large-scale scenes. Codes will be available on https://***/dtc111111/SN-Lidar. © 2025, CC BY-NC-SA.

关键词： Semantic Segmentation

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Robotic Electrospinning Actuated by Non-Circular Joint Continuum Manipulator for Endoluminal Therapy

arXiv

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arXiv 2021年

作者： Wu, Zicong Lou, Chuqian Jin, Zhu Huang, Shaoping Liu, Ning Zou, Yun Kovac, Mirko Gao, Anzhu Yang, Guang-Zhong Institute of Medical Robotics Department of Bioengineering Shanghai Jiao Tong University Shanghai200240 China Imperial College London Empa Swiss Federal Laboratories for Materials Science and Technology Limited Hong Kong999077 Hong Kong Aerial Robotics Laboratory Imperial College London LondonSW7 2AZ United Kingdom Institute of Medical Robotics Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Shanghai200240 China

Electrospinning has exhibited excellent benefits to treat the trauma for tissue engineering due to its produced micro/nano fibrous structure. It can effectively adhere to the tissue surface for long-term continuous therapy. This paper develops a robotic electrospinning platform for endoluminal therapy. The platform consists of a continuum manipulator, the electrospinning device, and the actuation unit. The continuum manipulator has two bending sections to facilitate the steering of the tip needle for a controllable spinning direction. Non-circular joint profile is carefully designed to enable a constant length of the centreline of a continuum manipulator for stable fluid transmission inside it. Experiments are performed on a bronchus phantom, and the steering ability and bending limitation in each direction are also investigated. The endoluminal electrospinning is also fulfilled by a trajectory following and points targeting experiments. The effective adhesive area of the produced fibre is also illustrated. The proposed robotic electrospinning shows its feasibility to precisely spread more therapeutic drug to construct fibrous structure for potential endoluminal treatments. © 2021, CC BY-NC-SA.

关键词： Electrospinning

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A map accessibility analysis algorithm for mobile robot navigation in outdoor environment

A map accessibility analysis algorithm for mobile robot navi...

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2019 IEEE International Conference on Real-Time Computing and robotics, RCAR 2019

作者： Yu, Xiangyu Wang, Jingchuan Chen, Weidong Wang, Hesheng Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Medical Robotics Shanghai Jiao Tong University Autonomous Robot Lab Department of Automation Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9781728137261

Autonomous mobile robots have been widely used to perform specific tasks in recent decades. Map accessibility analysis is necessary for those robots to achieve safe and efficient path planning and navigation, especially in outdoor environment where the terrain can be uneven. Researchers have done sufficient works on the influences of different factors. However, integrating these factors is still a great challenge since the information of some constraints are only needed for specific robot tasks. In this paper, we firstly build a Three Dimensional (3D) Occupancy Grid Map (OctoMap) using a 3D Lidar and encoder-based odometer, and then extract different factors from the OctoMap and real-time lidar data. In order to analyze the relationship between map accessibility and variety of constraints, we design an open system and propose a map accessibility analysis algorithm to integrate different factors which have influence on mobile robot navigation. The system and algorithm are demonstrated to be efficient via experiments. © 2018 IEEE

关键词： Motion planning

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