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2021 China Automation Congress, CAC 2021

作者： Huang, Yupei Wu, Zhengxing Du, Sheng The State Key Laboratory of Management and Control for Complex Systems Insititute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665426473

This paper proposes a speed control method for a biomimetic robotic fish based on linear active disturbance rejection control. Inspired by a bluefin tuna in nature, a robotic fish with a two-joint propulsive mechanism and a crescent-shaped caudal fin is first designed. Thereafter, for a steady forward swimming speed, a speed control framework based on the linear form of active disturbance rejection control method is proposed, where the oscillation frequency of the caudal fin is adopted to regulate the swimming speed. Meanwhile, a swimming speed estimation approach based on the oscillation period is provided to effectively reduce the phenomenon of swimming speed fluctuation. Finally, both simulation and experiment results illustrate that the proposed method is effective for the speed control of robotic fish. © 2021 IEEE

关键词： Speed control

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Safe Motion Planning for Autonomous Vehicles by Quantifying Uncertainties of Deep Learning-Enabled Environment Perception

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 2318-2332页

作者： Li, Dachuan Liu, Bowen Huang, Zijian Hao, Qi Zhao, Dezong Tian, Bin Southern Univ Sci & Technol Res Inst Trustworthy Autonomous Syst Shenzhen 518055 Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen 518057 Peoples R China Univ Glasgow James Watt Sch Engn Glasgow G12 8QQ Lanark Scotland Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

Conventional perception-planning pipelines of autonomous vehicles (AV) utilize deep learning (DL) techniques that typically generate deterministic outputs without explicitly evaluating their uncertainties and trustworthiness. Therefore, the downstream decision-making components may generate unsafe outputs leading to system failure or accidents, if the preceding perception component provides highly uncertain information. To mitigate this issue, this article proposes a coherent safe perception-planning framework that quantifies and transfers DL-based perception uncertainties. Following the Bayesian Deep Learning paradigm, we design a probabilistic 3D object detector that extracts objects from LiDAR point clouds while quantifying the corresponding aleatoric and epistemic uncertainty. A chance-constrained motion planner is designed to formulate an explicit link between DL-based perception uncertainties and operation risk and generate safe and risk-bounding trajectories. The proposed framework is validated through various challenging scenarios in the CARLA simulator. Experiment results demonstrate that our framework can effectively capture the uncertainties in DL, and generate trajectories that bound the risk under DL perception uncertainties. It also outperforms counterpart approaches without explicitly evaluating the uncertainties of DL-based perception.

关键词： Uncertainty Planning Three-dimensional displays Decision making Detectors Trajectory Probabilistic logic Autonomous driving motion planning object detection Bayesian deep learning uncertainty quantification

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DRR-Net: A Dense-Connected Residual Recurrent Convolutional Network for Surgical Instrument Segmentation From Endoscopic Images

IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS

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IEEE TRANSACTIONS ON MEDICAL ROBOTICS AND BIONICS 2022年第3期4卷 696-707页

作者： Yang, Lei Gu, Yuge Bian, Guibin Liu, Yanhong Zhengzhou Univ Sch Elect & Informat Engn Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Robot Percept & Control Engn Lab Henan Prov Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Sch Elect Engn Zhengzhou 450001 Henan Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

The precise segmentation of surgical instruments is the key link for the stable and reasonable operation of surgical robots. However, accurate surgical instrument segmentation is still a challenging task due to the complex surgical environment in endoscopic images, low contrast between surgical instruments and tissues, and the diversity of surgical instruments and their morphological variability. In recent years, deep learning has been widely applied into medical image segmentation and achieved a certain achievements, especially U-Net and its variants. However, existing surgical instrument segmentation networks still suffer from some shortcomings, such as insufficient processing of local feature maps, lack of temporal modeling information, etc. To address the above issues, in order to effectively improve the segmentation accuracy of surgical instruments, based on an encoder-decoder network structure, a novel dense residual recurrent convolutional network, called DRR-Net, is proposed in this paper for automatic and accurate surgical instrument segmentation from endoscopic images. Faced with lack of temporal modeling information, inspired by the recurrent neural networks (RNNs), an attention dense-connected recurrent convolutional block (ADRCB) is proposed to optimize the backbone network to obtain temporal information and learn the correspond pixel relationship between frames. To address the insufficient processing issue of local feature maps, to replace the simple skip connections, a residual path is proposed to enhance the context feature representation. Meanwhile, it could also reduce the effect of semantic gap issue. Further, to improve the segmentation accuracy on segmented objects with different sizes, a context fusion block (CFB) is proposed to embed into the bottleneck layer to extract multi-scale attention context features. Multiple public data sets on surgical instrument segmentation are adopted for model evaluation and comparison, including kvasir-instrume

关键词： Surgical instruments deep learning semantic segmentation residual path recurrent neural network

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Activity and Relationship Modeling Driven Weakly Supervised Object Detection 25

Activity and Relationship Modeling Driven Weakly Supervised ...

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25th International Conference on Pattern Recognition (ICPR)

作者： Li, Yinlin Qian, Yang Yang, Xu Zhang, Yuren Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing Peoples R China ByteDance Beijing Peoples R China

ISBN: (纸本)9781728188089

This paper presents a weakly supervised object detection method based on activity label and relationship modeling, which is motivated by the assumption that configuration of human and object are similar in same activity, and joint modeling of human, active object and activity could leverage the recognition of them. Compared to most weakly supervised method taking object as independent instance, firstly, active human and object proposals are learned and filtered based on class activation map of multi-label classification. Secondly, a spatial relationship prior including relative position, scale, overlaps etc are learned dependent on action. Finally, a multistream object detection framework integrating the spatial prior and pairwise ROI pooling are proposed to jointly learn the object and action class. Experiments are conducted on HICODET dataset, and our approach outperforms the state of the art weakly supervised object detection methods.

关键词： Object detection

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PMWorld: A Parallel Testing Platform for Autonomous Driving in Mines

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2024年第1期9卷 1402-1411页

作者： Ai, Yunfeng Liu, Yuhang Gao, Yu Zhao, Chen Cheng, Xiang Han, Jinpeng Tian, Bin Chen, Long Wang, Fei-Yue Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Waytous Inc Qingdao 266109 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Xi An Jiao Tong Univ Sch Software Engn Xian 710049 Peoples R China Chinese Acad Sci Inst Automat Beijing 100190 Peoples R China

Autonomous driving will be widely deployed in the near future which can efficiently reduce the workload on human drivers. Unlike complicated urban driving conditions, fully autonomous driving is expected to be implemented in closed and relatively simple environments, e.g., mines. Autonomous driving in mines holds substantial practical value that can significantly reduce mining costs and enhance operational efficiency. To ensure vehicle safety, unmanned driving systems should be rigorously tested before deployment. With the rapid development of digital twin technology, offline testing in cyberspace has gained increasing significance. While numerous driving simulators have been recently released, none of them can support simulation tests in mines. In addition, due to the ignorance of human factors in the system, the digital twin in CPS (Cyber-Physical systems) is inadequate for developing next-generation intelligent testing systems. To address these issues, we propose PMWorld (Parallel Mines World) based on parallel intelligence in this paper, which is a novel parallel testing platform for autonomous mining vehicles. It not only considers human behaviors during testing but also constitutes a closed loop between physical and virtual systems, allowing for more efficient testing methods. We also introduce the key technical infrastructures for constructing the PMWorld platform and present various testing functionalities supported by PMWorld, including HIL (Hardware-In-the-Loop) test, RIL (Road-In-the-Loop) test, virtual-real interactive test, and integrated test with the scheduling system.

关键词： Testing Autonomous vehicles Processor scheduling Digital twins Computational modeling Games Engines PMWorld (parallel mines world) parallel testing mining autonomous driving

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Phosphopeptides P140 cause oxidative burst responses of pulmonary macrophages in an imiquimod-induced lupus model

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MOLECULAR BIOMEDICINE 2023年第1期4卷 1-12页

作者： Zhong, Jianghong Zheng, Chanyu Chen, Zhongheng Yue, Hangqi Gao, Haiqiang Jiang, Yunfan Hui, Hui Tian, Jie Beihang Univ Beihang Univ Sch Engn Med 37 Xueyuan Rd Beijing 100191 Peoples R China Beihang Univ Key Lab Big Data Based Precis Med Minist Ind & Informat Technol Beijing 100191 Peoples R China Beihang Univ Sch Biol Sci & Med Engn Beijing 100191 Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst CAS Key Lab Mol Imaging Beijing Key Lab Mol ImagingInst Automat Beijing 100190 Peoples R China

Recent studies challenge the dogma that a 21-mer phosphopeptide P140 protects against direct cell damage in the phase-III clinical trial (NCT02504645) for lupus, involving reactive oxygen species (ROS)-dependent release of citrullinated histone H3 (H3cit)-linked neutrophil extracellular traps. An open question is the cellular location of ROS production and H3cit formation in lupus. In this study, we examined the effects of P140 peptides on ROS production and H3cit location in lupus with in vivo and situ fluorescence imaging with subcellular resolution. We developed a mouse model of the B6 strain harbouring a bioluminescent reporter under the control of the Lysozyme M promoter. Based on the imiquimod-induced disease model of B6 mice, we used bioluminescent imaging, flow cytometry analysis, and immunohistology staining to study the effects of P140 peptides in lupus. We found a profound accumulation of CX3CR1-positive macrophages in the lungs of lupus mice after the application of P140, accompanied by lung fibrosis formation. The defined P140-mediated macrophage responses were associated with an increase of H3cit in the cytosol, interleukin-1 receptor type 1 on the extracellular membrane, and intracellular production of ROS. Of interest, the disease of imiquimod-induced lupus was prevented with an antioxidant drug apocynin. This study shows that P140 peptides play a role in aggravated murine lupus in a manner dependent on ROS production and H3cit upregulation through pulmonary macrophages.

关键词： Lupus ROS H3cit Neutrophils Macrophages

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Backstepping-based parallel control for cascaded nonlinear systems 1

Backstepping-based parallel control for cascaded nonlinear s...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Lu, Jingwei Wei, Qinglai Zhou, Tianmin Han, Liyuan Wang, Fei-Yue School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9781665433372

In this study, a novel nonlinear parallel control method is proposed for cascaded nonlinear systems using the backstepping technique. Unlike the existing state feedback control methods, the control input is taken into the feedback system. First, an augmented system is constructed to facilitate the constructing the Lyapunov function. Then, the backstepping technique can be applied to obtain the nonlinear parallel control law, and the stability analysis is shown using the Lyapunov theory. Finally, a simulation is conducted to demonstrate the effectiveness of the proposed parallel control method. © 2021 IEEE.

关键词： Backstepping

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Parallel control-Based Event-Triggered Optimal control for Constrained Discrete-Time Nonlinear systems

Parallel Control-Based Event-Triggered Optimal Control for C...

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2021 China Automation Congress, CAC 2021

作者： Lu, Jingwei Bai, Tianxiang Wei, Qinglai Zhou, Tianmin Wang, Fei-Yue School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Beijing China

ISBN: (纸本)9781665426473

This study proposes a new event-triggered optimal control (ETOC) method for discrete-time (DT) constrained nonlinear systems. First, a new triggering condition is proposed. We show the asymptotic stability of the closed-loop system using the proposed triggering condition and analyze the degeneration degree of the real performance index. Second, to perform the proposed ETOC method effectively, parallel control (PC) combined with adaptive dynamic programming (ADP) is applied. Finally, the validity of the ETOC method is validated by a simulation. © 2021 IEEE

关键词： Asymptotic stability

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Toward Fair and Thrilling Autonomous Racing: Governance Rules and Performance Metrics for the Autonomous One

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第8期8卷 3974-3982页

作者： Li, Bai Fang, Yile Xu, Tian'ao Ma, Siji Wang, Haorui Wang, Yazhou Li, Xinyuan Zhang, Tantan Bian, Xuepeng Wang, Fei-Yue State Key Lab Adv Design & Mfg Vehicle Body Changsha 410082 Peoples R China Hunan Univ Coll Mech & Vehicle Engn Changsha 410082 Peoples R China Swiss Fed Inst Technol Dept Mech & Proc Engn CH-8092 Zurich Switzerland Macau Univ Sci & Technol Fac Innovat Engn Macau 999078 Peoples R China Beijing Inst Fash Technol Coll Fash Accessory Art Engn Coll Beijing 100105 Peoples R China Tencent Com Inc Tencent Automat Drive Lab Beijing 100193 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China

This letter is the second report from a series of IEEE TIV's Decentralized and Hybrid Workshops (DHWs) on Intelligent Vehicles for Education (IV4E). It outlines the prospect of The Autonomous One (TAO), a future autonomous racing competition modeled after Formula One to advance IV4E by pushing the boundaries of artificial intelligence. Existing autonomous races face challenges, including compromised fairness and low participant enthusiasm. These issues limit spectator engagement, thereby reducing the races' educational value. To prevent similar flaws in organizing TAO, our focus lies in setting governance rules from an organizer's perspective. In these DHWs, we analyzed the rules of existing autonomous races and suggested rule-making guidelines for TAO. To improve fairness, we recommend a balanced scoring system with rigorous monitoring of participating teams' performances. To discourage consistent victories by a single team, we suggest that leading teams share their source codes, thereby setting the championship-level performance as the baseline in the next season and accelerating the evolution of all participating teams' abilities. For enhancing suspense and spectator interest, an on-site adaptive rewarding scheme should be deployed to create thrilling turnarounds. Our strategies aim to maintain fairness, increase spectator interest, inspire competitiveness, and ultimately contribute to the advancement of IV4E in organizing TAO.

关键词： Autonomous racing intelligent vehicles for education (IV4E) the autonomous one (TAO)

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A Virtual Ultrasonography Simulator for Skill Training Using Magnetic-Inertial Probe Tracking

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IEEE-ASME TRANSACTIONS ON MECHATRONICS 2024年第1期29卷 445-454页

作者： Wang, Heng Dong, Shuda Yang, Qi Han, Jiancheng He, Ze'an He, Yihua Wang, Shuangyi South China Univ Technol Shien Ming Wu Sch Intelligent Engn Guangzhou 511442 Peoples R China Capital Med Univ Maternal Fetal Med Ctr Fetal Hear Beijing Anzhen Hosp Echocardiog Med Ctr Beijing 100029 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Hong Kong Inst Sci & Innovat Ctr Artificial Intelligence & Robot Hong Kong Peoples R China

Simulation-based ultrasonography training has been proven to be effective for novice practitioners to acquire operation skills. However, existing simulators are cumbersome, expensive, and sometimes fail to provide intuitive and interactive training experience. In this article, a compact, inexpensive, and interactive ultrasonography simulator is developed using magnetic-inertial 6-degree of freedom (DoF) probe pose tracking and virtual ultrasound image generation. Embedded with magnetic and inertial sensors, the sham ultrasound probe is tracked by fusing magnetic measurement of a single electromagnet embedded in the phantom and inertial measurement of the probe. Then the virtual scan plane is determined according to the estimated probe pose to slice the digital anatomical model for virtual image generation, providing visual feedback for skill training. The probe tracking accuracy is experimentally validated to be sufficiently accurate for correct image generation with the RMS position error of 3.67 mm and average orientation error of 0.74 degrees. The interactive performance of the simulator is evaluated by volunteer tests, showing shortened task completion time (40.68 s versus 50.46 s) and slightly improved accuracy of reaching target views compared to the slider control method. It is also shown that an inexperienced operator can learn to successfully acquire standard and diagnostic views of fetal hearts with congenital diseases. Due to intuitive freehand interaction, accurate probe tracking, and real-time visual guidance of ultrasound images and three-dimensional (3-D) virtual scene of the probe and anatomy, the proposed simulator can provide immersive training experience for quick ultrasound skill acquirement.

关键词： Probes Training Magnetic resonance imaging Phantoms Sensors Solid modeling Magnetic sensors

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