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Robotic Intra-Operative Ultrasound:Virtual Environments and Parallel systems

IEEE/CAA Journal of Automatica Sinica 2021年第5期8卷 1095-1106页

作者： Shuangyi Wang James Housden Tianxiang Bai Hongbin Liu Junghwan Back Davinder Singh Kawal Rhode Zeng-Guang Hou Fei-Yue Wang the State Key Laboratory of Management and Control of Complex Systems Chinese Academy of SciencesInstitute of AutomationBeijing 100190China the School of Biomedical Engineering and Imaging Sciences King’s College LondonLondon SE17EHUK the Xtronics Ltd.Gravesend DA122ADUK

Robotic intra-operative ultrasound has the potential to improve the conventional practice of diagnosis and procedure guidance that are currently performed *** towards automatic or semi-automatic ultrasound,being able to define ultrasound views and the corresponding probe poses via intelligent approaches become *** on the concept of parallel system which incorporates the ingredients of artificial systems,computational experiments,and parallel execution,this paper utilized a recent developed robotic trans-esophageal ultrasound system as the study object to explore the method for developing the corresponding virtual environments and present the potential applications of such *** proposed virtual system includes the use of 3 D slicer as the main workspace and graphic user interface(GUI),Matlab engine to provide robotic control algorithms and customized functions,and PLUS(Public software Library for Ultra Sound imaging research)toolkit to generate simulated ultrasound *** implementation methods were presented and the proposed features of the system were *** on this virtual system,example uses and case studies were presented to demonstrate its capabilities when used together with the physical TEE *** includes standard view definition and customized view optimization for pre-planning and navigation,as well as robotic control algorithm evaluations to facilitate real-time automatic probe pose *** conclude,the proposed virtual system would be a powerful tool to facilitate the further developments and clinical uses of the robotic intra-operative ultrasound systems.

关键词： Medical robots parallel intelligence parallel systems robotic-assisted surgery robotic ultrasound

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A Novel Sparse Geometric 3-D LiDAR Odometry Approach

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IEEE systems JOURNAL 2021年第1期15卷 1390-1400页

作者： Liang, Shuang Cao, Zhiqiang Guan, Peiyu Wang, Chengpeng Yu, Junzhi Wang, Shuo 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 100049 Peoples R China Peking Univ Beijing Innovat Ctr Engn Sci & Adv Technol Dept Mech & Engn Sci Coll EngnState Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China

Localization is a fundamental prerequisite, no matter whether a single robot or multirobot system, where light detection and ranging (LiDAR) odometry has attracted great interest with accurate depth information and robustness to illumination variations. In this article, a novel 3-D LiDAR odometry approach based on sparse geometric information is proposed. Different from geometric map-based 3-D LiDAR odometry methods with point features, we concern significant line and plane features based on eigenvalues of neighboring points. Furthermore, line-to-line and plane-to-plane associations instead of point-to-line and point-to-plane associations are adopted, and the problem of high computation complexity for scan-to-map matching module caused by point feature is solved. The proposed approach can not only guarantee the accuracy of pose estimation but also reduce computation complexity. Experiments on the public KITTI dataset and an outdoor scenario demonstrate the effectiveness of our approach in terms of accuracy and efficiency.

关键词： Laser radar Feature extraction Simultaneous localization and mapping Three-dimensional displays Computational complexity Distance measurement Lighting Line and plane features line-to-line and plane-to-plane associations sparse geometric map 3-D light detection and ranging (LiDAR) odometry

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Design of a curled hyper-redundant manipulator and the motion control with tip-following algorithm 35

Design of a curled hyper-redundant manipulator and the motio...

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35th Chinese control and Decision Conference (CCDC)

作者： Zhang, Aoshun Li, En Zhang, Feng Guo, Rui Luo, Mingrui Zhang, Yuwei Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing Peoples R China Chinese Acad Sci Engn Lab Intelligent Ind Vis Beijing Peoples R China State Grid Intelligent Technol Co Ltd Shandong Peoples R China State Grid Shandong Elect Power Co Jinan Power Su Jinan Shandong Peoples R China

ISBN: (纸本)9798350334722

In order to meet the operational needs in some narrow environments, the hyper-redundant snake-shaped manipulator has received extensive attention and has been widely studied due to its compact shape and multiple degrees of freedom. At present, the research of hyper-redundant manipulator mainly focuses on the straight push platform. Considering the limitation of working space, this paper designed a hyper-redundant manipulator delivered by a curled turntable. According to the characteristics of turntable delivery, a tip-following algorithm based on curve fitting and a tip-following strategy based on terminal posture record are proposed. Finally, with the algorithm applied to the physical robot we made, we successfully realized the tip-following motion control of the hyper-redundant manipulator.

关键词： Hyper-redundant manipulator Turntable delivery Tip-following algorithm

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Drivable Space of Rehabilitation Robot for Physical Human-Robot Interaction: Definition and an Expanding Method

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IEEE TRANSACTIONS ON ROBOTICS 2023年第1期39卷 343-356页

作者： Wang, Weiqun Liang, Xu Liu, Shengda Lin, Tianyu Zhang, Pu Lv, Zhen Wang, Jiaxing Hou, Zeng-Guang 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 100049 Peoples R China North China Univ Technol Dept Mech & Elect Engn Beijing 100144 Peoples R China Macau Univ Sci & Technol Inst Syst Engn CASIA Must Joint Lab Intelligence Sci & Technol Macau 999078 Peoples R China China Rehabil Res Ctr Beijing 100068 Peoples R China

Physical human-robot interaction performance of present rehabilitation robots are still not satisfactory in the clinical practice. Especially, the work space where the robot can be driven smoothly by users is still very limited, which prevents rehabilitation robots from being applied successfully. In this study, a new concept of drivable space is proposed to evaluate the work spaces of rehabilitation robots, and a method for expanding the drivable space is designed based on the dynamics of the coupled human-robot system and human joint characteristics. First, the definition of drivable space is presented based on comparison of human joint torques, and the minimal torques necessary to drive robot joints, which is mainly determined by the torque estimation errors for general rehabilitation robots driven smoothly by motors. Therefore, a method for improving torque estimation accuracies based on dynamics modeling is then designed. A data-driven error prediction method based on Gaussian process regression is proposed to adaptively compensate the model errors, by which the most accurate dynamic model so far for the coupled system can be obtained, and a method for generation of the training dataset, which is used in error prediction, is designed as well. Moreover, the torque-angle relationship of human joints is modeled and used to optimize the torque error distribution, by which it can be proven that the drivable space can be further expanded. Finally, performance of the proposed methods are demonstrated and validated by experiments carried out on a lower limb rehabilitation robot.

关键词： Assistive robots Torque Robot kinematics Training Exoskeletons Biological system modeling Adaptation models Adaptive learning dynamics modeling physical human-robot interaction (pHRI) rehabilitation robot

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Tracking of Uncertain Robotic Manipulators Using Event-Triggered Model Predictive control With Learning Terminal Cost

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第4期19卷 2801-2815页

作者： Kang, Erlong Qiao, Hong Chen, Ziyu Gao, Jie Chinese Acad Sci State Key Lab Management & Control Complex Syst Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CAS Ctr Excellence Brain Sci & Intelligence Techn Shanghai 200031 Peoples R China

This paper presents an event-triggered model predictive control (MPC) strategy with learning terminal cost for robotic manipulators containing model uncertainty and input constraints. In the proposed MPC structure, an adaptive predictive model for the robotic system is established by radial basis function neural networks (RBFNNs) firstly. Then, a terminal cost adjusted by the global learning mechanism is constructed. Both global steady-state optimization and transient fast convergence are achieved by adding the learning terminal cost to the MPC scheme. After that, a triggering condition of the MPC solving is developed based on the predictive model's weights and the predictive tracking error. Besides, the condition to avoid Zeno behavior is obtained. The recursive feasibility of the proposed MPC strategy is verified, and the ultimately uniformly boundedness (UUB) of all variables is proved according to the Lyapunov theorem. Finally, experiments based on an xMate7 Pro robot are conducted to demonstrate the effectiveness of the presented method.

关键词： Robots Costs Predictive models Optimization Stability analysis Manipulator dynamics Predictive control Model predictive control robotic manipulator leaning terminal cost neural networks event-triggered mechanism unknown dynamics

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Motion Learning and Rapid Generalization for Musculoskeletal systems Based on Recurrent Neural Network Modulated by Initial states

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IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL systems 2022年第4期14卷 1691-1704页

作者： Wang, Xiaona Chen, Jiahao Qiao, Hong Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Automat Beijing Key Lab Res & Applicat Robot Intelligence Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Ctr Excellence Brain Sci & Intelligence Technol Shanghai 200031 Peoples R China

Musculoskeletal robot with high precision and robustness is a promising direction for the next generation of robots. However, motion learning and rapid generalization of complex musculoskeletal systems are still challenging. Therefore, inspired by the movement preparation mechanism of the motor cortex, this article proposes a motion learning framework based on the recurrent neural network (RNN) modulated by initial states. First, two RNNs are introduced as a preparation network and an execution network to generate initial states of the execution network and time-varying motor commands of movement, respectively. The preparation network is trained by a reward-modulated learning rule, and the execution network is fixed. With the modulation of initial states, initial states can be explicitly expressed as knowledge of movements. By dividing the preparation and execution of movements into two RNNs, the motion learning is accelerated to converge under the application of the node-perturbation method. Second, with the utilization of learned initial states, a rapid generalization method for new movement targets is proposed. Initial states of unlearned movements can be computed by searching for low-dimensional ones in latent space constructed by learned initial states and then transforming them into the whole neural space. The proposed framework is verified in simulation with a musculoskeletal model. The results indicate that the proposed motion learning framework can realize goal-oriented movements of the musculoskeletal system with high precision and significantly improve the generalization efficiency for new movements.

关键词： Muscles Recurrent neural networks Mathematical models Bio-inspired control Robot kinematics Tendons Musculoskeletal system Biologically inspired control motor cortex movement preparation musculoskeletal system recurrent neural network (RNN)

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Mining 5.0: Concept and Framework for Intelligent Mining systems in CPSS

IEEE TRANSACTIONS ON INTELLIGENT VEHICLES

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IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 2023年第6期8卷 3533-3536页

作者： Chen, Long Xie, Junkun Zhang, Xiaotong Deng, Jianling Ge, Shirong Wang, Fei-Yue Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou 510275 Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Waytous Ltd Qingdao 26634 Peoples R China China Huaneng Grp Ltd Beijing 100031 Peoples R China China Univ Min & Technol Beijing Sch Mech Elect & Informat Engn Beijing 100083 Peoples R China Minist Emergency Management Key Lab Smart Min & Robot Beijing 100054 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100190 Peoples R China

This letter is part of the Intelligent Mining Development Forum and aims to summarize the discussions of Mining 5.0 from Intelligent Vehicle 5.0 project by IEEE TIV, which represents a shift from Cyber-Physical systems (CPS) to Cyber-Physical-Social systems (CPSS). This letter highlights the basic theories and summarizes the framework of Mining 5.0 with three worlds, three modes, and three technologies that promote values of ESG, ERS, and DEI in physical, mental, and artificial worlds. Mining 5.0 plays a pivotal role in achieving "6S" goals (Safety, Security, Sustainability, Sensitivity, Service, Smartness) for mining industries.

关键词： Mining 5.0 Industry 5.0 Intelligent mining Parallel intelligence

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Parallel control for Optimal Tracking via Adaptive Dynamic Programming

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IEEE/CAA Journal of Automatica Sinica 2020年第6期7卷 1662-1674页

作者： Jingwei Lu Qinglai Wei Fei-Yue Wang School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 The State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China IEEE State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078 Qingdao Academy of Intelligent Industries Qingdao 266109China

This paper studies the problem of optimal parallel tracking control for continuous-time general nonlinear *** existing optimal state feedback control,the control input of the optimal parallel control is introduced into the feedback ***,due to the introduction of control input into the feedback system,the optimal state feedback control methods can not be applied *** address this problem,an augmented system and an augmented performance index function are proposed ***,the general nonlinear system is transformed into an affine nonlinear *** difference between the optimal parallel control and the optimal state feedback control is analyzed *** is proven that the optimal parallel control with the augmented performance index function can be seen as the suboptimal state feedback control with the traditional performance index ***,an adaptive dynamic programming(ADP)technique is utilized to implement the optimal parallel tracking control using a critic neural network(NN)to approximate the value function *** stability analysis of the closed-loop system is performed using the Lyapunov theory,and the tracking error and NN weights errors are uniformly ultimately bounded(UUB).Also,the optimal parallel controller guarantees the continuity of the control input under the circumstance that there are finite jump discontinuities in the reference ***,the effectiveness of the developed optimal parallel control method is verified in two cases.

关键词： Adaptive dynamic programming(ADP) nonlinear optimal control parallel controller parallel control theory parallel system tracking control neural network(NN)

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Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzzy Inference System

Autonomous Mobile Robot Navigation using Adaptive Neuro Fuzz...

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2022 International Conference on Innovations and Development of Information Technologies and Robotics, IDITR 2022

作者： Haider, Muhammad Husnain Ali, Hub Khan, Abdullah Aman Zheng, Hao Bhutta, M. Usman Maqbool Usman, Shaban Zhi, Pengpeng Wang, Zhonglai University of Electronic Science and Technology of China Chengdu China Yangtze Delta Region Institute Huzhou China Casia State Key Laboratory for Management & Control of Complex Systems Beijing China Sichuan Artificial Intelligence Research Institute Yibin China The Chinese University of Hong Kong Robotics Lab Mae Hong Kong

ISBN: (纸本)9781665409322

Navigation of autonomous robots in unknown and cluttered environments lies among the marked trends in robotics. Unlike animals and humans, the collision-free movement of a robot is challenging and requires processing complex information. An autonomous robot needs to cope with a large amount of uncertainty while navigating. The previous methods have limitations, such as lacking obstacle avoidance behaviour, having a large number of governing rules, designing a separate controller for each navigation and obstacle avoidance, not considering the robot's dynamics, computationally expensive training, and poor performance in a cluttered environment. This paper proposes a method that comprises a single adaptive neuro fuzzy inference system (ANFIS) based controller with 16 rules compared to hundred of rules used by previous methods to address such problems. Our method takes heading angle along with distance sensors data as input. AU the inputs are fuzzified into linguistic variables such as near-far and left-right. Additionally, a fuzzy inference system (FIS) is designed and trained using the generated dataset for optimum performance of ANFIS. The proposed method efficiently provides collision-free navigation of the mobile robot in densely cluttered environments. Comprehensive experiments are performed to prove the robustness and potency of the proposed ANFIS controller. Moreover, the performance of the proposed method is compared with various previous methods. The results of these comparisons indicate our proposed method's superiority in finding a near-optimal path. © 2022 IEEE.

关键词： Navigation

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Intraoperative near-infrared fluorescence imaging can identify pelvic nerves in patients with cervical cancer in real time during radical hysterectomy

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EUROPEAN JOURNAL OF NUCLEAR MEDICINE AND MOLECULAR IMAGING 2022年第8期49卷 2929-2937页

作者： He, Kunshan Li, Pengfei Zhang, Zeyu Liu, Jiaqi Liu, Pan Gong, Shipeng Chi, Chongwei Liu, Ping Chen, Chunlin Tian, Jie Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Engn Med Beijing 100191 Peoples R China Beihang Univ Key Lab Big Data Based Precis Med Minist Ind & Informat Technol Beijing 100191 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Inst Software State Key Lab Comp Sci Beijing Peoples R China Chinese Acad Sci Inst Software Beijing Key Lab Human Comp Interact Beijing Peoples R China Southern Med Univ Nanfang Hosp Dept Obstet & Gynecol Guangzhou Peoples R China

Purpose Radical hysterectomy combined with pelvic lymphadenectomy is the standard treatment for early-stage cervical cancer, but unrecognized pelvic nerves are vulnerable to irreversible damage during surgery. This early clinical trial investigated the feasibility and safety of intraoperative near-infrared (NIR) fluorescence imaging (NIR-FI) with indocyanine green (ICG) for identifying pelvic nerves during radical hysterectomy for cervical cancer. Methods Sixty-six adults with cervical cancer were enrolled in this prospective, open-label, single-arm, single-center clinical trial. NIR-FI was performed in vivo to identify genitofemoral (GN), obturator (ON), and hypogastric (HN) nerves intraoperatively. The primary endpoint was the presence of fluorescence in pelvic nerves. Secondary endpoints were the ICG distribution in a nerve specimen and potential underlying causes of fluorescence emission in pelvic nerves. Results In total, 63 patients were analyzed. The ON was visualized bilaterally in 100% (63/63) of patients, with a mean fluorescence signal-to-background ratio (SBR) of 5.3 +/- 2.1. The GN was identified bilaterally in 93.7% (59/63) of patients and unilaterally in the remaining 4 patients, with a mean SBR of 4.1 +/- 1.9. The HN was identified bilaterally in 81.0% (51/63) of patients and unilaterally in 7.9% (5/63) of patients, with a mean SBR of 3.5 +/- 1.3. ICG fluorescence was detected in frozen sections of a nerve specimen, and was mainly distributed in axons. No ICG-related complications were observed. Conclusion This early clinical trial demonstrated the feasibility and safety of NIR-FI to visualize pelvic nerves intraoperatively. Thus, NIR-FI may help surgeons adjust surgical decision-making, avoid nerve damage, and improve surgical outcomes.

关键词： Near-infrared fluorescence imaging Indocyanine green Pelvic nerves Radical hysterectomy Cervical cancer

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