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IEEE Symposium on Intelligent Vehicle

作者： Xiaoyan Gong Yilin Wu Zifan Ye Xiwei Liu State Key Laboratory of Management and Control for Complex Systems Intelligent Education Institute of Qingdao Academy of Intelligent Industries Qingdao China Beijing NO. 13 School Beijing China NO.2 high school of Beijing Normal University Beijing China

ISBN: (纸本)9781538644539

New intelligent era calls for new learners and thus urgently needs a series of artificial intelligence. As a good educational platform for teaching artificial intelligence, smart cars have aroused concern and practices of all parties. However, at present, most courses and training pay more attention to basic knowledge and technology of smart cars, seldom to training based on artificial intelligence curriculum system and comprehensive competency integrating science, technology, art and management. Therefore, based on concept of iSTREAM (intelligence for Science, Technology, robotics, Engineering, Art, and Management) and Raspberry intelligent vehicle teaching platform, this paper introduced a smart car-themed artificial intelligence courses including basic courses, specialized courses, specialized technical courses and elective courses. This course can guide learners to develop smart cars based on visual cognition, in-depth learning, VR and 3D printing integrated artistic creativity. It combines disciplines such as science, technology, art, games and management to upgrade a single knowledge and technology course into a comprehensive competency course that integrates knowledge, skills, emotion and management. Practice in Beijing NO.13 and NO.101 High School shows that this course allows students to experience scientific research process, learn artificial intelligence related knowledge and skills, understand scientific way of thinking and scientific research methods, stimulate learners' responsibility and scientific passion, and cultivate leadership skills through self-learning and partly project management.

关键词： Automobiles Artificial intelligence Art Hardware Software Project management Intelligent vehicles

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Microsphere Superlenses-Based Optical Super-Resolution Imaging of Ovarian Tissue Sections

Microsphere Superlenses-Based Optical Super-Resolution Imagi...

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IEEE International Conference on Cyber Technology in Automation, control, and Intelligent systems

作者： Shuai Yuan Chaodi Jiang Hao Luo Shendi Li Xiaoduo Wang School of Electrical & Control Engineering Shenyang Jianzhu University Shenyang Liaoning Province China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China University of Chinese Academy of Sciences Beijing China School of Shenyang Ligong University Shenyang China Institutes for Robotics and Intelligent Manufacturing Shenyang China

Tissue sections can reveal tumour-specific changes, and a tumour diagnosis can be made by analyzing the arrangement and distribution of cells on the surface. Although histopathological diagnosis based on tissue section observation has been the gold standard for tumour diagnosis, there is still much room for improvement in terms of scale due to the limited spatial resolution of conventional optical microscopy. Microsphere super-resolution microscopy offers a potential solution to these problems. Super-resolution imaging of the surface of tissue sections was achieved with the aid of microspheres fixed to the surface of the tissue section and a removable piezoelectric ceramic displacement stage. A single microsphere of barium titanate, measuring $50\mu\mathrm{m}$ in diameter, is capable of achieving super-resolution and is particularly suitable for observing the surface nanostructure of tissue sections. In our experiment, we successfully combined the microsphere super-resolution microscope with scanning stitching techniques to obtain tissue section structure images with a resolution lower than 200nm. Our research results will help improve the accuracy of tissue section observations and promote advancements in this field.

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A Real-Time Multi-Task Framework for Guidewire Segmentation and Endpoint Localization in Endovascular Interventions

A Real-Time Multi-Task Framework for Guidewire Segmentation ...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Yan-Jie Zhou Shi-Qi Liu Xiao-Liang Xie Xiao-Hu Zhou Guan-An Wang Zeng-Guang Hou Rui-Qi Li Zhen-Liang Ni Chen-Chen Fan State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology China

Real-time guidewire segmentation and endpoint localization play a pivotal role in robot-assisted minimally invasive surgery, which is helpful to reduce radiation dose and procedure time. Nevertheless, the tasks often come with the challenge of limited computational resources. For this purpose, a real-time multi-task framework with two stages is developed. In the first stage, a Fast Attention-fused Network (FAD-Net) is proposed to obtain accurate guidewire segmentation masks. In the second stage, a lightweight localization network and a post-processing algorithm are designed to robustly predict the guidewire endpoint position. Quantitative and qualitative evaluations on intraoperative X-ray sequences from 30 patients demonstrate that the developed framework outperforms the previously-published results for the tasks, achieving state-of-the-art performance. Moreover, the inference rate of the developed framework is approximately 10.6 FPS, which meets the real-time requirement of X-ray fluoroscopy. These results indicate the proposed approach has the potential to be integrated into the robotic navigation framework for endovascular interventions, enabling robotic-assisted minimally invasive surgery.

关键词： Location awareness Minimally invasive surgery Navigation Prediction algorithms Real-time systems Robustness Inference algorithms

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Advancing Embodied Intelligence in Robotic-Assisted Endovascular Procedures: A Systematic Review of AI Solutions

arXiv

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arXiv 2025年

作者： Yao, Tianliang Lu, Bo Kowarschik, Markus Yuan, Yixuan Zhao, Hubin Ourselin, Sebastien Althoefer, Kaspar Ge, Junbo Qi, Peng Department of Control Science and Engineering College of Electronic and Information Engineering Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Department of Electronic Engineering Faculty of Engineering The Chinese University of Hong Kong 999077 Hong Kong Robotics and Microsystems Center School of Mechanical and Electrical Engineering Soochow University Suzhou215131 China Bavaria Forchheim91301 Germany CREATe Division of Surgery & Interventional Science UCL LondonHA7 4LP United Kingdom School of Biomedical Engineering & Imaging Sciences King’s College London LondonSE1 7EH United Kingdom Centre for Advanced Robotics @ Queen Mary School of Engineering and Materials Science Queen Mary University of London LondonE1 4NS United Kingdom Department of Cardiology Zhongshan Hospital Fudan University Shanghai Institute of Cardiovascular Diseases Shanghai200032 China State Key Laboratory of Cardiovascular Diseases Medical Innovation Center Shanghai East Hospital School of Medicine Tongji University Shanghai200092 China

Endovascular procedures have revolutionized the treatment of vascular diseases thanks to minimally invasive solutions that significantly reduce patient recovery time and enhance clinical outcomes. However, the precision and dexterity required during these procedures poses considerable challenges for interventionists. Robotic systems have emerged offering transformative solutions, addressing issues such as operator fatigue, radiation exposure, and the inherent limitations of human precision. The integration of Embodied Intelligence (EI) into these systems signifies a paradigm shift, enabling robots to navigate complex vascular networks and adapt to dynamic physiological conditions. Data-driven approaches, advanced computer vision, medical image analysis, and machine learning techniques, are at the forefront of this evolution. These methods augment procedural intelligence by facilitating real-time vessel segmentation, device tracking, and anatomical landmark detection. Reinforcement learning and imitation learning further refine navigation strategies and replicate experts’ techniques. This review systematically examines the integration of EI principles into robotic technologies, in relation to endovascular procedures. We discuss recent advancements in intelligent perception and data-driven control, and their practical applications in robot-assisted endovascular procedures. By critically evaluating current limitations and emerging opportunities, this review establishes a framework for future developments, emphasizing the potential for greater autonomy and improved clinical outcomes. Emerging trends and specific areas of research, such as federated learning for medical data sharing, explainable AI for clinical decision support, and advanced human-robot collaboration paradigms, are also explored, offering insights into the future direction of this rapidly evolving field. © 2025, CC BY.

关键词： Medical robotics

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Path Planning Based on Segmented Bezier Curves and A* Algorithm for Mobile Robot

Path Planning Based on Segmented Bezier Curves and A* Algori...

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IEEE International Conference on robotics and Biomimetics

作者： Xiangrui Meng Shuang Liang Zhiqiang Cao Zhonghui Li Shuo Wang State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Information Science Academy China Electronics Technology Group Corporation Beijing China Beijing Engo Technology Co. Ltd Beijing China

ISBN: (纸本)9781665405362

Path planning is an important problem for mobile robot in complex environments. In this paper, an optimized path planning method based on segmented Bezier curves and A* algorithm is proposed. The result of A* algorithm is used to guide the direction of segmented Bezier curves, which fit the A* path. The segmented Bezier curves are generated to meet the requirements of fitting index with the consideration of constraints from the environment and robot itself. The set of the optimized Bezier curves for each segmented A* path is connected end to end and regarded as the result of path planning for the mobile robot. The effectiveness of the proposed method is verified through the experiments.

关键词： Smoothing methods Conferences Biomimetics Fitting Path planning Mobile robots Indexes

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Catch Planner: Catching High-Speed Targets in the Flight

arXiv

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arXiv 2023年

作者： Yu, Huan Wang, Pengqin Wang, Jin Ji, Jialin Zheng, Zhi Tu, Jie Lu, Guodong Meng, Jun Zhu, Meixin Shen, Shaojie Gao, Fei The State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China The Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China Robotics Institute of Zhejiang University Hangzhou310027 China Hong Kong The State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou310027 China Huzhou Institute of Zhejiang University Huzhou313000 China School of Electrical Engineering Zhejiang University Hangzhou310027 China The Hong Kong University of Science and Technology Guangzhou China

Catching high-speed targets in the flight is a complex and typical highly dynamic task. However, existing methods require manual setting of catching height or time, resulting in lacks of adaptability and flexibility and cannot deal with multiple targets. To bridge this gap, we propose a planning-with-decision scheme called Catch Planner. For sequential decision making, a lightweight policy search method based on deep reinforcement learning is proposed. It is jointly trained with the motion planning and decoupled from physics to speed up training. For motion planning, we propose a trajectory optimization method that jointly optimizes the highly coupled catching time and terminal state. The core is the flexible-terminal constraint transcription. It converts the three unique constraints of catching into differentiable metrics, including equality constraints for terminal position and time, and inequality constraints that enable reasonable terminal position offset and attitude relaxation. In addition, sparse parameterization based on MINCO class considers both dynamic feasibility and collision avoidance constraints. As a result, a generally constrained quadrotor planning problem is transformed into an unconstrained optimization that can be solved reliably and efficiently. We also propose an online iterative optimization method for predicting differentiable trajectories of targets. Catch Planner provides a new paradigm for the combination of learning and planning, where all algorithms can be run in real time onboard at 100hz. Extensive experiments are carried out in real-world and simulated scenes to verify the robustness and expansibility when facing a variety of high-speed flying targets. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Coordinated Guiding Vector Field Design for Ordering-Flexible Multi-Robot Surface Navigation

arXiv

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arXiv 2024年

作者： Hu, Bin-Bin Zhang, Hai-Tao Yao, Weijia Sun, Zhiyong Cao, Ming The School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control The State Key Lab of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China The School of Robotics Hunan University Hunan410082 China The Institute of Engineering and Technology University of Groningen Groningen9747 AG Netherlands Department of Electrical Engineering Eindhoven University of Technology Netherlands

We design a distributed coordinated guiding vector field (CGVF) for a group of robots to achieve ordering-flexible motion coordination while maneuvering on a desired two-dimensional (2D) surface. The CGVF is characterized by three terms, i.e., a convergence term to drive the robots to converge to the desired surface, a propagation term to provide a traversing direction for maneuvering on the desired surface, and a coordinated term to achieve the surface motion coordination with an arbitrary ordering of the robotic group. By setting the surface parameters as additional virtual coordinates, the proposed approach eliminates potential singularity of the CGVF and enables both the global convergence to the desired surface and the maneuvering on the surface from all possible initial conditions. The ordering-flexible surface motion coordination is realized by each robot to share with its neighbors only two virtual coordinates, i.e. that of a given target and that of its own, which reduces the communication and computation cost in multi-robot surface navigation. Finally, the effectiveness of the CGVF is substantiated by extensive numerical simulations. Copyright © 2024, The Authors. All rights reserved.

关键词： Navigation

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Hierarchical Learning from Demonstrations for Long-Horizon Tasks

Hierarchical Learning from Demonstrations for Long-Horizon T...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Boyao Li Jiayi Li Tao Lu Yinghao Cai Shuo Wang China Academy of Launch Vehicle Technology Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

Although reinforcement learning (RL) has achieved great success in robotic manipulation skills learning, it is still challenging for long-horizon tasks. Combining RL with demonstrations is an effective solution. In this paper, we propose a novel hierarchical learning from demonstrations method for long-horizon tasks, which leverages (i) object-centered segmentation of demonstrations to automatically segment the teaching trajectories into episodes. (ii) a bi-level hierarchical imitation learning method with a parallel training mechanism to train the two-level policies simultaneously. Experimental results on three challenging long-horizon tasks with sparse rewards show that our proposed method significantly outperforms state-of-art approaches in terms of both sample-efficiency and success rate. Moreover, our method is the only one which achieves satisfactory performance in tasks of multi-object stack and multi-object push&stack.

关键词： Deep learning Training Learning systems Automation Conferences Reinforcement learning Trajectory

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Fuzzy variable impedance control based on stiffness identification for human-robot cooperation

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IOP Conference Series: Earth and Environmental Science 2017年第1期69卷

作者： Dachao Mao Wenlong Yang Zhijiang Du State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin Heilongjiang 150080 China

This paper presents a dynamic fuzzy variable impedance control algorithm for human-robot cooperation. In order to estimate the intention of human for co-manipulation, a fuzzy inference system is set up to adjust the impedance parameter. Aiming at regulating the output fuzzy universe based on the human arm's stiffness, an online stiffness identification method is developed. A drag interaction task is conducted on a 5-DOF robot with variable impedance control. Experimental results demonstrate that the proposed algorithm is superior.

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Coordinate Dispatch of Combined Heat and Power System Based on Approximate Dynamic Programming

Coordinate Dispatch of Combined Heat and Power System Based ...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Bo Hu Ding Liu Chuanzhi Zang Yue Wang Wanting Li Yuqi Liu Peng Zeng State Grid Liaoning Electric Power Co. Ltd. Liaoning Shenyang China Key Laboratory of Networked Control System Shenyang Institute of Automation Chinese Academy of Sciences Liaoning Shenyang China Chinese Academy of Sciences University Beijing China Liaoning Lightning Protection Technical Service Center Shenyang Liaoning China

ISBN: (数字)9781728196060

ISBN: (纸本)9781728196077

Combined heat and power (CHP) can simultaneously produce electrical and thermal energy. CHP is an important and main component of the power system in areas with heat demand. However, its operating mode limits the flexibility of the system and aggravates the waste of renewable energy. This paper presents a coordinate dispatch algorithm for electric heating integrated energy system (EHIES), a combined heat and power system with wind power. An efficient optimization algorithm based on dynamic programming (DP) is developed, which overcomes the challenges of solving high dimensional optimization problems for the EHIES. The results of simulation experiments show that the proposed coordinate dispatch algorithm can meet the scheduling time requirements, reduce the operating cost of the system and realize the full consumption of wind power.

关键词： Cogeneration Heuristic algorithms Wind power generation Dynamic programming Optimization Space heating Resistance heating

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