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17th IEEE International Conference on Mechatronics and Automation, ICMA 2020

作者： Pang, Lei Cao, Zhiqiang Yu, Junzhi Zhang, Weimin Chen, Xuechao Institute of Automation State Key Laboratory of Management and Control for Complex Systems China Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems China

ISBN: (纸本)9781728164151

Person following is a crucial capability for mobile robots in tasks of human assistance and cooperation. In this paper, a person-following approach based on path planning is proposed for mobile robots, which takes person following and obstacle avoidance into account in a unified framework. The proposed approach is comprised of a sensing module and a planning module. The sensing module with a camera and a 3D LiDAR is employed to locate the target person and perceive obstacles. For the planning module, the combination of global planner and local planner effectively outputs collision-free paths and control commands for a mobile robot to keep following the target person. Experiments were conducted to evaluate the performance of the proposed approach. © 2020 IEEE.

关键词： Mobile robots

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Exploration on the Characteristics of 3d Crack Network Expansion Induced by Hydraulic Fracturing: A Hybrid Approach Combining Experiments and Algorithms

SSRN

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SSRN 2023年

作者： Jia, Li Peng, Shoujian Wu, Bin Xu, Jiang Yan, Fazhi Chen, Yuexia State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing400030 China State and Local Joint Engineering Laboratory of Methane Drainage in Complex Coal Gas Seam School of Resources and Safety Engineering Chongqing University Chongqing400030 China College of Safety and Emergency Management Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China School of Emergency Technology and Management North China Institute of Science and Technology Beijing101601 China

To realize the efficient development and utilization of unconventional energy, it is very necessary to explore the characteristics of 3D crack network expansion during sandstone hydraulic fracturing (HF). In this paper, through the physical simulation tests of sandstone HF under different shear stress levels, based on the CT image data acquisition, the 3D reconstruction of the HF specimen was realized, and the recognition algorithms of the 3D crack network were established. Based on the crack branch and bipartite graph representation, the graph theory representation method of crack network was improved, that is, the intersection of crack branch and crack branch and the extension of intersection line were used as the precursors of the elements in the two vertex sets U and V of bipartite graph , respectively, so that the topological structure representation method with crack branch as the basic element was extended to 3D crack network. Combined with topology and fractal theory, a quantitative characterization method of crack network was established, which comprehensively considers topological structure parameters and fractal dimension. With the increase of the shear stress level, the fractal dimension of the crack increases linearly, and the parameters of the crack topological structure increase first and then decrease. © 2023, The Authors. All rights reserved.

关键词： Hydraulic fracturing

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Locomotion control of a hybrid propulsion biomimetic underwater vehicle via deep reinforcement learning

Locomotion control of a hybrid propulsion biomimetic underwa...

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2021 IEEE International Conference on Real-Time Computing and Robotics, RCAR 2021

作者： Zhang, Tiandong Wang, Rui Wang, Yu Wang, Shuo Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai200031 China

ISBN: (纸本)9781665436786

This paper presents a novel deep reinforcement learning (DRL) method to solve the locomotion control problem of the biomimetic underwater vehicle (BUV) with hybrid propulsion, in order to meet the challenge of intractable multi-fins coordination and the complex hydrodynamic model. The system overview of the BUV, named RoboDact, with two flexible long fins and a double-joint fishtail as hybrid propulsion, is introduced. After that, the locomotion control problem is modeled as a Markov decision process (MDP) to be solved. Therefore, the locomotion control method based on soft actor-critic (SAC, a novel DRL algorithm) is proposed. The simulation environment is established based on the kinetic model for interaction. Finally, the feasibility and effectiveness of the proposed control method is demonstrated after extensive simulations. It will provide rich insights into the coordination control of biomimetic underwater vehicles. © 2021 IEEE.

关键词： Reinforcement learning

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On the 3d Fluid Behavior During Cbm Coproduction in a Multi Pressure system: Insights from Experimental Analysis and Mathematical Models

SSRN

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SSRN 2023年

作者： Dai, Shijie Xu, Jiang Jia, Li Chen, Jieren Yan, Fazhi Chen, Yuexia Peng, Shoujian State Key Laboratory of Coal Mine Disaster Dynamics and Control Chongqing University Chongqing400030 China State and Local Joint Engineering Laboratory of Methane Drainage in Complex Coal Gas Seam School of Resources and Safety Engineering Chongqing University Chongqing400030 China College of Safety and Emergency Management Engineering Taiyuan University of Technology Shanxi Taiyuan030024 China School of Emergency Technology and Management North China Institute of Science and Technology Beijing101601 China

To investigate the dynamic response characteristics of coalbed methane (CBM) reservoirs with multi pressure system induced by coproduction, physical simulation experiments were conducted on CBM coproduction under both conventional and constant-rate coproduction modes using a self-built experimental platform. The results showed that constant-rate coproduction can significantly extend the stable coproduction period of CBM compared to conventional coproduction, but it may have an inhibitory effect on certain reservoirs, which is negatively correlated with the initial reservoir pressure. Constant-rate coproduction can cause disturbance effects on reservoir pressure, leading to an increase in the expansion resistance of the pressure-reduction funnel during coproduction, and even the appearance of the pressure-buildup funnel of low-pressure reservoirs. A calculation principle for the 3D fluid flow parameters was developed by considering the dynamic permeability and the pressure gradient of the reservoir. The 3D fluid flow parameters were employed to quantitatively characterize the spatial and temporal evolution of fluid relative flow velocity and deflection angle in the reservoir during coproduction. Near the far end of the wellbore, the fluid flowed nearly horizontally, while near the wellbore, the fluid flowed towards or away from the wellbore center due to different levels of disturbance. © 2023, The Authors. All rights reserved.

关键词： Coal bed methane

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Society 5.0: Metaverse Facilitated Human-centric “5H” Services Across Cyber-Physical-Social Spaces

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IFAC-PapersOnLine 2024年第3期58卷 159-164页

作者： Xiao Wang Jun Huang QingHua Ni Zhe Wan HuaDan Xue Larry Stapleton Fei-Yue Wang Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei 230601 China Macau University of Science and Technology Macau 999078 China Beijing Normal University Beijing 100875 China Department of Radiology Peking Union Medical College Hospital Shuaifuyuan Beijing 100730 Dongcheng District China Brownsbarn Thomastown Co. Kilkenny Ireland R95D8P2 Qingdao Academy of Intelligent Industries Qingdao 266109 China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China

Cyber-Physical-Social systems bridge the gap between social resource organization and distribution, and give birth to Society 5.0, which represents a transformative vision aimed at realizing the human-centric paradigm for more efficient and effective sustainable development. In this paper, we discuss the decentralized paradigm in fostering human needs-driven smart services, in line with Maslow’s hierarchy of needs theory. Societies 5.0 is committed to optimizing the use of natural, artificial, and social resources by enhancing democratic participation and ensuring that decision-making processes are more transparent, reliable, and aligned with the needs of the populace in sustainable ways. Moreover, our discussion extends to how Society 5.0’s principles correlate with the United Nations Sustainable Development Goals (SDGs), providing a framework that supports health, education, and poverty reduction, among other targets. This approach also aligns with the Human Development Index (HDI), reinforcing the notion that technological progress in Society 5.0 does not overshadow but rather complements and enhances human values, paving a synergistic path towards sustainable development that is both advanced and fundamentally human-centric.

关键词： Cyber-Physical-Social systems Society 5.0 Human-Centric Services Socio-technical synergistic sustainable development

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Probabilistic Graph Matching with Multiplicative Updating Algorithm for Correspondence between Remote Sensing Images

Probabilistic Graph Matching with Multiplicative Updating Al...

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Information Communication and Software Engineering (ICICSE), IEEE International Conference on

作者： Jing Yang Xu Yang Zhang-Bing Zhou Zhi-Yong Liu School of Information Engineering China University of Geosciences(Beijing) State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Information Engineering China University of Geosciences(Beijing) TELECOM SudParis Beijing China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Center for Excellence in Brain Science and Intelligence Technology Beijing China

ISBN: (纸本)9781665430302

Point correspondence is an important problem in remote sensing images matching task which lays the foundation for the image registration task. Due to the existence of the rotation transformation and the large scale, and the change of scenery between the two obtained images, the point correspondence problem is still challenging. Tradition algorithms such as the SIFT and SURF aim at using the point appearance to find the point correspondence. However, when the above situations happens, the appearance based algorithms will fail to solve the problem. In this paper, the structural information between points is introduced to the correspondence problem and a spectral graph matching algorithm is proposed based on the probabilistic scheme and the multiplicative updating scheme. The proposed algorithm has three advantages compared with the traditional algorithms. First, incorporating the structural cues to the matching problem makes it more robust to the large scale and the geometric transformation. Second, the best assignment between points which is interpreted by a probabilistic manner is figured out by ranking the probabilities. Third, the optimization problem is solved by an approximate spectral method efficiently and a multiplicative updating algorithm is introduced to ensure the final discrete solution. Experiments on the real remote sensing images validate the effectiveness of the proposed algorithm.

关键词： Image registration Software algorithms Approximation algorithms Probabilistic logic Sensors Internet Task analysis

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Procedure Recognition by Knowledge-Driven Segmentation in Robotic-Assisted Vitreoretinal Surgery

Procedure Recognition by Knowledge-Driven Segmentation in Ro...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Zhen Li Yawen Deng Qiang Ye Weihong Yu Haoxiang Qi Yaliang Liu Zhangguo Yu Gui-Bin Bian School of Electronic and Information Engineering Tongji University Shanghai China Institute of Automation Chinese Academy of Sciences Beijing China School of Mechatronic Engineering Beijing Institute of Technology Beijing China Department of Ophthalmology Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China Key Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China International Joint Research Laboratory of Biomimetic Robots and Systems Ministry of Education Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Internal limiting membrane (ILM) peeling is a vital vitreoretinal surgery procedure. However, due to the thickness of just 1-2 micrometers and the intricacies associated with its varying density and adhesion, the difficulty of manipulation exceeds the physiological limits of human perception and operation. Surgical robot is characterized by high precision and stability. However, navigating intricate intraocular environments and handling minuscule high-precision areas remain enormous challenges. These include issues of uneven lighting, field-of-view loss, and motion blur. This paper proposed a perception method named ‘Multimodal Surgical Process Recognition based on Domain Knowledge and Segmentation (MSPR-DKS),’ designed to address these challenges and provide input for the precise control of robots. Moreover, a comprehensive dataset focused on ILM peeling during macular hole surgeries was established. Experimental results underscore the efficacy of this approach, with segmentation accuracies exceeding 99.27% for instruments and macular holes and an average accuracy of 98.97% in recognizing surgical processes. This study paves the way for leveraging domain knowledge and image segmentation to improve robot-assisted manipulation of soft tissues in ophthalmology.

关键词： Image segmentation Visualization Accuracy Medical robotics Motion segmentation Surgery Lighting

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Micro-displacement Amplifying Mechanism of a Piezoelectric Single Crystal Actuator and Its Motion Characterization

Micro-displacement Amplifying Mechanism of a Piezoelectric S...

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IEEE International Conference on Mechatronics and Automation

作者： Tianlu Zhang Zhangming Du Chao Zhou Zhiqiang Cao Shuo Wang Long Cheng State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728164168

ISBN: (纸本)9781728164175

Piezoelectric single crystal actuator is useful in the micro- and nano-scaled. But the range of the operation is limited. The paper presented the amplifying mechanism of the single crystal actuator in order to amplify the output of the piezoelectric actuator in micro- and nano-applications. Due to the single crystal's expansion when the applied signals decrease, the paper developed the adjustable spring that added to the amplifying mechanism. This paper tested the characteristics of the amplifying mechanism by designed a set of experiment device. The input and output displacement of the amplifying mechanism driven by kinds of signals were measured. It can concluded that the amplifying mechanism achieve the positive and negative displacement response and show its accuracy and stable. These excellent properties of the amplifying mechanism amplify the micro-displacement and expand the operating range of single crystal actuator in micro- and nano-positioning applications.

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Learning Latent Causal Factors from the Intricate Sensor Feedback of Contact-Rich Robotic Assembly Tasks

SSRN

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SSRN 2023年

作者： Chen, Yurou Yu, Jiyang Lin, Zhenyang Shen, Liancheng Liu, Zhiyong The State Key Lab 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 The Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China Beijing Institute of Spacecraft System Engineering China Academy of Space Technology Beijing China

Achieving precise robotic assembly is paramount for safety and performance in industrial settings. Conventional assembly methods require tedious manual adjustment of many parameters, and it is challenging to meet the assembly requirements with tight clearance. Robot learning has been at the forefront of research, showing potential for automation and intelligence in robotic operations. However, intricate information such as force feedback is indispensable for high-precision robot assembly tasks, and current learning methods grapple with processing this complex observation data due to discontinuous force changes during operation and the inherent noise from the force sensor. To alleviate the low learning efficiency from the problematic observations, we propose introducing the concept of latent causal factors that cause the sensor observations and truly matter in assembly tasks. This paper analyses the impact of discontinuous and noisy observations and offers two ways to infer causal factors. Based on the implied latent factors, we propose a method that learns high-precision assembly policies from interacting with the environment. The algorithm's performance is evaluated in simulated and real-world nut assembly environments, demonstrating significant improvements over the previous methods. This research also underscores the promise of causal inference in addressing intricate industrial challenges%. © 2023, The Authors. All rights reserved.

关键词： Robotic assembly

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Muscle-Synergy-Based Planning and Neural-Adaptive control for a Prosthetic Arm

IEEE Transactions on Artificial Intelligence

引用

IEEE Transactions on Artificial Intelligence 2021年第5期2卷 424-436页

作者： Li, Guoxin Li, Zhijun Li, Junjun Liu, Yueyue Qiao, Hong The Department of Automation University of Science and Technology of China Hefei230026 China The Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230026 China The School of Automation Science and Engineering South China University of Technology Guangzhou511442 China The State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing100190 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The Center for Excellence in Brain Science and Intelligence Technology Institute of Neuroscience Chinese Academy of Science Shanghai200031 China

Upper limb loss has significant effects on the individual's quality of life. Artificial prosthetic limbs as an alternative to the lost limb are designed to allow amputees to regain motor function. Motion classification via extracted surface electromyogram (sEMG) signals is widely utilized to realize a friendly human-robot interface in the control of the prosthesis. However, limited classification of discrete motion patterns from sEMG prevents intuitive motor control. Thus, instead of using discrete patterns, decoding the human intention continuously from sEMG would significantly benefit the prosthesis control. In this article, we propose a muscle-synergy-based intention decoding and motion planning that can model a broad set of complex upper limb movements as a combination of motor primitives. A novel muscle activation-to-force mapping model is developed to detect muscular effort of the healthy side to drive the affected side. A neural-network-approximation-based controller is developed for the bionic neuroprosthetic arm to execute the movement. Operational experiments with prosthetic movement control were performed on four healthy participants and an upper limb amputee participant. Results of controlling prosthetic arm (0.94 ± 0.02 of R2 in the horizontal reaching tasks and 0.95 ± 0.01 in the vertical reaching tasks for the healthy subjects, 0.95 and 0.97 for the amputee) demonstrate that our control method could successfully capture human movement intention and effectively control the movement of prosthesis. Impact statement-Powered robotic prostheses have enabled individuals with limb amputations to regain motor function via motion intention decoding from extracting surface electromyogram signals. Most research works have provided human-robot interaction through motion classification obtained from pattern recognition using surface electromyogram signals and simple movement control. However, decoding the human intent continuously from surface electromyogram is con

关键词： Matrix algebra

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