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IEEE International Conference on Robotics and Biomimetics

作者： Boyue Zhang Shaowei Cui Chaofan Zhang Jingyi Hu Shuo Wang School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China 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 Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences Shanghai China

ISBN: (纸本)9781665481106

In robot grasp and dexterous manipulation tasks, tactile sensing is important for the control adjustment of the manipulator. In this paper, we present a novel low-cost parallel gripper with high-resolution tactile sensing, named the GelStereo Gripper. Furthermore, an adaptive grasp strategy is proposed to endow the gripper with tactile-feedback grasp stability-maintaining ability. We install the gripper on our robot platform and conduct various grasp experiments by utilizing proposed control methods. Experimental results verify the reliability of the GelStereo gripper and also prove the effectiveness of the proposed strategy for experimental objects with different features.

关键词： Shape Pose estimation Force Tactile sensors Grasping Robot sensing systems Sensors

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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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Learning control of dynamical systems based on Markov decision processes: Research frontiers and outlooks

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Zidonghua Xuebao/Acta Automatica Sinica 2012年第5期38卷 673-687页

作者： Xu, Xin Shen, Dong Gao, Yan-Qing Wang, Kai Institute of Automation College of Mechatronic Engineering and Automation National University of Defense Technology Changsha 410073 China Institute of Automation Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Management and Control for Complex Systems Beijing 100190 China Department of Systems and Industrial Engineering University of Arizona Tucson AZ85721-0020 United States Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha 410073 China

Learning control of dynamical systems based on Markov decision processes (MDPs) is an interdisciplinary research area of machine learning, control theory, and operations research. The main objective in this research area is to realize data-driven multi-stage optimal control for complex or uncertain dynamical systems. This paper presents a comprehensive survey on the theory, algorithms, and applications of MDP-based learning control of dynamical systems. Emphases are put on recent advances in the theory and methods of reinforcement learning (RL) and adaptive/approximate dynamic programming (ADP), including temporal-difference learning theory, value function approximation for continuous state and action spaces, direct policy search, approximate policy iteration, and adaptive critic designs. Applications and the trends for future research and developments in related fields are also discussed. Copyright © 2012 Acta Automatica Sinica. All rights reserved.

关键词： Markov processes

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Scenario Engineering for Autonomous Transportation: A New Stage in Open-Pit Mines

arXiv

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arXiv 2024年

作者： Teng, Siyu Li, Xuan Li, Yuchen Li, Lingxi Xuanyuan, Zhe Ai, Yunfeng Chen, Long Hong Kong Baptist University Kowloon999077 Hong Kong BNU-HKBU UIC Zhuhai China The Department of Mathematics and Theories Peng Cheng Laboratory Shenzhen518000 China Indianapolis United States Guangdong Provincial Key Laboratory IRADS BNU-HKBU UIC Zhuhai519087 China The University of Chinese Academy of Sciences Beijing100049 China The State Key Laboratory of Multimodal Artificial Intelligence Systems The State Key Laboratory of Management and Control for Complex Systems Chinese Academy of Sciences Beijing100190 China WAYTOUS Inc. Beijing100083 China Shenzhen518107 China

In recent years, open-pit mining has seen significant advancement, the cooperative operation of various specialized machinery substantially enhancing the efficiency of mineral extraction. However, the harsh environment and complex conditions in open-pit mines present substantial challenges for the implementation of autonomous transportation systems. This research introduces a novel paradigm that integrates Scenario Engineering (SE) with autonomous transportation systems to significantly improve the trustworthiness, robustness, and efficiency in open-pit mines by incorporating the four key components of SE, including Scenario Feature Extractor, Intelligence and Index, Calibration and Certification, and Verification and Validation. This paradigm has been validated in two famous open-pit mines, the experiment results demonstrate marked improvements in robustness, trustworthiness, and efficiency. By enhancing the capacity, scalability, and diversity of autonomous transportation, this paradigm fosters the integration of SE and parallel driving and finally propels the achievement of the '6S' objectives. Copyright © 2024, The Authors. All rights reserved.

关键词： Efficiency

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A Multi-Objective Optimization Algorithm: Power system Hybrid Static Economic Dispatch with Wind Energy

SSRN

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SSRN 2022年

作者： Qu, Linan Li, Lingling Tseng, Ming-Lang Chen, Chao Lim, Ming K. State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin300130 China Nanjing210003 China Institute of Innovation and Circular Economy Asia University Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan Key Laboratory of Complex System Control Theory and Application Tianjin University of Technology Tianjin300384 China Adam Smith Business School University of Glasgow United Kingdom

This study proposes a novel multi-objective optimization based on improved pathfinder algorithm (IPFA) algorithm and contributes to build a hybrid static dynamic economic dispatch with wind energy. The novel IPFA optimizes the built hybrid static economic dispatch model, while considering inequality and equality constraints during the model solving process. Especially, new energy system reduces the fuel consumption and operation cost of the system;however, the randomness of renewable energy output destroys the stability of the system. The upper limit of wind power grid connection is limited by the permeability constraint, and the constraints such as forbidden operation area constraint and slope rate constraint are considered. The solution speed and stability of the IPFA algorithm are improved through introducing Kent map initialization, nonlinear adaptation factor, and following correction strategy. The validity of the built model and the optimization performance of the proposed method is verified by two cases. The test results reveal that the operation cost obtained by the IPFA considering wind energy is 9.3% and 7.5% lower than that without considering wind energy. The novel method contributes in ensuring the economy and stability of the power system and improving the utilization rate of renewable energy. © 2022, The Authors. All rights reserved.

关键词： Electric load dispatching

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CPSS-based Signal Forwarding Method at Relays for Full-duplex Cooperative Vehicular Networks

CPSS-based Signal Forwarding Method at Relays for Full-duple...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Han, Shuangshuang Ma, Houxue Wang, Xiao Liu, Hong Cao, Dongpu Wang, Fei-Yue State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Shandong266000 China Qingdao Huituo Intelligent Machine Company Qingdao China JIANGSU XCMG CONSTRUCTION MACHINERY RESEARCH INSTITUTE LTD Xuzhou Jiangsu221004 China Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue Wes WaterlooONN2L 3G1 Canada Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781538644522

With increasing popularity of Internet of Vehicles (IoV), concerns for reliable and low complexity communication techniques are proposed due to the requirements of signal reliability and transmission delay for vehicles. Meanwhile, the explosive and pervasive use of social network applications further adds drivers' social relationships and behavioural characteristics into it, and makes it a cyber-physical-social system (CPSS). This paper proposes and analyzes an improved forward scheme for full deplex cooperative vehicular networks in terms of its CPSS features. The proposed CPSS-based forwarding (CPSS-F) strategy forwards a soft estimate of the received signal based on social historic data at the relay node (vehicle/infrastructure) to the destination node (vehicle/infrastructure), which achieves improved reliability than the two conventional strategies in cooperative networks, i.e., amplify-and-forward (AF) and decode-and-forward (DF). Furthermore, the proposed CPSS-F relay achieves performance gains and complexity reduction compared to the conventional AF and DF. Experimental results further confirm the advantages of the proposed CPSS-F for cooperative vehicular networks. The proposed CPSS-F approach is easily extended to other cooperative vehicular social networks, for example, multi-way, half-duplex, or large-antenna networks. © 2018 IEEE.

关键词： complex networks

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End-to-End Driving Activities and Secondary Tasks Recognition Using Deep Convolutional Neural Network and Transfer Learning

End-to-End Driving Activities and Secondary Tasks Recognitio...

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2018 IEEE Intelligent Vehicles Symposium, IV 2018

作者： Xing, Yang Tang, Jianlin Liu, Hong Lv, Chen Cao, Dongpu Velenis, Efstathios Wang, Fei-Yue Advanced Vehicle Engineering Centre Cranfield University BedfordMK43 0AL United Kingdom Vehicle Intelligence Pioneers Ltd Qingdao China Jiangsu XCMG Construction Machinery Research Institute Ltd. No. 26 Tuolanshan Road Economic Development Zone Xuzhou Jiangsu221004 China School of Mechanical and Mechatronics Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China

ISBN: (纸本)9781538644522

Drivers' decision and their corresponding behaviors are important aspects that can affect the driving safety, and it is necessary to understand the driver behaviors in real-time. In this study, an end-to-end driving-related tasks recognition system is proposed. Specifically, seven common driving activities are identified, which are normal driving, right mirror checking, rear mirror checking, left mirror checking, using in-vehicle video device, texting, and answering mobile phone. Among these, the first four activities are regarded as normal driving tasks, while the rest three are divided into distraction group. The images are collected using a consumer range camera, namely, Kinect. In total, five drivers are involved in the naturalistic data collection. Before training the identification model, the raw images are first segmented using a Gaussian mixture model (GMM) to extract the driver region from the background. Then, a pre-trained deep convolutional neural network (CNN) model is trained to classify the behaviors, which directly takes the processed RGB images as the input and outputs the identified label. In this work, the AIexNet is selected as the pre-trained CNN model. Then, to reduce the training cost, the transfer learning mechanism is applied to the CNN model. An average of 79\% detection accuracy is achieved for the seven driving tasks. The proposed integration model can be used as a low-cost driver distraction and dangerous tasks recognition modeL. © 2018 IEEE.

关键词： Neural network models

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Sharing Mobilized Energy Storage for Temporal-Spatial Coordination of Transportation and Power systems

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Journal of Modern Power systems and Clean Energy 2024年第3期13卷 892-903页

作者： Yuhang Ding Xinjiang Chen Guangchun Ruan Gengyin Li Ming Zhou Jiang Dai Jianxiao Wang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China State Grid Hangzhou Lin'an District Power Supply Company Hangzhou China Department of Industrial Engineering and Management College of Engineering Peking University Beijing China Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Power Dispatching and Control Center Guizhou Power Grid Company Guiyang China National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China

Mobilized energy storage (MES) can provide a variety of services for power systems, including peak shaving, frequency regulation, and congestion alleviation. In this paper, we develop an MES sharing approach based on temporal-spatial network (TSN) toward systemwide temporal-spatial flexibility enhancement, specifically in which the heavy-duty vehicles can exchange batteries at the energy storage stations connected with power grids. To achieve the temporal-spatial coordination of transportation and power systems, we propose a coordinated scheduling model. A decentralized algorithm based on the improved optimality condition decomposition (OCD) algorithm is proposed to address the information asymmetry between transportation and power systems while enhancing computational efficiency. Case studies based on IEEE 30-/118-bus and transportation systems demonstrate that MESs using the proposed approach can significantly improve the utilization of batteries while reducing operating costs by over 40% compared with stationary energy storages (SESs).

关键词： Transportation Power systems Batteries Planning Costs Power grids Uncertainty

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An Active and Adaptive Image Enhancement Method for Applications in Low-Light and Narrow Environment

An Active and Adaptive Image Enhancement Method for Applicat...

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Chinese Automation Congress (CAC)

作者： Mingrui Luo En Li Rui Guo Shengchuan Li Cunfeng Kang The 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 State Grid Shandong Electric Power Company Jinan China State Grid Liaoning Electric Power Company Limited Shenyang China Mechanical engineering and Applied Electronic Technology Beijing University of Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The images in the low-light and narrow environment generally have the problems of non-uniform illumination, low contrast, and large random noise. Existing algorithms are mostly subject to the imaging effect of the original image, and are prone to failure in extremely weak light or strong reflective environments. This paper proposes an active and adaptive image enhancement method. First of all, this paper combines the distributed light source model and the difference in brightness between frames to complete active feedback fill light, which improves the quality of the input image. Then gamma correction, contrast limited adaptive histogram equalization and bilateral filter are designed to enhance image details and suppress noise amplification. Combined with fuzzy inference parameter self-adjustment method, algorithm adaptability is improved. Finally, through experimental verification, the method proposed in this paper can effectively enhance the images in the low-light and narrow environment.

关键词： Light sources Histograms Adaptive equalizers Filtering algorithms Brightness Image color analysis Image enhancement

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Rethinking temporal object detection from robotic perspectives

arXiv

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arXiv 2019年

作者： Chen, Xingyu Wu, Zhengxing Yu, Junzhi Wen, Li State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China Beijing Innovation Center Engineering Science and Advanced Technology Peking University Beijing100871 China School of Mechanical Engineering and Automation Beihang University Beijing100191 China

Video object detection (VID) has been vigorously studied for years but almost all literature adopts a static accuracy-based evaluation, i.e., average precision (AP). From a robotic perspective, the importance of recall continuity and localization stability is equal to that of accuracy, but the AP is insufficient to reflect detectors' performance across time. In this paper, non-reference assessments are proposed for continuity and stability based on object tracklets. These temporal evaluations can serve as supplements to static AP. Further, we develop an online tracklet refinement for improving detectors' temporal performance through short tracklet suppression, fragment filling, and temporal location fusion. In addition, we propose a small-overlap suppression to extend VID methods to single object tracking (SOT) task so that a flexible SOT-by-detection framework is then formed. Extensive experiments are conducted on ImageNet VID dataset and real-world robotic tasks, where the superiority of our proposed approaches are validated and verified. Codes will be publicly available. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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