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arXiv 2024年

作者： Wang, Xiao Tang, Ke Dai, Xingyuan Xu, Jintao Du, Quancheng Ai, Rui Wang, Yuxiao Gu, Weihao Engineering Research Center of Autonomous Unmanned System Technology Ministry of Education Anhui University Hefei230031 China Haomo Technology Co. Ltd. Beijing100192 China The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Qingdao Academy of Intelligent Industries Qingdao266114 China The School of Computer and Communication Engineering University of Science and Technology Beijing Beijing100083 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China

In public roads, autonomous vehicles (AVs) face the challenge of frequent interactions with human-driven vehicles (HDVs), which render uncertain driving behavior due to varying social characteristics among humans. To effectively assess the risks prevailing in the vicinity of AVs in social interactive traffic scenarios and achieve safe autonomous driving, this article proposes a social-suitable and safety-sensitive trajectory planning (S4TP) framework. Specifically, S4TP integrates the Social-Aware Trajectory Prediction (SATP) and Social-Aware Driving Risk Field (SADRF) modules. SATP utilizes Transformers to effectively encode the driving scene and incorporates an AV’s planned trajectory during the prediction decoding process. SADRF assesses the expected surrounding risk degrees during AVs-HDVs interactions, each with different social characteristics, visualized as two-dimensional heat maps centered on the AV. SADRF models the driving intentions of the surrounding HDVs and predicts trajectories based on the representation of vehicular interactions. S4TP employs an optimization-based approach for motion planning, utilizing the predicted HDVs’ trajectories as input. With the integration of SADRF, S4TP executes real-time online optimization of the planned trajectory of AV within low-risk regions, thus improving the safety and the interpretability of the planned trajectory. We have conducted comprehensive tests of the proposed method using the SMARTS simulator. Experimental results in complex social scenarios, such as unprotected left-turn intersections, merging, cruising, and overtaking, validate the superiority of our proposed S4TP in terms of safety and rationality. S4TP achieves a pass rate of 100% across all scenarios, surpassing the current state-of-the-art methods Fanta of 98.25% and Predictive-Decision of 94.75%. Copyright © 2024, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Research On Optimization Of Electric Vehicle Charging Scheduling Based On Blockchain

Research On Optimization Of Electric Vehicle Charging Schedu...

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IEEE International Conference on Energy Internet (ICEI)

作者： Yuhong Di Ru Huo Chuang Sun Shiqin Zeng Zongxuan Sha Tao Huang Faculty of Information Technology Beijing University of Technology Beijing China Department of Automation Tsinghua University Beijing China System Operation Department Shenzhen Power Supply Bureau Co. Ltd Shenzhen China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China

With the popularization and application of new energy, the vigorous development of electric vehicles is facilitated. While the number of electric vehicles has increased significantly, a large number of electric vehicles are charged in disorder, resulting in the problems of low charging efficiency and serious grid load. This paper proposes a blockchain-based charging scheduling strategy for electric vehicles. Firstly, a trusted information platform based on blockchain is built, and a secure scheduling architecture for electric vehicle charging based on blockchain is designed. Furthermore, considering the factors of user reputation, time-of-use electricity price, and distance consumption, a user behavior-based calculation method of the cost is proposed. Finally, an electric vehicle charging scheduling strategy based on Particle Swarm Optimization is constructed to achieve the goal of orderly charging scheduling and peak shaving and valley filling of a large number of electric vehicles. The theoretical analysis and simulation experiments show that the proposed optimization scheme could effectively reduce user overhead and grid pressure.

关键词： Scheduling algorithms Information security Power system stability Electric vehicle charging Power grids Stability analysis Scheduling

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Data-driven modeling the nonlinear backlash of steerable endoscope under a large deflection cannulation in ERCP surgery

Data-driven modeling the nonlinear backlash of steerable end...

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

作者： Jiang, Wei Yu, Tao He, Xiao Yang, Yongming Wang, Zhidong Liu, Hao Shenyang Institute of Automation Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Minimally Invasive Surgical Robot Liaoning Province Shenyang110016 China Key Laboratory of Minimally Invasive Surgical Liaoning Province Shenyang110016 China Department of Advanced Robotics Chiba Institute of Technology Narashino Chiba275-0016 Japan

ISBN: (纸本)9781665436786

Unexpected nonlinear backlash performances of two degrees of freedom (DoFs) tendon sheath mechanism (TSM) have serious impacts on the precise cannulation in ERCP (Endoscopic Retrograde Cholangio-Pancreatography) surgery. We proposed to model backlash performance of two DoFs steerable endoscope by using LSTM (Long Short-Term Memory) network. Trajectory following and orientation tasks are performed after the backlash model has been considered into the control strategy. Experimental results show that the LSTM backlash model can accurately describe the non-linearities of backlash, and significantly reduce the positioning and orientation deviation of the steerable endoscope under large deflection. The proposed backlash modeling method can be extended to be applied to bronchial, urethral and other natural orifice interventional tasks that work under a large deflection. © 2021 IEEE.

关键词： Endoscopy

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Congestion Management in Local Market Considering the Payback Effect 6

Congestion Management in Local Market Considering the Paybac...

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6th Asia Conference on Power and Electrical Engineering, ACPEE 2021

作者： Masood, Arsalan Xin, Ai Abbas Rizvi, Syed Asad Saeed Qazi, Hasan Qambrani, Lashkar Khan Khan, Salman North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing China National Electric Power Regulatory Authority Consumer Affairs Department Islamabad Pakistan Beijing Institute of Technology School of Automation Advanced Power Conversion Center Beijing China

ISBN: (纸本)9781728191591

The proliferation of distributed energy resources is gaining momentum and it brings several challenges for the power system such as network congestions. To solve network congestion, conventional grid reinforcements were used. However, with the introduction of demand flexibility, the system operators can now defer the grid reinforcements and chose an alternate solution. By using demand flexibility and providing flexibility services, the load of the consumer can be shifted to other times. However, this shifting can also bring different challenges such as creating new peaks. This paper aims to solve the network congestion by using flexibility services attained from the consumers while considering the energy payback effect to avoid further peaks. In this work, an IEEE 13- bus system is employed and modeling of the bidding model along with payback conditions are presented. In the end, the cost incurred by the system operator for providing flexibility is calculated using different case studies. © 2021 IEEE.

关键词： Reinforcement

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AGV Semantic Attack Detection Based on Hidden Markov Model

AGV Semantic Attack Detection Based on Hidden Markov Model

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IEEE International Conference on Industrial technology (ICIT)

作者： Sichao Zhang Wei Liang Yinlong Zhang Zhibo Pang State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China Department of Automation Technology ABB Corporate Research Sweden Vasteras Sweden

ISBN: (纸本)9781728119496

Automatic Guided Vehicle (AGV) semantic attack is now becoming an important research topic in digital workshops. Unfortunately, the conventional attack detections could trigger the wrong attack detection alarms, because of the unexpected wireless radio frequency interference. This paper investigates a new method for AGV semantic attack detection based on Hidden Markov Model (HMM). Our method is able to discriminate the typical wireless retransmission in presence of environment interference or in the context of AGV semantic attack by comparing the hidden state transition probability with the predefined threshold. Our model has been evaluated on our developed platform. The extensive experimental results and analysis prove the effectiveness of our method.

关键词： Wireless communication Conferences Semantics Hidden Markov models Interference Markov processes Radiofrequency interference

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Dominant Mode Identification and Influence Mechanism Investigation of Frequency Oscillations as Affected by Automatic Generation Control

SSRN

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SSRN 2022年

作者： Zhou, Tao Chen, Liufu Bu, Siqi Ye, Hua Sun, Jie School of Automation Nanjing University of Science and Technology Nanjing China Department of Electrical Engineering The Hong Kong Polytechnic University Hong Kong Special Administrative Region Hong Kong Key Laboratory of Power System Intelligent Dispatch and Control Ministry of Education Shandong University Jinan China

Several frequency oscillation events have recently been observed in practical power grids, which are strongly related with dynamic process of AGC (automatic generation control). To investigate the dynamic performance and oscillation mechanism of frequency oscillations, this paper establishes the state-space model of multi-area AGC systems containing different kinds of generator units and time-scale dynamics. Without distinguishing AGC and PFR (primary frequency regulation) modes, Homotopy method is applied to identify the dominant mode and thus analyze the key influencing factors of frequency oscillations affected by AGC dynamics. The method can clearly establish the congruent relationship between eigenvalues and state variables and is more rapid and intuitive than participation factors in this scene. Finally, simulations are performed in single-area as well as multi-area system based on real power grid data to demonstrate the proposed model and method. And relevant parameter influence mechanism and oscillation performances are investigated for AGC system oscillations. © 2022, The Authors. All rights reserved.

关键词： Time delay

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Mechanics of Soft-Body Rolling Motion without External Torque

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Physical Review Letters 2025年第19期134卷 198401-198401页

作者： Xudong Liang Yimiao Ding Zihao Yuan Yuxuan Han Yutang Zhou Junqi Jiang Zongling Xie Peng Fei Yixuan Sun Pan Jia Guoying Gu Zheng Zhong Feifei Chen Guangwei Si Zhefeng Gong School of Science Harbin Institute of Technology Shenzhen 518055 China Department of Neurology of the Fourth Hospital and School of Brain Science and Brain Medicine Zhejiang University School of Medicine Hangzhou 310058 China Robotics Institute and State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Shanghai 200240 China State Key Laboratory of Cognitive Science and Mental Health Institute of Biophysics Chinese Academy of Sciences 15 Datun Road Beijing 100101 China University of Chinese Academy of Sciences 19A Yuquan Road Beijing 100049 China Huazhong University of Science and Technology Optical and Electronic Information Wuhan 430074 China Zhejiang Lab Hangzhou 311121 China Shanghai Key Laboratory of Intelligent Robotics Meta Robotics Institute Shanghai Jiao Tong University Shanghai 200240 China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine Integration State Key Laboratory of Brain-machine Intelligence Zhejiang University 1369 West Wenyi Road Hangzhou 311121 China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Hangzhou 310058 China

The Drosophila larva, a soft-body animal, can bend its body and roll efficiently to escape danger. However, contrary to common belief, this rolling motion is not driven by the imbalance of gravity and ground reaction forces. Through functional imaging and ablation experiments, we demonstrate that the sequential actuation of axial muscles within an appropriate range of angles is critical for generating rolling. We model the interplay between muscle contraction, hydrostatic skeleton deformation, and body-environment interactions, and systematically explain how sequential muscle actuation generates the rolling motion. Additionally, we construct a pneumatic soft robot to mimic the larval rolling strategy, successfully validating our model. This mechanics model of soft-body rolling motion not only advances the study of related neural circuits, but also holds potential for applications in soft robotics.

关键词： Creatinine

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Autonomous Guidewire Navigation for Robot-assisted Endovascular Interventions: A Knowledge-Driven Visual Guidance Approach

arXiv

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arXiv 2024年

作者： Liu, Wentao Xu, Weijin Li, Xiaochuan Liang, Bowen He, Ziyang Zhu, Mengke Song, Jingzhou Yang, Huihua Lu, Qingsheng Dynamic Products Department Wandong Medical Technology Co. Ltd Beijing100015 China Department of Cyberspace Defense Beijing Police College Beijing102202 China School of Intelligent Engineering and Automation Beijing University of Posts and Telecommunications Beijing100876 China Department of Vascular Surgery Shanghai Changhai Hospital Naval Medical University Shanghai200433 China State Key Laboratory of Space-Ground Integrated Information Technology Beijing Institute of Satellite Information Engineering Beijing100086 China

Autonomous robots for endovascular interventions hold significant potential to enhance procedural safety and reliability by navigating guidewires with precision, minimizing human error, and reducing surgical time. However, existing methods of guidewire navigation rely on manual demonstration data and have a suboptimal success rate. In this work, we propose a knowledge-driven visual guidance (KVG) method that leverages available visual information from interventional imaging to facilitate guidewire navigation. Our approach integrates image segmentation and detection techniques to extract surgical knowledge, including vascular maps and guidewire positions. We introduce BDA-star, a novel path planning algorithm with boundary distance constraints, to optimize trajectory planning for guidewire navigation. To validate the method, we developed the KVD-Reinforcement Learning environment, where observations consist of real-time guidewire feeding images highlighting the guidewire tip position and the planned path. We proposed a reward function based on the distances from both the guidewire tip to the planned path and the target to evaluate the agent’s ***, to address stability issues and slow convergence rates associated with direct learning from raw pixels, we incorporated a pre-trained convolutional neural network into the policy network for feature extraction. Experiments conducted on the aortic simulation autonomous guidewire navigation platform demonstrated that the proposed method, targeting the left subclavian artery, left carotid artery and the brachiocephalic artery, achieved a 100% guidewire navigation success rate, along with reduced movement and retraction distances and trajectories tend to the center of the vessels. Copyright © 2024, The Authors. All rights reserved.

关键词： Image segmentation

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NeRF in Robotics: A Survey

arXiv

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arXiv 2024年

作者： Wang, Guangming Pan, Lei Peng, Songyou Liu, Shaohui Xu, Chenfeng Miao, Yanzi Zhan, Wei Tomizuka, Masayoshi Pollefeys, Marc Wang, Hesheng The University of Cambridge ETH Zurich Switzerland The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Key Laboratory of System Control and Information Processing Ministry of Education of China China Engineering Research Center of Intelligent Control for Underground Space Ministry of Education School of Information and Control Engineering Advanced Robotics Research Center China University of Mining and Technology Xuzhou221116 China ETH Zurich Zurich Switzerland Mechanical Systems Control Laboratory University of California Berkeley United States

Meticulous 3D environment representations have been a longstanding goal in computer vision and robotics fields. The recent emergence of neural implicit representations has introduced radical innovation to this field as implicit representations enable numerous capabilities. Among these, the Neural Radiance Field (NeRF) has sparked a trend because of the huge representational advantages, such as simplified mathematical models, compact environment storage, and continuous scene representations. Apart from computer vision, NeRF has also shown tremendous potential in the field of robotics. Thus, we create this survey to provide a comprehensive understanding of NeRF in the field of robotics. By exploring the advantages and limitations of NeRF, as well as its current applications and future potential, we hope to shed light on this promising area of research. Our survey is divided into two main sections: The Application of NeRF in Robotics and The Advance of NeRF in Robotics, from the perspective of how NeRF enters the field of robotics. In the first section, we introduce and analyze some works that have been or could be used in the field of robotics from the perception and interaction perspectives. In the second section, we show some works related to improving NeRF's own properties, which are essential for deploying NeRF in the field of robotics. In the discussion section of the review, we summarize the existing challenges and provide some valuable future research directions for reference. © 2024, CC BY-NC-ND.

关键词： Robotics

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A Real-time Fire Segmentation Method Based on A Deep Learning Approach

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IFAC-PapersOnLine 2022年第6期55卷 145-150页

作者： Mengna Li Youmin Zhang Lingxia Mu Jing Xin Ziquan Yu Shangbin Jiao Han Liu Guo Xie Yi Yingmin Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology Xi'an Shaanxi 710048 China Department of Mechanical Industrial and Aerospace Engineering Concordia University Montreal Quebec H3G 1M8 Canada College of Automation Engineering Nanjing University of Aeronautics and Astronautics (NUAA) Nanjing Jiangsu China

As a kind of the forest “fault”, fire is highly destructive and difficult to rescue. Fire segmentation is helpful for firefighters to understand the fire scale and formulate a reasonable fire-fighting plan. Therefore, this paper proposes a real-time fire segmentation method based on deep learning. This method is an improved version of deeplbav3+, which is an encoder-decoder structure network. Encoder network is composed of deep convolutional neural network and atrous spatial pyramid pooling. Different from deeplabv3+, in order to improve the segmentation speed, this paper uses the lightweight network mobilenetv3 to build a new deep convolutional neural network and does not use atrous convolution, but it will affect the segmentation accuracy. Therefore, in order to compensate for the loss of segmentation accuracy, on the basis of the original decoder network, this paper adds two different shallow features to make the network contain rich fire feature information. Experimental results show that the comprehensive performance of this method is better than the original deeplabv3+, especially the segmentation speed of the network is greatly improved, which is about 59 FPS.

关键词： Forest fire Fire segmentation Atrous spatial pyramid pooling Mobilenetv3 Deeplabv3+

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