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第35届中国控制与决策会议

作者： Sichao Zhang Wei Liang Xudong Yuan Haibo An Meng Zheng Yinlong Zhang State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences University of Chinese Academy of Sciences Science and Technology on Information Systems Engineering Laboratory the 28th Research Institute of CETC

Automatic Guided Vehicle(AGV) has been widely used in the warehouse for transporting the bulky and heavy ***,the AGV may deviate the regular trajectories in presence of incorrect or untimely commands sent form the server due to,e.g.,cyber attacks,unexpected blocks of the wireless *** order to ensure AGV running safely,this paper presents a visual surveillance system by making full use of the measurements from the forward and downward ***,the forward camera estimates the AGV positions and attitudes by tracking the surrounding landmarks detected from the forward image ***,the downward camera is used to detect the QR codes fixed on the floor and estimate the AGV poses in the absolute reference *** from that,the AGV poses from the downward camera could correct scale and poses estimated the forward *** proposed method has been extensively performed on the developed *** results proves the effectiveness in using the complementary forwarddownward visual measurements for AGV security surveillance.

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Design and Simulation of a Helical Microrobot for Maximum Speed Enhancement by Physical Surface Modification

Design and Simulation of a Helical Microrobot for Maximum Sp...

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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Guo, Siyu Wang, Maolin Wang, Huaping Zhong, Shihao Qiu, Yukang Hou, Yaozhen Shi, Qing Fukuda, Toshio Beijing Institute of Technology Intelligent Robotics Institute School of Mechatronical Engineering Beijing100081 China Ministry of Education Key Laboratory of Biomimetic Robots and Systems Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Beijing Institute of Technology Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (纸本)9798350327182

Magnetic helical microrobots have great potential in biomedical applications. However, improving their motion performance in the complex and variable in vivo fluid environment remains challenging. Previous researches mainly focus on optimizing the geometric parameter, the evolutionary structure design, and chemical modifications of the robots, with limited attention given to the impact of physical surface modifications on propulsion performance. To address this issue, we design the helical microrobot with different surface physical modifications, including dimpled and raised surfaces, to evaluate their effects on swimming performance. With ANSYS Fluent, we simulated their swimming process in water and found that microrobots with physically modified surfaces experience less resistance and exhibit a larger step-out frequency while swimming. Moreover, our findings suggest that helical microrobots with dimpled surfaces demonstrate superior swimming performance due to their larger gas-liquid interface area compared to those with raised surfaces. © 2023 IEEE.

关键词： Chemical modification

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Reinforcement Learning for Dynamic Task Execution: A Robotic Arm for Playing Table Tennis

Reinforcement Learning for Dynamic Task Execution: A Robotic...

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IEEE International Conference on robotics and Biomimetics

作者： Zida Zhao Haodong Huang Shilong Sun Jing Jin Wenjie Lu School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen China Harbin Institute of Technology Shenzhen State Key Laboratory of Robotics and Systems (HIT) Suzhou Research Institute of HIT

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Dynamic tasks like table tennis pose a significant challenge for robots, although relatively easy for humans to learn. These tasks require precise control of rapid movements and timing in situations where the estimation of the flying ball and the robot's state is imprecise. Traditional model-based control methods struggle to accurately model and adapt to these dynamic changes, leading to difficulties and challenges in practical applications. To address this issue, a table tennis hitting method for a robotic arm based on reinforcement learning (RL) is proposed in this paper. Firstly, a simulation model of a robotic arm with eight degrees of freedom for playing table tennis was constructed. Secondly, a simple and effective reward function was designed to accomplish the task of hitting the table tennis ball with the robotic arm. Finally, two sets of experiments involving single and random ball launches were designed. Experimental results demonstrate that the proposed method enables the robotic arm to accurately hit the table tennis ball, showcasing the potential of RL in dynamic tasks.

关键词： Adaptation models Sports equipment Filtering Estimation Reinforcement learning Manipulators Robustness Timing PD control Sports

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Parameter-Sharing-Based Average-Consensus Time Synchronization in IoT Networks

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IEEE Internet of Things Journal 2023年第9期10卷 8215-8227页

作者： Shi, Fanrong Yang, Simon X. Mukherjee, Mithun Jiang, Hong Da Costa, Daniel Benevides Wong, Wing-Kwong Southwest University of Science and Technology Robot Technology Used for Special Environment Key Laboratory of Sichuan Province School of Information Engineering Mianyang621010 China School of Engineering University of Guelph Advanced Robotics and Intelligent Systems Laboratory GuelphONN1G 2W1 Canada Nanjing University of Information Science and Technology School of Artificial Intelligence School of Future Technology Nanjing210044 China Technology Innovation Institute Abu Dhabi United Arab Emirates National Yunlin University of Science and Technology Future Technology Research Center Douliu64002 Taiwan

Average-consensus protocol is one of the ways to develop distributed time-synchronization algorithms in Internet of Things (IoT) networks. However, the large number of iteration leads to a common time notion issue in nodes. This poses a critical challenge in the convergence of the time-synchronization algorithm and resulting asymptotic convergence in the average consensus protocol. In this article, a parameter-sharing-based average-consensus time-synchronization (PACTS) algorithm is proposed. For fast convergence, the proposed PACTS quickly forwards the time information to multihop nodes and employs multihop average-consensus instead of single-hop average consensus. Specifically, a node asynchronously and periodically broadcasts the relative clock offset estimation of neighbors with its local time information. Meanwhile, the relative clock offset estimation of the multihop node is calculated and used to estimate the average value. Consequently, an average consensus among local multihop nodes is obtained. As a result, the iteration number and convergence time are significantly reduced over the network. Finally, the experimental results indicate that the proposed PACTS algorithm has low complexity, high accuracy, and quick convergence. © 2023 IEEE.

关键词： Topology

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An Intelligent Deterministic Scheduling Method for Ultralow Latency Communication in Edge Enabled Industrial Internet of Things

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IEEE Transactions on Industrial Informatics 2023年第2期19卷 1756-1767页

作者： Lu, Yinzhi Yang, Liu Yang, Simon X. Hua, Qiaozhi Sangaiah, Arun Kumar Guo, Tan Yu, Keping School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Robotics and Intelligent Systems Laboratory School of Engineering University of Guelph GuelphONN1G2W1 Canada Computer School Hubei University of Arts and Science Xiangyang441000 China Future Technology Research Center National Yunlin University of Science and Technology Douliu City64002 Taiwan Graduate School of Science and Engineering Hosei University Tokyo184-8584 Japan Riken Center for Advanced Intelligence Project Riken Tokyo103-0027 Japan

Edge enabled Industrial Internet of Things (IIoT) platform is of great significance to accelerate the development of smart industry. However, with the dramatic increase in real-time IIoT applications, it is a great challenge to support fast response time, low latency, and efficient bandwidth utilization. To address this issue, time sensitive network (TSN) is recently researched to realize low latency communication via deterministic scheduling. To the best of our knowledge, the combinability of multiple flows, which can significantly affect the scheduling performance, has never been systematically analyzed before. In this article, we first analyze the combinability problem. Then, a noncollision theory based deterministic scheduling (NDS) method is proposed to achieve ultralow latency communication for the time-sensitive flows. Moreover, to improve bandwidth utilization, a dynamic queue scheduling (DQS) method is presented for the best-effort flows. Experiment results demonstrate that NDS/DQS can well support deterministic ultralow latency services and guarantee efficient bandwidth utilization. © 2005-2012 IEEE.

关键词： Bandwidth

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An Isomerism Learning Model to Solve Time-Varying Problems Through Intelligent Collaboration

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IEEE/CAA Journal of Automatica Sinica 2023年第8期10卷 1772-1774页

作者： Zhihao Hao Guancheng Wang Bob Zhang Leyuan Fang Haisheng Li the Department of Computer and Information Science University of MacaoMacao 999078China the School of Data Science the Chinese University of Hong KongShenzhen 518172 Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen 518000 China Industrial Control Systems Cyber Emergency Response Team Beijing 100040China IEEE the College of Electrical and Information Engineering Hunan UniversityChangsha 410082 the Peng Cheng Laboratory Shenzhen 518000China the Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing 100048 the School of Computer Science and Engineering Beijing Technology and Business UniversityBeijing 100048China

Dear Editor,This letter deals with a solution for time-varying problems using an intelligent computational(IC)algorithm driven by a novel decentralized machine learning approach called isomerism *** order to meet the challenges of the model’s privacy and security brought by traditional centralized learning models,a private permissioned blockchain is utilized to decentralize the model in order to achieve an effective coordination,thereby ensuring the credibility of the overall model without exposing the specific parameters and solution process.

关键词： ism thereby letter

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Emotion Recognition from Audio-Visual Information based on Convolutional Neural Network

Emotion Recognition from Audio-Visual Information based on C...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Syrine Haddad Olfa Daassi Safya Belghith Laboratory of Robotics Informatics and Complex Systems (RISC Lab-LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Laboratory of Robotics Informatics and Complex Systems (RISC Lab-LR16ES07) National Engineering School of Carthage University of Carthage Carthage Tunisia

In recent years., emotion recognition becomes a heavily researched field to consider in any project related to affective computing. Due to the almost limitless applications of this new discipline., the development of emotion recognition systems has emerged as a lucrative opportunity in the corporate sector. Emotion recognition can be detected in many ways., face., speech., text., gestures., etc. This paper proposes an emotion recognition approach using multiple information sources. Our approach involves both visual and speech information and it is based on Convolutional neural networks. The experiments prove the effectiveness of the proposed approach and the importance of combining visual with audio data.

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Motion Capture and Gait Analysis of Ants with Leg Injuries

Motion Capture and Gait Analysis of Ants with Leg Injuries

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Chinese Control Conference (CCC)

作者： Ming Zeng Yuanhao Li Chang Meng Shutong Zhong Zhijing Wang Feng Zhao Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Robotics and Automation Systems School of Electrical and Information Engineering Tianjin University Tianjin

Many insects in nature, such as ants, recover quickly after sustaining leg injuries. If this capability could be imparted to multi-legged robots, it would drastically enhance their ability to resist destruction and adapt to the environment. In this paper, the Camponotus Japonicus is used as the research object, and its locomotive patterns are studied under different leg-loss conditions. To facilitate this, a high-speed acquisition system, with a dual-view (bottom view and side view) setup, is designed to analyze the ant's linear gaits. Subsequently, the pivotal gait characteristics of the injured ants are extracted by keyframe annotation technology, including the gait phase diagram, duty factor, angle before support, angle before swing, and leg swing range. Lastly, according to the obtained gait characteristics, the general and specific laws of gait adjustment in the absence of one or two legs are systematically analyzed, which provides the basis for parametric settings and strategy optimization of robotic injured gait self-adjustment.

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Online Learning in Blockchain-based Energy Trading systems 24

Online Learning in Blockchain-based Energy Trading Systems

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2024 International Symposium on Theory, Algorithmic Foundations, and Protocol Design for Mobile Networks and Mobile Computing, MobiHoc 2024

作者： Liu, Yunshu Cheung, Man Hon Huang, Jianwei Pillar of Engineering Systems and Design Singapore University of Technology and Design Singapore Singapore Department of Computer Science City University of Hong Kong Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage The Chinese University of HongKong Guangdong Shenzhen518172 China

ISBN: (纸本)9798400705212

In this paper, we consider a blockchain-based energy trading (BBET) system with the proof-of-stake (PoS) protocol. The system designer aims to minimize system cost by considering the prosumers’ strategic token allocation between blockchain staking and energy purchase for their applications. This is challenging as the system designer does not know prosumers’ private information of impatience levels towards different applications. To this end, we propose an online learning mechanism (OLM), which includes incentive mechanisms to guide both prosumers’ private information reporting and staking decisions in two phases. In the exploration phase, we design a randomized staking reward to encourage prosumers’ truthful reporting of their private information for the learning of impatience level distributions. Based on the threshold structure of the prosumers’ equilibrium staking strategies, in the exploitation phase, we propose a learning-error-based reward to minimize the system cost considering the finite-sample bias. By characterizing the optimal exploration duration, we prove that OLM achieves an asymptotic zero-regret against the complete information benchmark, with the regret bounded by O(3√lnT/T) when operating for T time slots. We implement the corresponding smart contract in Ethereum to demonstrate the feasibility of our approach. Experiment results show that our mechanism reduces regret by an average of 74% compared to the state-of-art mechanism. © 2024 Copyright held by the owner/author(s).

关键词： Blockchain

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Feature Space Augmentation and Old Class Space Preservation for Class Incremental Learning

Feature Space Augmentation and Old Class Space Preservation ...

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Chinese Control Conference (CCC)

作者： Ming Zeng Siying Li Jiazhe Liu Yuanhao Li Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Robotics and Automation Systems School of Electrical and Information Engineering Tianjin University Tianjin

The ability of continuously learning new knowledge without forgetting old ones is crucial to adapt to an ever-changing world. This scenario becomes more challenging when the previous data are not available. Current class incremental learning (CIL) approaches tend to incorporate new classes with backward compatibility, i.e., maintaining discriminability of old ones. By contrast, we focus on the extensibility and compatibility for future new classes in the early stage. Our proposed method achieves this by augmenting feature space. In detail, a large number of pseudo-new classes are generated via real example mixture and then train the initial spatially augmented model using pseudo-new classes and the base classes. Besides, considering the fact that it is impossible to maintain backward compatibility with only one prototype for each old class, the updated model also needs to preserve old class space in incremental stages. We employ a certain perturbation to the old class prototypes to effectively avoid old class space from being over-squeezed by the samples of new classes. The experiments on CIFAR-100 and ImageNet-Subset (100 classes) demonstrate that our method substantially reduces the overlap of old and new classes, outperforming state-of-the-art various baselines.

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