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American control Conference (ACC)

作者： Qi Mao Dan Ma Yanling Ding Hui Peng Jie Chen Department of Electrical Engineering City University of Hong Kong Hong Kong China College of Information Science and Engineering State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China School of Automation Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China

This paper concerns robust consensus problems for continuous-time first-order multi-agent systems with respect to uncertain gain and phase of the agent’s frequency response varying within certain ranges. We consider dynamic output feedback control protocol in the form of proportional-integral (PI) control. The agents can in general be nonminimum phase and unstable systems, which are interconnected by an undirected graph network. We seek to determine the largest ranges of gain and phase variations, referred to as the gain consensus margin (GCM) and the phase consensus margin (PCM), respectively, so that consensus can be achieved robustly within these ranges. Our main results consist of explicit analytical expressions of the GCM and PCM, which demonstrate how the agent’s unstable pole and nonminimum phase zero, as well as the network connectivity may fundamentally confine the gain and phase variation ranges so that consensus can or cannot be maintained.

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Autonomous Navigation and Obstacle Avoidance for Mobile Robots in a Tax Environment

Autonomous Navigation and Obstacle Avoidance for Mobile Robo...

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IEEE International Conference on Cyber Technology in automation, control, and intelligent systems

作者： Lianyi Yu Zhengxing Wu Yang Jiang Min Tan Junzhi Yu Laboratory of Cognitive and Decision Intelligence for Complex System Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Northeastern University Shenyang China Department of Mechanics and Engineering Science State Key Laboratory for Turbulence and Complex System BIC-ESAT College of Engineering Peking University Beijing China

This paper focuses on the navigation and obstacle avoidance problems of intelligent mobile robots in a tax environment. Through combining the improved jump point search (JPS) and dynamic window approach (DWA) algorithms, a fusion path planning method is proposed for the mobile robots in tax services. Specially, a bi-directional JPS algorithm with an adaptive heuristic function is provided to decrease the search complexity and improve search flexibility in the global path planning. Meanwhile, considering the features of the tax environment, several evaluation subfunctions are particularly designed to leverage the global information and optimize the local path planning. Finally, various simulations and experiments in a Turtlebot3 robot validate the feasibility and effectiveness of the proposed method. The obtained results provide valuable guidance for the autonomous navigation research of the tax service robots.

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Human behavior model and its application on performance management in a chemical plant workshop

Human behavior model and its application on performance mana...

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2012 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2012

作者： Han, Feng Liu, Xiwei Shang, Xiuqin Cheng, Changjian Li, Bo Fan, Dong Olefine Plant SINOPEC Qilu Petrochemical Co. Ltd. Zibo China School of Chemical Engineering Beijing University of Chemical Technology Beijing China State Key Laboratory of Management and Control for Complex Systems Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Beijing China Dongguan Research Institute of CASIA Cloud Computing Center Chinese Academy of Sciences Songshan Lake Dongguan China

ISBN: (纸本)9781467324007

To investigate the influences of performance management rules on human behavior in a chemical plant workshop, we construct an artificial workshop. It mainly contains human behavior model, workshop environment model including the definition of work position performance management rules and production events, and their interaction rules. The human behavior model is built based on DISC (Dominance, Influence, Steadiness, Compliance) behavior model and the Agent technology. A platform for workshop human behavior computing is finally created for computational experiments on performance management and decision-making. The platform can be used as a support for refined management in the chemical plant. © 2012 IEEE.

关键词： Chemical plants

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Forget less,count better:a domain-incremental self-distillation learning benchmark for lifelong crowd counting

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Frontiers of Information Technology & Electronic Engineering 2023年第2期24卷 187-202页

作者： Jiaqi GAO Jingqi LI Hongming SHAN Yanyun QU James ZWANG Fei-Yue WANG Junping ZHANG Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan UniversityShanghai 200433China Institute of Science and Technology for Brain-inspired Intelligence Fudan UniversityShanghai 200433China Shanghai Center for Brain Science and Brain-inspired Technology Shanghai 201210China School of Information Science and Technology Xiamen UniversityXiamen 361005China College of Information Sciences and Technology the Pennsylvania State UniversityUniversity ParkPA 16802USA State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Crowd counting has important applications in public safety and pandemic control.A robust and practical crowd counting system has to be capable of continuously learning with the newly incoming domain data in real-world scenarios instead of fitting one domain ***-the-shelf methods have some drawbacks when handling multiple domains:(1)the models will achieve limited performance(even drop dramatically)among old domains after training images from new domains due to the discrepancies in intrinsic data distributions from various domains,which is called catastrophic forgetting;(2)the well-trained model in a specific domain achieves imperfect performance among other unseen domains because of domain shift;(3)it leads to linearly increasing storage overhead,either mixing all the data for training or simply training dozens of separate models for different domains when new ones are *** overcome these issues,we investigate a new crowd counting task in incremental domain training setting called lifelong crowd *** goal is to alleviate catastrophic forgetting and improve the generalization ability using a single model updated by the incremental ***,we propose a self-distillation learning framework as a benchmark(forget less,count better,or FLCB)for lifelong crowd counting,which helps the model leverage previous meaningful knowledge in a sustainable manner for better crowd counting to mitigate the forgetting when new data arrive.A new quantitative metric,normalized Backward Transfer(nBwT),is developed to evaluate the forgetting degree of the model in the lifelong learning *** experimental results demonstrate the superiority of our proposed benchmark in achieving a low catastrophic forgetting degree and strong generalization ability.

关键词： Crowd counting Knowledge distillation Lifelong learning

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Biomimetic inspiration for PKM torso design in humanoid robots

Biomimetic inspiration for PKM torso design in humanoid robo...

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IEEE International Conference on Mechatronics and automation

作者： Hulin Huang Ceccarelli Marco Weimin Zhang Qiang Huang Zhangguo Yu Xuechao Chen Ministry of Education Key Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Beijing China Laboratory of Robotics and Mechatronics University of Cassino and South Latium Cassino Italy Ministry of Education International Joint Research Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China

ISBN: (纸本)9781479970995

A new torso design of humanoid robot is proposed as a 3-DOF PKM (Parallel Kinematic Manipulator) structure by using results of a biomimetic analysis of torso in humans and animals. Main characteristics of torso in nature are presented from which design requirements are outlined for a solution in humanoid robot. The PKM torso is consist of two platforms and four UPU mechanisms. The usefulness of such a biomimetic inspiration is proved by a numerical simulation of the proposed PKM torso in its basic movements.

关键词： Torso Humanoid robots Joints Thorax Horses Payloads

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FTEKFNet: Hybridizing Physical and Data-driven Estimation Algorithms for Vehicle state

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on intelligent Vehicles 2024年 1-11页

作者： Wang, Yan Hu, Fengqing Tian, Cheng Li, Panshuo Huang, Hailong Yin, Guodong Huang, Chao Department of Industrial and System Engineering The Hong Kong Polytechnical University Hong Kong Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnical University Hong Kong School of Automation and the Guangdong Provincial Key Laboratory of Intelligent Decision and Cooperative Control Guangdong University of Technology Guangzhou China School of Mechanical Engineering Southeast University Nanjing China

Sideslip angle and vehicle velocity are crucial for both traditional and autonomous vehicles. They play essential roles in chassis stability control, as well as in tasks such as path planning and tracking control. However, these states cannot be directly measured by onboard sensors, therefore various vehicle state estimation algorithms have been developed. Most of these algorithms assume that the noise characteristics are known, ignoring the impact of missing measurement data, and cannot simultaneously handle the effects of colored noise and white noise. To address these issues, we propose a fault-tolerant extended Kalman filter network (FTEKFNet), which integrates both physics-based and data-driven methods for vehicle state estimation. Based on the Fault Tolerant Extended Kalman Filter (FTEKF) iterative framework, a pre-trained artificial neural network is utilized to directly predict the Kalman gain, and it is combined with FTEKF to form FTEKFNet. Experimental results under different conditions demonstrate that FTEKFNet can simultaneously deal with unknown noise and data loss problems and has good adaptability to color noise. The estimation performance of the proposed algorithm is better than the traditional FTEKF and EKF methods. IEEE

关键词： Extended Kalman filters

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Path Planning of Mobile Robot Based on an Improved Genetic Algorithm

Path Planning of Mobile Robot Based on an Improved Genetic A...

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Digital Twins and Parallel Intelligence (DTPI), IEEE International Conference on

作者： Zenghua Chen Gang Xiong Sheng Liu Zhen Shen Yue Li State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Automobile Chang'an University Xi'an China

ISBN: (数字)9781665492270

ISBN: (纸本)9781665492287

In order to solve the problem of premature convergence of the basic genetic algorithm when planning the robot running path, the basic genetic algorithm is improved and optimized. Different population initialization methods are used to initialize multiple populations randomly, so as to improve the diversity of populations; Improve the adaptive strategy and elite strategy of crossover and mutation operators to improve the convergence speed of the algorithm; Add the path tortuosity as the planning index in the fitness function to make the planned path smoother, and add constraints to the model to avoid obstacles; Finally, through the transformation of the coding paradigm of the above improved genetic algorithm, it can run on Flink distributed cluster to obtain faster solution speed, so as to meet the efficiency requirements of path planning in large-scale robot cluster system. The optimized algorithm is compared with the basic genetic algorithm. The simulation results show that the improved algorithm is efficient in robot path planning.

关键词： Sociology Clustering algorithms Path planning Planning Trajectory Statistics Collision avoidance

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Distributed Cooperative decision-Making of Cross-Domain Swarm

Distributed Cooperative Decision-Making of Cross-Domain Swar...

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Chinese automation Congress (CAC)

作者： Xingyu Liu Cheng Yuwen Wangcheng Zhang Mingyang Sun Ding Jian Ziyang Zhen State Key Laboratory of Mechanics and Control for Aerospace Structures College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing China CASIC Research Institute of Intelligent Decision Engineering Wuhan China UAV System Company Limited Aviation Industry Corporation of China Chengdu China

This paper investigates a cooperative decision-making problem of the cross-domain swarm system involving unmanned surface vehicles (USVs) and unmanned aerial vehicles (UAVs). Different from the distributed optimization of multi-agent systems, the cooperative decision-making problem considers the payoff functions that depend on the states of all agents. Each agent can only adjust its own state. To address the cooperative decision-making problem, a two-time scale system is constructed, where the fast model includes an average consensus for gradient estimation of the global function and the slow model incorporates a feedback control of all USVs and UAVs. By the Lyapunov analysis, the optimal solution is globally exponentially stable. Finally, a cooperative encirclement task is utilized to verify the effectiveness of the proposed algorithm.

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Semi-autonomous greenhouse environment control by combining expert knowledge and machine learning

Semi-autonomous greenhouse environment control by combining ...

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Chinese automation Congress (CAC)

作者： Mengzhen Kang Yuchen Weng Haitong Pang Liangde Li Xing-Rong Fan Jing Hua Fangle Chang Xiujuan Wang Longhua Ma The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Ningbo Institute of Technology Zhejiang University Ningbo China The School of Computer Science and Information Engineering Chongqing Technology and Business University Chongqing China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China Qingdao AgriTech Co. Ltd Qingdao Qingdao China The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing Engineering Research Center of Intelligent Systems and Technology Beijing China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

The concept of "autonomous greenhouse" hints the automatic setting of control target, and giving orders on facilities to reach these targets (set point). Information and communication technologies are widely used, but how to integrate common sense or knowledge in decision-making is still challenging. Semi-autonomous greenhouse control is to set rules with joint advantages of experienced grower and powerful machinery, with the support of knowledge graph and semantic analysis. The contribution of each side is dependent on the intelligence level of computers, the availability of data, etc. In this work, the hybrid system between human and machinery is shown with the temperature control in greenhouse. The target is set mainly by expert knowledge, while the temperature controlled by vent opening is decided by an algorithm. Result shows that the target is better reached compared to that of the experience-based approach. This example shows the feasibility of human-machine hybrid intelligence system for greenhouse environment control. Although this approach is tested for temperature control, it can be extended to control multiple factors simultaneously (temperature, light intensity, humidity, etc.) by driving multiple facilities (heating, lightening, venting, etc.).

关键词： Green products Temperature measurement Monitoring Temperature sensors Vents Agriculture Temperature distribution

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Distributed stochastic gradient tracking algorithm with variance reduction for non-convex optimization

arXiv

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arXiv 2021年

作者： Jiang, Xia Zeng, Xianlin Sun, Jian Chen, Jie Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China Ministry of Education Beijing100081 China School of Electronic and Information Engineering Tongji University Shanghai200082 China

This paper proposes a distributed stochastic algorithm with variance reduction for general smooth non-convex finite-sum optimization, which has wide applications in signal processing and machine learning communities. In distributed setting, large number of samples are allocated to multiple agents in the network. Each agent computes local stochastic gradient and communicates with its neighbors to seek for the global optimum. In this paper, we develop a modified variance reduction technique to deal with the variance introduced by stochastic gradients. Combining gradient tracking and variance reduction techniques, this paper proposes a distributed stochastic algorithm, GT-VR, to solve large-scale non-convex finite-sum optimization over multi-agent networks. A complete and rigorous proof shows that the GT-VR algorithm converges to first-order stationary points with O(k1) convergence rate. In addition, we provide the complexity analysis of the proposed algorithm. Compared with some existing first-order methods, the proposed algorithm has a lower O(PMΕ−1) gradient complexity under some mild condition. By comparing state-of-the-art algorithms and GT-VR in experimental simulations, we verify the efficiency of the proposed algorithm. © 2021, CC BY.

关键词： Stochastic systems

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