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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

共 5313 条记录，以下是881-890 订阅

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Smart Transducers, robotics and Artificial Intelligence in Industry 4.0 26

Smart Transducers, Robotics and Artificial Intelligence in I...

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26th international conference on Industrial Technology, ICIT 2025

作者： Da Rocha, Helbert Chen, Gang Li, En Salvado, José A. Espirito-Santo, Antonio University of Beira Interior It - Institute of Telecommunications Dept. of Electromechanical Engineering Covilhã Portugal College of Mechanical Engineering Jiaxing University Jiaxing314000 China Institute of Automation Chinese Academy of Sciences Engineering Laboratory of Industrial Intelligent Vision Beijing100190 China

ISBN: (纸本)9798331521950

this paper evaluates the possibility of implementing a production line that incorporates the strategies of Industry 4.0 into its operation system, which includes the use of the Internet of things (IoT), artificial intelligence (AI), big data, cloud computing, and robotics to develop a smart factory. these factories have a high level of connectivity and integration of systems, allowing an effective sharing of information in real-time. Topics include the function of smart transducers in real-time management and the improvement of process interoperability through the application of the IEEE 1451 standard. this is achieved through the integration of robotics with AI to enable the adaptive and precise control of production variables, thus improving the efficiency and quality of products. the paper also discusses the issues and scenarios of using these technologies where standardization plays a major role. © 2025 IEEE.

关键词： Intelligent robots

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Modeling and Hovering control of 5-DoF Tilt-Birotor Robot 16

Modeling and Hovering Control of 5-DoF Tilt-Birotor Robot

引用

16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Hu, Anyuan Zhao, Xizhen Xu, Dabo Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China

ISBN: (纸本)9781728177090

this paper explores design, modeling and hovering control of a new under-actuated 5-DoF (degree of freedom) tilt-birotor robot. Specifically, the robot has an assembled inverted pendulum to be an extra actuating input in addition to a pair of tilt wings. Hence, it has a 5-DoF actuator control input vector: two motor thrusts, two tilt angles, and one tunable pendulum angle. We show the model analysis in terms of the Newton-Euler formulation and establish a key equation between the model control inputs and the actuator inputs. Based on that, we are able to achieve the hovering control task using an inner-outer-loop design. We also illustrate the results by numerical and experimental tests.

关键词： Inverted pendulum

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Transfer Learning: An Approach on the Riemannian Manifold for Multiclass Motor Imagery EEG Brain-Computer Interface

Transfer Learning: An Approach on the Riemannian Manifold fo...

引用

international conference on Mechatronics and Machine vision in Practice (M2VIP)

作者： Xu Sun Yaqi Chu Yang Luo Xingang Zhao Department of Control Engineering Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation (SIA) Chinese Academy of Sciences (CAS) Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences (CAS) Shenyang China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Motor imagery (MI) EEG-based brain-computer interface (BCI) facilitates direct communication between the brain’s intentions and a computer. In essence, it allows a person to control external equipment by decoding EEG signals during the mental imagery of limb movements. However, due to the intricate individual variances in EEG signals, creating a general decoding model with parameters applicable to all subjects is exceptionally challenging. Consequently, a prolonged calibration process is necessary to gather labeled subject-specific data for each individual, making it less user-friendly. A potential solution to this problem is transfer learning, a methodology that transfers knowledge from related domains to a target domain. To tackle the problem, this paper proposes a novel transfer learning approach on the Riemannian manifold framework in the context of multiclass MI EEG-based BCI classification. Particularly, a user-specific frequency band selection (FBS) method with MI EEG class distinctiveness is utilized to improve the accuracy and efficiency of Riemannian space calculations, which is measured using the inter-class distance and intra-class variance on the manifold. then, the Riemannian space Alignment (RA) strategy is used to calibrate the MI EEG variances of different subjects. the comparative experiments between the proposed approach and baseline/conventional methods are conducted on a public dataset with four-class motor imagery EEG, including two-class transfer learning scenario and four-class transfer learning scenario. proposed transfer learning approach is outperformed. Compared with the baseline/conventional methods using a fixed wide frequency band, the preliminary results suggests that the proposed approach can significantly improve the transfer learning performance for the MI EEG signals from different subjects in Riemannian space.

关键词： Manifolds Mechatronics Machine vision Transfer learning Motors Particle measurements Electroencephalography Brain-computer interfaces Frequency measurement Decoding

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Model-Free Large-Scale Cloth Spreading With Mobile Manipulation: Initial Feasibility Study

Model-Free Large-Scale Cloth Spreading With Mobile Manipulat...

引用

IEEE international conference on automation Science and Engineering (CASE)

作者： Xiangyu Chu Shengzhi Wang Minjian Feng Jiaxi Zheng Yuxuan Zhao Jing Huang K. W. Samuel Au Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong SAR Multi-Scale Medical Robotics Center Hong Kong SAR School of Engineering Westlake University China Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong SAR

Cloth manipulation is common in domestic and service tasks, and most studies use fixed-base manipulators to manipulate objects whose sizes are relatively small with respect to the manipulators' workspace, such as towels, shirts, and rags. In contrast, manipulation of large-scale cloth, such as bed making and tablecloth spreading, poses additional challenges of reachability and manipulation control. To address them, this paper presents a novel framework to spread large-scale cloth, with a single-arm mobile manipulator that can solve the reachability issue, for an initial feasibility study. On the manipulation control side, without modeling highly deformable cloth, a vision-based manipulation control scheme is applied and based on an online-update Jacobian matrix mapping from selected feature points to the end-effector motion. To coordinate the control of the manipulator and mobile platform, Behavior Trees (BTs) are used because of their modularity. Finally, experiments are conducted, including validation of the model-free manipulation control for cloth spreading in different conditions and the large-scale cloth spreading framework. the experimental results demonstrate the large-scale cloth spreading task feasibility with a single-arm mobile manipulator and the model-free deformation controller.

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A Novel Method Combining Leader-Following control and Reinforcement Learning for Pursuit Evasion Games of Multi-Agent Systems 16

A Novel Method Combining Leader-Following Control and Reinfo...

引用

16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Zhu, Zhe-Yang Liu, Cheng-Lin Jiangnan Univ Inst Automat Key Lab Adv Proc Control Light Ind Minist Educ Wuxi 214122 Jiangsu Peoples R China

ISBN: (纸本)9781728177090

In the existing literature, many methods have been utilized in solving the pursuit evasion game. However, most of these methods have one thing in common: all pursuers have to know the evader's position. this paper presents a method combining the multi-agent leader-following control and reinforcement learning, which aims at addressing a pursuit evasion game only when partial pursuers have the knowledge of the position of the evader who moves randomly in two-dimensional grid space. through decomposing the team task, pursuers realize the encirclement of the evader. Simulation experiment validates the effectiveness of our method.

关键词： Reinforcement learning

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Robustness of neural-network-based nonlinear iterative learning control 16

Robustness of neural-network-based nonlinear iterative learn...

引用

16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Patan, Krzysztof Patan, Maciej Univ Zielona Gora Inst Control & Computat Engn Zielona Gora Poland

ISBN: (纸本)9781728177090

the purpose of this work is to develop a robust iterative learning control for nonlinear systems based on neural networks. In order to introduce the robustness to the control scheme the problem of accurate estimation of uncertainties associated with the black-box type model is concerned. An uncertainty of the system is derived in terms of the variance of the model output prediction using a concept of Fisher information matrix well-known in the optimum experimental design theory. Once the bounds of the system response are estimated, they can be directly applied during training of the learning controller by a rigorous definition of the penalty cost function. then, a neural controller is suitably adopted to the effective design of iterative learning control for nonlinear systems. the proposed approach is experimentally verified on the example of a magnetic levitation system.

关键词： Nonlinear systems

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Event-triggered Optimal Adaptive control for Robot Trajectory Tracking 16

Event-triggered Optimal Adaptive Control for Robot Trajector...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Chen, Shuo Fan, Yuan Guo, Yuchao Zhu, Mingjian Anhui Univ Sch Elect Engn & Automat Anhui Engn Lab Human Robot Integrat Syst & Intell Hefei 230601 Peoples R China

ISBN: (纸本)9781728177090

In this paper, an event-triggered optimal adaptive control is developed for robot trajectory tracking system. Due to the nonlinearity of Hamilton function, we apply the actor-critic neural network structure to solve it. Firstly, the critic network is used to estimate the cost function and the actor network is used to estimate the optimal event-triggered control law. Due to the advantage of event-triggered method, the weight update rate of actor-critic neural network only occurs when the triggering condition is violated, which save a lot of communication resources. then, the event-triggered robot trajectory tracking system is ultimately bounded by Lyapunov stability analysis. Finally, the simulation show that the proposed method is effective.

关键词： Cost functions

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Consensus-based formation control and obstacle avoidance for nonholonomic multi-robot system 16

Consensus-based formation control and obstacle avoidance for...

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16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Koung, Daravuth Fantoni, Isabelle Kermorgant, Olivier Belouaer, Lamia Ecole Cent Nantes ECN Lab Sci Numer Nantes LS2N Nantes France E COBOT Carquefou France LS2N Nantes France CNRS Paris France ECN Nantes France

ISBN: (纸本)9781728177090

Managing multiple robots into a formation can be beneficial, especially in logistics sectors where multiple robots can work together to transport larger loads. this paper presents a consensus control law for formation with navigation and obstacle avoidance of multiple wheeled mobile robots. the formation control is based on adapting a consensus algorithm from flocking, and we propose an obstacle avoidance methodology that ensures the formation while navigating around obstacles. Simulations of the control law using four wheeled mobile robots as well as experiments using actual industrial robots are shown in order to validate the theory.

关键词： Industrial robots

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ROS-driven Disassembly Planning Framework incorporating Screw Detection

ROS-driven Disassembly Planning Framework incorporating Scre...

引用

international conference on Electrical Engineering, Computing Science and Automatic control (CCE)

作者： Timon Hoebert David Neubauer Munir Merdan Wilfried Lepuschitz Stefan thalhammer Markus Vincze Practical Robotics Institute Austria Vienna Austria TU Wien Vienna Austria

the automation of electronic waste disassembly processes is very challenging due to the diversity and conditions of the products, but also due to the heterogeneous disassembly environments based on different hardware and software components. All these resources and the subjacent information flows should be coordinated and integrated to ensure an effective disassembly process. In this paper, we present a developed architecture for closed-loop planning and controlling dynamic disassembly processes of robots. We extended the infrastructure provided by the Robot Operating System (ROS) to integrate the components of the robotic system with its vision system and the software components for inference, replanning, and knowledge transfer. the architecture was implemented for a real use case of antenna amplifier disassembly. the implemented framework is generalizable for other purposes implementing four automatic configuration mechanisms to support domain-specific tailoring: code generation, object serialization, object mapping, and object-triple mapping.

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control of A 2R Planar Horizontal Underactuated Manipulator 16

Control of A 2R Planar Horizontal Underactuated Manipulator

引用

16th IEEE international conference on control, automation, robotics and vision (ICARCV)

作者： Chen, Tan Goodwine, Bill Univ Notre Dame Dept Aerosp & Mech Engn Notre Dame IN 46556 USA

ISBN: (纸本)9781728177090

this paper presents a new method for trajectory planning and control of a 2R planar horizontal robotic arm with only the first joint actuated, which is known to be controllable but not asymptotically stabilizable with smooth feedback controllers. the main idea is to first determine an admissible trajectory that satisfies both the constraints of system dynamics and boundary value conditions, and then design a feedforward controller for the system. For complex and highly nonlinear systems, an optimization method is applied to determine the trajectory. Moreover, a time-scaling method can be used to control the magnitude of the inputs and also make it easier to find a solution in the optimization procedure. To further improve the robustness of the controller, a feedback controller is designed along the planned trajectory. Position to position control of the robot is simulated to demonstrate the effectiveness of the method.

关键词： Trajectories

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