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arXiv 2024年

作者： Zhu, Yongjian Cheng, Hao Zhang, Feitian State Key Laboratory of Turbulence and Complex Systems the Robotics and Control Laboratory Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing100871 China

Miniature robotic blimps, as one type of lighter-than-air aerial vehicles, have attracted increasing attention in the science and engineering community for their enhanced safety, extended endurance, and quieter operation compared to quadrotors. Accurately modeling the dynamics of these robotic blimps poses a significant challenge due to the complex aerodynamics stemming from their large lifting bodies. Traditional first-principle models have difficulty obtaining accurate aerodynamic parameters and often overlook high-order nonlinearities, thus coming to their limit in modeling the motion dynamics of miniature robotic blimps. To tackle this challenge, this letter proposes the Auto-tuning Blimp-oriented Neural Ordinary Differential Equation method (ABNODE), a data-driven approach that integrates first-principle and neural network modeling. Spiraling motion experiments of robotic blimps are conducted, comparing the ABNODE with first-principle and other data-driven benchmark models, the results of which demonstrate the effectiveness of the proposed method. Copyright © 2024, The Authors. All rights reserved.

关键词： Airships

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NA-CPG: A robust and stable rhythm generator for robot motion control

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Biomimetic Intelligence & Robotics 2022年第4期2卷 30-38页

作者： Ru Tong Changlin Qiu Zhengxing Wu Jian Wang Min Tan Junzhi Yu State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Department of Advanced Manufacturing and Robotics College of EngineeringPeking UniversityBeijing 100871China

Central pattern generators(CPGs)have been widely applied in robot motion control for the spontaneous output of coherent periodic ***,the underlying CPG network exhibits good convergence performance only within a certain range of parameter spaces,and the coupling of oscillators affects the network output accuracy in complex topological ***,CPGs may diverge when parameters change drastically,and the divergence is irreversible,which is catastrophic for the control of robot ***,normalized asymmetric CPGs(NA-CPGs)that normalize the amplitude parameters of Hopf-based CPGs and add a constraint function and a frequency regulation mechanism are ***-CPGs can realize parameter decoupling,precise amplitude output,and stable and rapid convergence,as well as asymmetric output ***,it can effectively cope with large parameter changes to avoid network oscillations and *** optimize the parameters of the NA-CPG model,a reinforcement-learning-based online optimization method is further ***,a biomimetic robotic fish is illustrated to realize the whole optimization *** demonstrated that the designed NA-CPGs exhibit stable,secure,and accurate network outputs,and the proposed optimization method effectively improves the swimming speed and reduces the lateral swing of the multijoint robotic fish by 6.7%and 41.7%,*** proposed approach provides a significant improvement in CPG research and can be widely employed in the field of robot motion control.

关键词： Normalized asymmetric CPGs Rhythm generator Motion control Robotic fish

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Probably approximately correct stability of allocations in uncertain coalitional games with private sampling

arXiv

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arXiv 2023年

作者： Pantazis, George Fele, Filiberto Fabiani, Filippo Grammatico, Sergio Margellos, Kostas Delft Center for Systems and Control TU Delft Netherlands Department of Systems Engineering and Automation University of Seville Spain IMT School for Advanced Studies Lucca Italy Department of Engineering Science University of Oxford United Kingdom

We study coalitional games with exogenous uncertainty in the coalition value, in which each agent is allowed to have private samples of the uncertainty. As a consequence, the agents may have a different perception of stability of the grand coalition. In this context, we propose a novel methodology to study the out-of-sample coalitional rationality of allocations in the set of stable allocations (i.e., the core). Our analysis builds on the framework of probably approximately correct learning. Initially, we state a priori and a posteriori guarantees for the entire core. Furthermore, we provide a distributed algorithm to compute a compression set that determines the generalization properties of the a posteriori statements. We then refine our probabilistic robustness bounds by specialising the analysis to a single payoff allocation, taking, also in this case, both a priori and a posteriori approaches. Finally, we consider a relaxed ζ -core to include nearby allocations and also address the case of empty core. For this case, probabilistic statements are given on the eventual stability of allocations in the ζ -core. © 2023, CC BY.

关键词： Sampling

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Fabric Defect Detection System using YOLO

Fabric Defect Detection System using YOLO

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IIAI International Conference on advanced Applied Informatics (IIAIAAI)

作者： Masashi Kawaguchi Yuta Morimitsu Ryusei Watanabe Chihiro Ikuta Naohiro Ishii Masayoshi Umeno Department of Electrical & Electronic Engineering Suzuka College National Institute of Technology Suzuka Japan Information Systems Architecture Course Advanced Institute of Industrial Technology Tokyo Japan Department of Electronic Engineering Chubu University Kasugai Japan

In textile processing companies, after purchasing fabric, dyeing, and other treatments, such as surface finishing are carried out before shipping. Defect detection technology is one of the most important issues in this field. Because if we ship a defective fabric without noticing the defect, it will cause damage to the delivery destination. Previously, defect detection of fabric has been performed using methods such as CNN. However, conventional methods such as general multilayer neural networks require two separate processing stages: object detection and identification. Therefore, there is a problem that sufficient real-time performance cannot be guaranteed in the inspection process on an actual manufacturing line. Therefore, in this study, we construct a fabric defect detection system using YOLO which is capable of real-time object detection. In the future, we aim to realize the automation of textile defect detection systems by the construction of an image recognition system using machine learning. It is expected that this will result from reduced detection errors by the human eye, improve detection accuracy, and reduce labor costs.

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Real-Time Face-Tracking control of a 4 DOF Manipulator Using Deep Learning Detection

Real-Time Face-Tracking Control of a 4 DOF Manipulator Using...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Ali Asadi Amirreza Hassan Nejad S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

This paper has presented the design, simulation, and real-time implementation of a four-degree-of-freedom (4-DOF) robotic system for face detection and tracking. The system has combined kinematic control with deep learning. It has used the Xception network for high-accuracy facial detection. A Fuzzy PID-MIJ control algorithm has optimized the trajectory planning. This has ensured smooth and precise target tracking. Both simulations and physical experiments have validated the system's real-time performance. The results have shown that the system can reliably track faces with minimal error. This approach has demonstrated significant potential for dynamic, human-interactive applications.

关键词： Deep learning Target tracking Trajectory planning Target recognition Face recognition Kinematics Real-time systems Robustness Planning Face detection

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A Novel Bio-Intelligent Real-Time Redundancy Resolution Method for Cable-Driven Parallel Manipulators

A Novel Bio-Intelligent Real-Time Redundancy Resolution Meth...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： S. Ali A. Moosavian SeyedSina Seyedi HasanAbadi Arman Gholibeikian Department of Mechanical Engineering K. N. Toosi University of Technology Advanced Robotic and Automated Systems (ARAS) Center of Excellence in Robotics and Control Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Cable-driven parallel manipulators (CDPMs) are widely used where there is a need for high speeds and high accelerations, thanks to the low inertia and low cost of their links. However, cables can only exert tensional force. Due to this inherent property of the cables, CDPMs are required to be designed with at least one degree of redundancy in their joint space, to be able to generate an acceptable range of wrench within the workspace. Meanwhile, redundancy in joint space leads a problem called redundancy resolution, which is basically a force distribution from workspace to joint space. In this research, inspired by a simple mathematical model of the limbic system of the brain, a novel real-time redundancy resolution method is proposed. The presented method utilizes the kinematic information of the end-effector to find the solution to the redundancy resolution problem. The results show that this Brain Emotional Learning (BEL) method has a better performance in comparison with some common numerical optimization methods solved with a nonlinear programming solver like fmincon function in MATLAB, in terms of computational cost, continuity of the profile of the cables forces, and robustness.

关键词： Power cables Redundancy Force Optimization methods Kinematics Brain modeling Mathematical models Real-time systems Robustness Trajectory

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control of Continuum Manipulators with Shape Constraints via Deep Reinforcement Learning

Control of Continuum Manipulators with Shape Constraints via...

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RSI/ISM International Conference on Robotics and Mechatronics (ICROM)

作者： Nima Maghooli Omid Mahdizadeh Mohammad Bajelani S. Ali A. Moosavian Department of Mechanical Engineering Center of Excellence in Robotics and Control Advanced Robotics and Automated Systems (ARAS) K. N. Toosi University of Technology Tehran Iran

ISBN: (数字)9798331529734

ISBN: (纸本)9798331529741

Continuum robots, while versatile for handling complex tasks, present significant challenges in control system design. This paper introduces a novel framework that integrates position and orientation control laws to address shape constraints effectively. Specifically, a Deep Reinforcement Learning (DRL) strategy is proposed to facilitate trajectory tracking within the desired orientation. To ensure safety, the framework directly controls the end-effector's position and orientation in the workspace, avoiding unsafe zones. A centralized control law, the Modified Transpose Jacobian (MTJ), is employed to resolve the robot's redundancy without relying on inverse kinematics. The proposed approach is validated through simulations on a Tendon-Driven Continuum Robot (TDCR), demonstrating superior performance compared to similar learning-based controllers.

关键词： Meters Mechatronics Shape Trajectory tracking Termination of employment Position control Kinematics Deep reinforcement learning Safety Continuum robots

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A discrete moth-flame optimization algorithm for multiple automated guided vehicles scheduling problem in a matrix manufacturing workshop

A discrete moth-flame optimization algorithm for multiple au...

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作者： Zeng, Junhai Xie, Wei Pan, Mi School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China Key Laboratory of Autonomous Systems and Networked Control Ministry of Education South China University of Technology Guangdong Guangzhou510641 China Guangdong Provincial Key Laboratory of Technique and Equipment for Macromolecular Advanced Manufacturing South China University of Technology Guangdong Guangzhou510641 China Department of Civil and Environmental Engineering University of Macau 999078 China

The growing need for customized and varied production has highlighted the significance of smart and automated factories in the manufacturing sector. In this particular context, the scheduling of multiple Automated Guided Vehicles (AGVs) plays a pivotal role in enhancing the efficiency of operations within an intelligent manufacturing shop. This study centers on the material handling process within a matrix manufacturing shop with the objective of identifying the most cost-effective transportation routes for materials. To accomplish the specified objective, this research aims to identify an optimal solution for reducing transportation costs. In particular, this study formulates a mixed-integer linear programming model and introduces a novel discrete variant of the moth-flame optimization (MFO) algorithm, named DMFO, to address the scheduling problem. The DMFO algorithm incorporates several significant enhancements. Firstly, a population initialization method is proposed, which combines a nearest-neighbor-based adaptive heuristic and a random sorting technique to ensure the formation of a well-structured population. Secondly, the flame generation mechanism and spiral flight search processes within the MFO have been redefined to achieve a more optimal balance between exploration and exploitation. A neighborhood search mechanism is subsequently devised, employing the concept of neighborhood relevance to accelerate the convergence process. Additionally, a heuristic approach is introduced to reduce the computational cost. Moreover, a population regeneration mechanism is proposed to avoid the algorithm falling into a local optimum. To validate the effectiveness of the DMFO, a comparative analysis is conducted using a dataset of 110 real-world factory instances. In this analysis, eight well-established optimization algorithms are employed. The simulation results consistently demonstrate that the relative percentage deviation (RPD) value of the DMFO tends to approach 0% more

关键词： Materials handling

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Disturbance Rejection for Pedaling Rehabilitation Robot Based on Integration of Equivalent-Input-Disturbance and Repetitive control Methods 32

Disturbance Rejection for Pedaling Rehabilitation Robot Base...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Zhou, Yujian She, Jinhua Wang, Feng Iwasaki, Makoto School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Tokyo University of Technology School of Engineering Tokyo Hachioji192-0982 Japan Nagoya Institute of Technology Department of Electrical and Mechanical Engineering Nagoya466-8555 Japan

ISBN: (纸本)9798350399714

This study integrated an improved equivalent-input-disturbance (EID) and a repetitive control methods to ensure reference tracking and enhance disturbance-rejection performance for a pedaling rehabilitation robot. A repetitive controller ensures steady-state tracking of a periodic reference input. An EID estimator with a state observer estimates and compensates for the effect of disturbances generated by patients with lower-limb impairment. The stability condition of the closed-loop system is analyzed based on the Lyapunov stability theory. Simulation results show the effectiveness of this method. © 2023 IEEE.

关键词： Disturbance rejection

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Online controller Tuning Method Using Fictitious Reference Iterative Tuning Based on Recursive Least-Squares Method for Quadrotor Flight control

Online Controller Tuning Method Using Fictitious Reference I...

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IEEE International Conference on Industrial engineering and engineering Management

作者： Ayumu Sato Ryo Tanaka Advanced Engineering School National Institute of Technology Kurume College Fukuoka Japan Department of Control and Information Systems Engineering National Institute of Technology Kurume College Fukuoka Japan

This paper proposes an online controller tuning method using fictitious reference iterative tuning (FRIT) design method based on recursive least-squares (RLS) method for quadrotor flight control. FRIT is a method of directly adjusting controller parameters such as PID gains using input/output data without using a mathematical model of the controlled object. This paper compares the performance of conventional discrete PID control with that of discrete PID control implementing RLS-FRIT by simulation. The results show that the proposed RLS-FRIT has better control performance than the conventional method. The robustness against disturbances was also found to be superior to that of the conventional method.

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