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IEEE International Conference on Cyborg and Bionic systems (CBS)

作者： Junhui Gao Rongjie Du Xiaolong Quan Guanglu Jia Qiang Huang Toshio Fukuda Qing Shi School of Mechatronical Engineering Intelligent Robotics InstituteBeijing Institute of Technology Beijing China Ministry of Education >Laboratory of Biomimetic Robots and Systems(Beijing Institute of Technology) Beijing China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing Institute of Technology Beijing China Ministry of Education Laboratory of Biomimetic Robots and Systems (Beijing Institute of Technology) Beijing China

Large quadruped robots have shown high environmental adaptability and promising applications. However, due to the difficulty of integrating multiple sensors or highperformance CPU in such a limited dimension, which strongly hindered the perception and calculation ability, quadruped robots in small scale haven’t shown equally performance. To overcome this limitation, we integrated the inertial measurement unit (IMU) into the small-sized robotic rat SQuRo, and proposed simplified control strategies to enhance its robustness. The improvements includes three parts: 1) A posture control strategy is proposed and verified by posture adjustment in a slope test; 2) Straight line tracking was achieved by detecting and controlling the yaw angle of SQuRo; 3) The autonomous fall recovery was achieved by detecting the fall and executed a predefined fall recovery strategy.

关键词： Measurement units Trajectory tracking Kinematics Inertial navigation Rats Robot sensing systems Robustness

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Linear Kinematics for General Constant Curvature and Torsion Manipulators

Linear Kinematics for General Constant Curvature and Torsion...

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IEEE International Conference on Soft robotics (RoboSoft)

作者： Bill Fan Farhan Rozaidi Capprin Bass Gina Olson Melinda Malley Ross L Hatton Olin College of Engineering Needham MA USA Collaborative Robotics and Intelligent Systems (CoRIS) Institute Oregon State University Corvallis OR USA University of Massachusetts Amherst Amherst MA

We present a novel general model that unifies the kinematics of constant curvature and constant twist continuum manipulators. Combining this kinematics with energy-based physics, we derive a linear mapping from actuator configuration to manipulator deformation that is analogous to traditional robot forward kinematics. Our model generalizes across manipulators with different sizes, types of bending, and types of actuators, without the need for parameter re-fitting. The combination of generality and linearity makes the model useful for control and planning algorithms. Finally, our model is shown to be accurate through experimental validation on manipulators with pneumatic artificial muscles.

关键词： Actuators Loading Kinematics Soft robotics Pneumatic systems Planning Manipulator dynamics

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Approaching Metaheuristic Deep Learning Combos for Automated Data Mining

arXiv

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arXiv 2024年

作者： Assunção, Gustavo Menezes, Paulo Institute of Systems and Robotics University of Coimbra R. Silvio Lima Coimbra3030-194 Portugal Department of Electrical and Computer Engineering University of Coimbra R. Silvio Lima Coimbra3030-194 Portugal

Lack of data on which to perform experimentation is a recurring issue in many areas of research, particularly in machine learning. The inability of most automated data mining techniques to be generalized to all types of data is inherently related with their dependency on those types which deems them ineffective against anything slightly different. Meta-heuristics are algorithms which attempt to optimize some solution independently of the type of data used, whilst classifiers or neural networks focus on feature extrapolation and dimensionality reduction to fit some model onto data arranged in a particular way. These two algorithmic fields encompass a group of characteristics which when combined are seemingly capable of achieving data mining regardless of how it is arranged. To this end, this work proposes a means of combining meta-heuristic methods with conventional classifiers and neural networks in order to perform automated data mining. Experiments on the MNIST dataset for handwritten digit recognition were performed and it was empirically observed that using a ground truth labeled dataset’s validation accuracy is inadequate for correcting labels of other previously unseen data instances. © 2024, CC BY.

关键词： Adversarial machine learning

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Modeling and Planar Trajectory Tracking Control of an MJ-AUV

Modeling and Planar Trajectory Tracking Control of an MJ-AUV

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Chinese Control Conference (CCC)

作者： Zakarya Omar Ke-Xian Liu Chao Ren Qing-Hao Meng Tianjin Key Laboratory of Intelligent Unmanned Swarm Technology and System Institute of Robotics and Autonomous Systems School of Electrical and Information Engineering Tianjin University Tianjin China Faculty of Engineering Sana'a University Sana'a Yemen

A Multi-Joint Autonomous Underwater Vehicle (MJ-AUV) is designed and equipped with multiple movable segments or joints that allow for greater maneuverability and flexibility in the water. These joints allow the MJ-AUV to change its shape, orientation, and direction of movement, so it can access tight spaces or navigate in complex underwater environments, making it useful for a variety of tasks in the deep sea, such as surveillance, exploration, and maintenance. However, the multiple joint structure of the MJ-AUV complicates the dynamics of the system. Furthermore, the joints' rotation causes misalignment of the cabins, causing deviations in the desired forces and torques. To address these issues, this study focuses on the 2-dimensional (2D) modeling as well as the trajectory tracking controller of the MJ-AUV. In this setting, the proposed controller utilizes a hierarchically structured robust nonlinear back-stepping and sliding-mode approach. Hence, this controller is simulated under various uncertainties and disturbances and the results show that it presents a global finite-time convergence to a bounded neighborhood of the origin for both position and velocity tracking errors, demonstrating its effectiveness and robustness.

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Decentralized Reinforcement Learning for Multiple Robotic Fish in Cooperative Pursuit Task

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IEEE Transactions on Cognitive and Developmental systems 2025年

作者： Feng, Yukai Wu, Zhengxing Wang, Jian Li, Sijie Huang, Yupei Yu, Junzhi Tan, Min Institute of Automation Chinese Academy of Sciences Key Laboratory of Cognition and Decision Intelligence for Complex Systems Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China College of Engineering Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics Beijing100871 China

The control of multi-robot systems, particularly in the pursuit-evasion (PE) with multiple robots, has gained significant attention in both academic and non-academic settings. However, the collaborative operation of multi-robotic fish systems encounters substantial challenges due to the complex underwater environment and unique movement mode. In this paper, we propose a multi-agent reinforcement learning (MARL) approach to develop a viable strategy for underwater cooperative pursuit. Initially, considering the hydrodynamic model and motion characteristics of robotic fish, we construct a specific simulation environment with multiple fish-like agents, which provides a highly realistic state transition model. Next, we develop a MARL-based strategy learning framework that incorporates appropriate reward functions and agent actions for policy learning. Finally, a series of comprehensive simulations and practical experiments are conducted to validate the effectiveness of the proposed method and confirm its successful application in underwater pursuit scenarios. These findings offer valuable insights for further research in underwater multiple robot systems. © 2016 IEEE.

关键词： Chatbots

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Ensemble of CNN classifiers using Choquet Fuzzy Integral Technique for PCB Defect Classification

Ensemble of CNN classifiers using Choquet Fuzzy Integral Tec...

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IEEE International Conference on Computer Communication and the Internet (ICCCI)

作者： Boonchana Purahong Woranidtha Krungseanmuang Kasi Tenghongsakul Tuanjai Archevapanich Parkpoom Khunthawiwone Nitjaree Satayarak Attasit Lasakul Department of Computer Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Robotics and Computational Intelligent Systems School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electrical Engineering School of Engineering King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Department of Electronics and Communication Engineering Faculty of Engineering and Architecture Rajamagala University of Technology Suvarnabhumi Nonthaburi Thailand

ISBN: (数字)9798350351767

ISBN: (纸本)9798350351774

This paper presents a novel method for detecting defects in printed circuit boards (PCBs) using an ensemble of classifiers based on the Choquet fuzzy integral. Our approach employs convolutional neural network (CNN) models, specifically ResNet152, VGG19, and InceptionV3 as base classifiers to identify six types of PCB defects: spurs, mouse bites, short circuits, open circuits, spurious copper, and pinholes. Given the critical role of PCBs in ensuring electronic equipment reliability, effective defect detection methods like ours are essential. We employ pre-trained CNN models for feature extraction and classification of PCB defects. Following this, we combine the prediction scores using the Choquet fuzzy integral to derive more accurate final labels, exceeding the accuracy of standalone models. Our approach is tested on PCB images obtained from public repositories, captured using a linear scan CCD. The evaluation results demonstrate average precision, recall, F-score, and accuracy of 93.0%, 95.2%, 95.1%, and 95.1%, respectively.

关键词： Measurement Accuracy Electronic equipment Printed circuits Predictive models Feature extraction Internet

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Comparison of Facial Expression and Cerebral Blood Flow Sensors for Emotion Estimation Using Communication Robots

Comparison of Facial Expression and Cerebral Blood Flow Sens...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Naoya Hasegawa Rin Hirahara Shohei Kurokawa Yoshihiko Takahashi Department of Comprehensive Engineering - Mechanical Systems Course Kindai University Technical College Nabari Mie Japan Department of Robotics and Mechatronics Kanagawa Institute of Technology Atsugi Kanagawa Japan

In this study, a communication system that quantifies human emotions and provides feedback control was fabricated for a communication robot. The robot can provide suggestions to users and relay transmissions during human-to-human communication. Based on the emotions of the listener; the speed, words, and tone of the speaker are selected. Furthermore, the sensor was adopted to estimate emotions using facial expressions. To investigate its reliability, the results were compared with those from cerebral blood flow sensors which have been reported in numerous studies. There are several methods for measuring cerebral blood flow; however near-infrared spectroscopy (NIRS) sensors are used by placing them on either side of the subject's forehead to determine the right and left brains activity based on the flow rate of hemoglobin in the blood. The right-brain is dominant in spatial cognition, including visual and auditory perception, whereas the left-brain is dominant in language comprehension, reasoning, and relating information to knowledge in memory. The experimental results of the face sensors and the NIRS sensors are determined when a soap bubble robot ejects bubbles at the subject.

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Comparison of Facial Expression Sensors and Skin Potential Response Sensors for Emotion Estimation by Communication Robots

Comparison of Facial Expression Sensors and Skin Potential R...

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International Conference on Control, Automation and systems ( ICCAS)

作者： Naoya Hasegawa Rin Hirahara Yoshihiko Takahashi Department of Comprehensive Engineering - Mechanical Systems Course Kindai University Technical College Nabari Mie Japan Department of Robotics and Mechatronics Kanagawa Institute of Technology Atsugi Kanagawa Japan

A communication system that quantifies human emotions and provides feedback control was fabricated for a communication robot. The robot can provide suggestions to users and relay transmissions during human-to-human communication. For instance, if the user is excited, the robot can calm them down, and if the user is sad, the robot can cheer them up. The reliability in estimating emotions from facial expressions of a proposed sensor was compared with that of galvanic skin reflex (GSR) and skin potential response (SPR) sensors, which have been reported in several papers. A GSR sensor can measure tension and agitation in humans. The sensor estimating emotions from facial expressions can measure five facial expressions: “Happiness”, “Surprise”, “Anger”, “Sadness”, and “Neutrality”. Furthermore, the method is non-contact and suitable for practical use because it uses images. As the emotion-estimated values are output, they can be used without any privacy issues. The changes in emotions toward subjects using an entertainment robot that can eject soap bubbles were evaluated. An experimental chamber was created to prevent disturbances and measure only the effects of the soap bubble ejection and cessation events. This study reports that SPR sensors and emotion estimation sensors can be handled similarly.

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Robust Tracking for Electromagnetic-Actuated Microrobot Via Sliding Mode Control

Robust Tracking for Electromagnetic-Actuated Microrobot Via ...

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Annual Conference of Industrial Electronics Society

作者： Yihang Wu Ziyao Qu Xiaocong Chang Zhijian Hu Rongni Yang Renjie Ma State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China School of Electrical and Electronic Engineering Nanyang Technological University Singapore School of Control Science and Engineering Shandong University Jinan China

Microrobots can work in small, enclosed spaces and complete complex tasks. It has potential applications in the field of biomedical and life sciences. This paper focuses on investigating robust dynamic tracking for electromagnetic-actuated microrobot via sliding mode control. First, we generalize the planar motion to the case of spatial trajectory tracking and then establish the three-dimensional tracking error model of microrobot. Then, we deploy a novel sliding control strategy and ensure the stability of sliding mode dynamics, once the robotic state reaches the sliding surface and sustains there in the sequel. Finally, the numerical simulation is conducted, compared to the traditional proportion-integration-differentiation control strategy, to verify the effectiveness and advantages of our proposed control scheme.

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Detecting Worker Attention Lapses in Human-Robot Interaction: An Eye Tracking and Multimodal Sensing Study 28

Detecting Worker Attention Lapses in Human-Robot Interaction...

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28th International Conference on Automation and Computing, ICAC 2023

作者： Dai, Zhuangzhuang Park, Jinha Kaszowska, Aleksandra Li, Chen School of Engineering and Applied Science Aston University Dept. of Computer Science Engineering and Physical Science BirminghamB4 7ET United Kingdom The Maersk Mc Kinney Moller Institute University of Southern Denmark Sdu Robotics Odense5230 Denmark Aalborg University Department of Electronic Systems Aalborg9220 Denmark Aalborg University The Department of Materials and Production Aalborg9220 Denmark

ISBN: (纸本)9798350335859

The advent of industrial robotics and autonomous systems endow human-robot collaboration in a massive scale. However, current industrial robots are restrained in co-working with human in close proximity due to inability of interpreting human agents' attention. Human attention study is non-trivial since it involves multiple aspects of the mind: perception, memory, problem solving, and consciousness. Human attention lapses are particularly problematic and potentially catastrophic in industrial workplace, from assembling electronics to oper-ating machines. Attention is indeed complex and cannot be easily measured with single-modality sensors. Eye state, head pose, posture, and manifold environment stimulus could all play a part in attention lapses. To this end, we propose a pipeline to annotate multimodal dataset of human attention tracking, including eye tracking, fixation detection, third-person surveil-lance camera, and sound. We produce a pilot dataset containing two fully annotated phone assembly sequences in a realistic manufacturing environment. We evaluate existing fatigue and drowsiness prediction methods for attention lapse detection. Experimental results show that human attention lapses in production scenarios are more subtle and imperceptible than well-studied fatigue and drowsiness. © 2023 IEEE.

关键词： Eye tracking

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