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International Conference on Mechatronics and Machine Vision in Practice (M2VIP)

作者： Jiacheng Wan Yinfeng Fang Chunsheng Guo Zhaojie Ju Dalin Zhou Pinghu Digital Technology Innovation Research Institute School of Communication Engineering Hangzhou Dianzi University Hangzhou China Intelligent Systems and Biomedical Robotics Group School of Computing University of Portsmouth Portsmouth UK

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

The demand for emotion recognition is increasing not only in daily life but also in professional domains such as disease diagnosis and clinical rehabilitation. By monitoring non-invasive physiological signals of patients, providing timely feedback on their emotional state can significantly aid in patient recovery and subsequent expert treatment evaluation. In this study, we propose a mutual fusion approach utilizing both feature fusion and decision fusion techniques for emotion classification. Firstly, statistical knowledge-based methods were employed to select physiological signals and extract highly correlated and complementary features for feature-level fusion. Subsequently, a similarity weight, based on feature selection and statistical analysis, was incorporated into the decision fusion process. Our proposed method yielded promising results, achieving 82.5% accuracy and 75.2% macro f1 score for three-class classification (neutral vs. stress vs. amusement), 95.4% accuracy and 94.6% macro f1 score for two-class classification (stress vs. non-stress) using publicly available data from the WESAD dataset. These results demonstrate that employing a mutual fusion approach outperforms single fusion methods in emotion classification tasks.

关键词： Accuracy Mechatronics Statistical analysis Machine vision Anxiety disorders Knowledge based systems Feature extraction Medical diagnosis Biomedical monitoring Monitoring

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Fast yet predictable braking manoeuvers for real-time robot control

arXiv

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arXiv 2023年

作者： Hamad, Mazin Gutierrez-Moreno, Jesus Kussaba, Hugo T.M. Mansfeld, Nico Abdolshah, Saeed Swikir, Abdalla Burgard, Wolfram Haddadin, Sami Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Franka Emika GmbH Munich Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University Libya Department of Engineering University of Technology Nuremberg Germany Germany

This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is real-time capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings. © 2023, CC BY-NC-ND.

关键词： Braking

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AN ADDRESSABLE ELECTROWETTING VALVE FOR CENTRIFUGAL MICROFLUIDICS 26

AN ADDRESSABLE ELECTROWETTING VALVE FOR CENTRIFUGAL MICROFLU...

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26th International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Xia, Yanming Song, Chao Meng, Yingchao Xue, Peng deMello, Andrew J. Gao, Quan Stavrakis, Stavros Ma, Shenglin Cao, Xiaobao Department of Mechanical & Electrical Engineering Xiamen University Xiamen China Institute for Chemical and Bioengineering ETH Zürich Zürich8093 Switzerland Institute of Robotics and Intelligent Systems ETH Zürich Zürich8006 Switzerland Guangzhou Laboratory Guangdong Guangzhou510320 China

ISBN: (纸本)9781733419048

This paper presents and assesses an addressable electrowetting centrifugal (EWC) valve, which can rapidly and selectively open through (remote) control of the applied electric field. The utility of EWC valves is showcased by the implementation of fluid complexly controlling and CD4 cell antigens detection. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life Sciences. All rights reserved.

关键词： Wetting

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Adaptive Fuzzy Generalized Predictive Control of pH in Tubular Photobioreactors on Microalgae Plant

Adaptive Fuzzy Generalized Predictive Control of pH in Tubul...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Mafalda Amaro Jérôme Mendes Tiago Matias Rui Araújo Department of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal Department of Mechanical Engineering CEMMPRE ARISE University of Coimbra Coimbra Portugal Oncontrol Technologies Coimbra Portugal

Culture conditions in a photobioreactor on a microalgae plant are essential for production rates, and the pH value is one of the critical variables for a harmonizing process. However, the biological nature of microalgae growth represents a complex reaction that is arduous to model and control. This work proposes an approach of the application of adaptive identification and predictive control to regulate the pH in a photobioreactor in a microalgae production system. The proposed approach is composed of two main stages: identification and control. The identification is performed by a Takagi-Sugeno (T-S) fuzzy model, which will be learned with an offline strategy, by Fuzzy c-means, for the antecedent part, and by the Least Squares Method (LSM), for the consequent part. Then, the model is updated with an online method, the Recursive Least Squares with Adaptive Directional Forgetting (RLS-ADF) factor algorithm. The control scheme is based on a Generalized Predictive Control approach, which is a Model Predictive Controller (MPC), with the adaptive T-S fuzzy model designed in the identification stage. In this way, the model parameters from GPC are online adapted by RLS-ADF. To validate the control structure, the proposed approach was tested by using an estimated model from real data of a microalgae production process.

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Efficient Learning and Control of String- Type Artificial Muscle Driven Robotic systems

Efficient Learning and Control of String- Type Artificial Mu...

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American Control Conference (ACC)

作者： Jiyue Tao Sunil Kumar Rajendran Yunsong Zhang Feitian Zhang Dexin Zhao Tongsheng Shen The Department of Advanced Manufacturing and Robotics College of Engineering Peking University Beijing China The National Innovation Institute of Defense Technology Beijing China The BSS Technologies Inc. Takoma Park MD USA The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing China

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

This paper investigates the learning-based control of robotic systems driven by string-type artificial muscles. Due to the highly nonlinear dynamics of the actuators and the complicated mechanical structure, it is typically very challenging to design traditional model-based controllers that exhibit desired control performances. With the rapid development of machine learning techniques, deep reinforcement learning (DRL) algorithms have been utilized to control a variety of complex robotic systems. However, these DRL algorithms usually require a huge amount of training data and iterations to converge, which is generally unacceptable for the robotic systems of interest. Therefore, this paper designs an efficient learning-based control algorithm, aiming to improve the training efficiency for robotic systems driven by string-type artificial muscle actuators. Specially, two training improvements are proposed including imitation learning and data augmentation. This paper applies the proposed learning methods to three popular DRL algorithms and tests the control performances in three case studies using three string-type artificial muscle-driven robots, including a parallel robotic wrist, a two degrees-of-freedom (DOF) robotic eye and a robotic finger. Simulation results show that the proposed learning-based control methods significantly accelerate the convergence speed and improve the data efficiency in all the three case studies.

关键词： Artificial muscles Wrist Training Actuators Machine learning algorithms Imitation learning Fingers

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Development and Validation of a Modular Sensor-Based System for Gait Analysis and Control in Lower-Limb Exoskeletons

arXiv

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arXiv 2024年

作者： Marinou, Giorgos Kourouma, Ibrahima Mombaur, Katja Heidelberg University Heidelberg69120 Germany Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe76131 Germany Department of Systems Design Engineering The Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada

With rapid advancements in exoskeleton hardware technologies, successful assessment and accurate control remain challenging. This study introduces a modular sensor-based system to enhance biomechanical evaluation and control in lower-limb exoskeletons, utilizing advanced sensor technologies and fuzzy logic. We aim to surpass the limitations of current biomechanical evaluation methods confined to laboratories and to address the high costs and complexity of exoskeleton control systems. The system integrates inertial measurement units, force-sensitive resistors, and load cells into instrumented crutches and 3D-printed insoles. These components function both independently and collectively to capture comprehensive biomechanical data, including the anteroposterior center of pressure and crutch ground reaction forces. This data is processed through a central unit using fuzzy logic algorithms for real-time gait phase estimation and exoskeleton control. Validation experiments with three participants, benchmarked against gold-standard motion capture and force plate technologies, demonstrate our system’s capability for reliable gait phase detection and precise biomechanical measurements. By offering our designs open-source and integrating cost-effective technologies, this study advances wearable robotics and promotes broader innovation and adoption in exoskeleton research. Copyright © 2024, The Authors. All rights reserved.

关键词： Exoskeleton (robotics)

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Fixed Time Synchronization for Heterogeneous Complex Network with Time Delay by Soft Variable Structure Control Method

Fixed Time Synchronization for Heterogeneous Complex Network...

引用

Asian Control Conference

作者： Jie Wei Zhihuan Chen Tinghao Shi XiuJuan Zheng Zhenhua Zhu Mian Hu Engineering Research Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan University of Science and Technology Wuhan China Institute of Robotics and Intelligent Systems Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

Ahstract-This study focuses on the fixed-time synchronization problem of drive-response complex networks with time delay. Distinguish from most of previous works, a more practical complex network is introduced, whose nodes and dimension between drive-response system are different. To achieve fixed time synchronization of the network, a dynamic soft variable structure control with fixed-time strategy is proposed, aiming to achieve higher regulation rate and shorter settling time. Then, the stability of the controller is rigorously proven through mathematical analysis and numerical simulations validate the effectiveness of presented results.

关键词： Delay effects Power system dynamics Stability criteria Complex networks Power system stability Numerical simulation Robustness

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Biodegradable Gliding Paper Flyers Fabricated Through Inkjet Printing

Biodegradable Gliding Paper Flyers Fabricated Through Inkjet...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Luca Girardi Rui Wu Yuki Fukatsu Hiroki Shigemune Stefano Mintchev Department of Environmental Systems Science Environmental Robotics Laboratory ETH Zurich Zurich Switzerland Swiss Federal Institute for Forest Snow and Landscape Research (WSL) Birmensdorf Switzerland Department of Electrical Engineering Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Seed-inspired minimalist microflyers are showing potential as dispersal platforms for sensor networks and seeds. Their function relies on the sheer number of low-cost flyers, inevitably raising concerns about the post-operation environmental impact. We propose a biodegradable paper glider platform fabricated through origami inkjet printing. This method can fold origami along printing patterns on different paper varieties. We created a printing pattern that allows 2D paper sheets to self-fold into a 3D flying wing glider with designable wing geometry and center of gravity (CG). The design allows stable and repeatable gliding behavior, proven in our gliding tests. It successfully achieves the dispersal of multiple gliders from a hovering drone, covering an average horizontal distance larger than the release height. We also tested the biodegradation of different paper types compatible with the printing method, showing near-complete degradation after 15 weeks in moist soil. Our study presents a novel, potentially scalable approach for fabricating environmentally friendly microflyers, offering new avenues for remote environmental sensing and automated forest restoration programs.

关键词： Geometry Atmospheric modeling Forestry Aerodynamics Three-dimensional printing Stability analysis Ink jet printing Seeds (agriculture) Thermal stability Paints

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Multi-Agent 3D Map Reconstruction and Change Detection in Microgravity with Free-Flying Robots 74

Multi-Agent 3D Map Reconstruction and Change Detection in Mi...

引用

74th International Astronautical Congress, IAC 2023

作者： Dinkel, Holly Di, Julia Santos, Jamie Albee, Keenan Borges, Paulo Moreira, Marina Alexandrov, Oleg Coltin, Brian Smith, Trey Department of Aerospace Engineering The University of Illinois Urbana-Champaign UrbanaIL United States Department of Mechanical Engineering Stanford University StanfordCA United States Department of Physics Chalmers University of Technology Gothenburg Sweden Maritime and Multi-Agent Autonomy Group The Jet Propulsion Laboratory California Institute of Technology PasadenaCA United States Robotics and Autonomous Systems Group CSIRO Data61 BrisbaneQLD Australia Intelligent Robotics Group The NASA Ames Research Center Moffett FieldCA United States

Assistive free-flyer robots autonomously caring for future crewed outposts-such as NASA's Astrobee robots on the International Space Station (ISS)-must be able to detect day-to-day interior changes to track inventory, detect and diagnose faults, and monitor the outpost status. This work presents a framework for multi-agent cooperative mapping and change detection to enable robotic maintenance of space outposts. One agent is used to reconstruct a 3D model of the environment from sequences of images and corresponding depth information. Another agent is used to periodically scan the environment for inconsistencies against the 3D model. Change detection is validated after completing the surveys using real image and pose data collected by Astrobee robots in a ground testing environment and from microgravity aboard the ISS. This work outlines the objectives, requirements, and algorithmic modules for the multi-agent reconstruction system, including recommendations for its use by assistive free-flyers aboard future microgravity outposts. Copyright © 2023 by the International Astronautical Federation (IAF). All rights reserved.

关键词： 3D Reconstruction Artemis Change Detection Mapping Microgravity robotics

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A Formalisation of the Purpose Framework: the Autonomy-Alignment Problem in Open-Ended Learning Robots

arXiv

引用

arXiv 2024年

作者： Baldassarre, Gianluca Duro, Richard J. Cartoni, Emilio Khamassi, Mehdi Romero, Alejandro Santucci, Vieri Giuliano Institute of Cognitive Sciences and Technologies National Research Council of Italy Italy Integrated Group for Engineering Research CITIC Universidade da Coruña Spain Institute of Intelligent Systems and Robotics Sorbonne University CNRS ParisF-75005 France

The unprecedented advancement of artificial intelligence enables the development of increasingly autonomous robots. These robots hold significant potential, particularly in moving beyond engineered factory settings to operate in the unstructured environments inhabited by humans. However, this possibility also generates a relevant autonomy-alignment problem to ensure that robots’ autonomous learning processes still focus on acquiring knowledge relevant to accomplish human practical purposes, while their behaviour still aligns with their broader purposes (e.g., related to security and ethical constraints interference). The literature has only begun to address this problem, and a conceptual, terminological, and formal framework is still lacking. Here we address one of the most challenging instances of the problem: autonomous open-ended learning (OEL) robots, capable of cumulatively acquiring new skills and knowledge through direct interaction with the environment, guided by self-generated goals and intrinsic motivations. In particular, we propose a computational framework, first introduced qualitatively and then formalised, to support the design of OEL robot architectures that balance autonomy and control. The framework pivots on the novel concept of purpose. A human purpose specifies what humans (e.g., designers or users) want the robot to learn, do or not do, within a certain boundary of autonomy and independently of the domains in which it operates. The framework decomposes the autonomy-alignment problem into more tractable sub-problems: the alignment of ‘robot purposes’ with human purposes, either by hardwiring or through learning;the arbitration between multiple purposes;the grounding of purposes into specific domain-dependent robot goals;and the competence acquisition needed to accomplish these goals. The framework and its potential utility are further elucidated through the discussion of hypothetical example scenarios framed within it. Copyright © 2024, The Auth

关键词： Robot learning

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