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arXiv 2025年

作者： Oikonomou, Katerina Maria Kansizoglou, Ioannis Gasteratos, Antonios Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece

Feature discrimination is a crucial aspect in neural network design, as it directly impacts the network’s ability to distinguish between classes and generalize across diverse datasets. The accomplishment of achieving high-quality feature representations ensures high intra-class separability and poses one of the most challenging research directions. While convetional deep neural networks (DNNs) rely on complex transformations and very deep networks to come up with meaningful feature representations, they usually require days of training and consume significant energy amounts. To this end, spiking neural networks (SNNs) offer a promising alternative. SNN’s ability to capture temporal and spatial dependencies renders them particularly suitable for complex tasks, where multi-modal data are required. In this paper, we propose a feature discrimination approach for multi-modal learning with SNNs, focusing on audio-visual data. We employ deep spiking residual learning for visual modality processing and a simpler yet efficient spiking network for auditory modality processing. Lastly, we deploy a spiking multilayer perceptron for modality fusion. We present our findings and evaluate our approach against similar works in the field in classification challenges. To the best of our knowledge, this is the first work investigating feature discrimination in SNNs. © 2025, CC BY-NC-SA.

关键词： Deep neural networks

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Case Study on an Experimental Autonomous Vehicle and Evaluation of its System Reliability 4

Case Study on an Experimental Autonomous Vehicle and Evaluat...

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4th International Conference on Electrical, Computer, Communications and Mechatronics engineering, ICECCME 2024

作者： Misaros, Marius Robier, Clement Stan, Ovidiu-Petru Enyedi, Szilard Stan, Anca Gota, Dan Ioan Miclea, Liviu Technical University of Cluj-Napoca Automation Department Cluj-Napoca Romania Cesi Ingineering School of Lyon Generalist Department Lyon France Technical University of Cluj Napoca Industrial Engineering Robotics and Production Management Romania

ISBN: (纸本)9798350391183

The ability of a vehicle to move without the input of a human operator marks its transition into the domain of the autonomous vehicle. This concept is gaining increasing popularity with the objective of enhancing navigational safety and facilitating the transmission and reception of information from other road users, thereby facilitating more efficient navigation. It was thus resolved to construct a partially autonomous vehicle that employs sensors and Li-Fi technology to receive and process external stimuli approaching the control side of the vehicle. The prototype in the proposed and analyzed case study is steered by means of a steering wheel that has been specially configured for this application and is connected by radio. Additionally, the case study examined the reliability of the vehicle, which is a crucial factor in determining the potential causes of malfunction or even complete failure of the prototype under test. The principal objective of the case study is to construct a prototype and conduct a reliability analysis of the system, examining each component to ascertain the strengths and weaknesses of the prototype under test. The findings of the study underscore the significance of assessing the hardware elements of the proposed autonomous vehicle, culminating in a favorable assessment of its reliability. © 2024 IEEE.

关键词： Reliability analysis

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Lidar-Visual-Inertial Odometry with Online Extrinsic Calibration

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Journal of Shanghai Jiaotong university(Science) 2023年第1期28卷 70-76页

作者：茅天阳赵文韬王景川陈卫东 Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai 200240China

To achieve precise localization,autonomous vehicles usually rely on a multi-sensor perception system surrounding the mobile *** is a time-consuming process,and mechanical distortion will cause extrinsic calibration ***,we propose a lidar-visual-inertial odometry,which is combined with an adapted sliding window mechanism and allows for online nonlinear optimization and extrinsic *** the adapted sliding window mechanism,spatial-temporal alignment is performed to manage measurements arriving at different *** nonlinear optimization with online calibration,visual features,cloud features,and inertial measurement unit(IMU)measurements are used to estimate the ego-motion and perform extrinsic *** experiments were carried out on both public datasets and real-world *** indicate that the proposed system outperforms state-of-the-art open-source methods when facing challenging sensor-degenerating conditions.

关键词： autonomous vehicles multi-sensor fusion online calibration

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A Real-time Local Planner Design for Smart Wheelchairs Based on Koopman Bilinear Model Predictive Control

A Real-time Local Planner Design for Smart Wheelchairs Based...

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2024 IEEE International Conference on Real-Time Computing and robotics, RCAR 2024

作者： Yu, Guanfeng Zhang, Han Wang, Zehao Wang, Jingchuan Shanghai Jiao Tong University Institute of Medical Robotics Department of Automation China Ministry of Education of China Key Laboratory of System Control and Information Processing China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China

ISBN: (纸本)9798350372601

The paper introduces a Koopman bilinear model predictive control (KBMPC) as a local planner for smart wheelchair systems. This approach leverages the Koopman Operator to streamline the nonlinear terms in MPC, aiming to achieve superior real-time performance. In particular, we employ the Extended Dynamic Mode Decomposition (EDMD) method to approximate the infinite dimensional Koopman operator. To tackle the challenge of designing lifting functions, we systematically utilize the derivatives of known nonlinear structures to methodically derive lifting functions. This systematic approach facilitates the identification of a precise bilinear model. Next, we formulate the corresponding Koopman bilinear MPC problem. Finally, an iterative algorithm is employed to efficiently solve the KBMPC problem. This method linearizes the bilinear model around estimations and iteratively solves the resulting quadratic programming problem to refine estimations. This allows KBMPC to function as a real-time local planner for smart wheelchairs. The prediction of nonlinear terms shows that the identified Koopman bilinear model increases the prediction accuracy. Furthermore, experiments demonstrate the advantages of the designed KBMPC local planner, highlighting its superior real-time performance and predictive capabilities. © 2024 IEEE.

关键词： Iterative methods

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Semi-Autonomous Navigation Based on Local Semantic Map for Mobile Robot

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Journal of Shanghai Jiaotong university(Science) 2025年第1期30卷 27-33页

作者： ZHAO Yanfei XIAO Peng WANG Jingchuan GUO Rui Department of Automation Institute of Medical RoboticsShanghai Jiao Tong UniversityShanghai200240China State Key Laboratory of System Control and Information Processing Ministry of Education of ChinaShanghai200240China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240China State Grid Intelligence Technology Co. Ltd.Jinan250013China

Mobile robots represented by smart wheelchairs can assist elderly people with mobility *** paper proposes a multi-mode semi-autonomous navigation system based on a local semantic map for mobile robots,which can assist users to implement accurate navigation(e.g.,docking)in the environment without prior *** order to overcome the problem of repeated oscillations during the docking of traditional local path planning algorithms,this paper adopts a mode-switching method and uses feedback control to perform docking when approaching semantic *** last,comparative experiments were carried out in the real *** show that our method is superior in terms of safety,comfort and docking accuracy.

关键词： semi-autonomous navigation mobile robot semantic map voice interaction

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The Journey/DAO/TAO of Embodied Intelligence: From Large Models to Foundation Intelligence and Parallel Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1313-1316页

作者： Tianyu Shen Jinlin Sun Shihan Kong Yutong Wang Juanjuan Li Xuan Li Fei-Yue Wang College of Information Science and Technology Beijing University of Chemical TechnologyBeijing 100029China School of Electrical and Information Engineering Jiangsu UniversityZhenjiang 212013China State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and RoboticsCollege of EngineeringPeking UniversityBeijing 100871China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China Peng Cheng Laboratory Shenzhen 518000China State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China IEEE

THE tremendous impact of large models represented by ChatGPT[1]-[3]makes it necessary to con-sider the practical applications of such models[4].However,for an artificial intelligence(AI)to truly evolve,it needs to possess a physical“body”to transition from the virtual world to the real world and evolve through interaction with the real *** this context,“embodied intelligence”has sparked a new wave of research and technology,leading AI beyond the digital realm into a new paradigm that can actively act and perceive in a physical environment through tangible entities such as robots and automated devices[5].

关键词： Intelligence DAO TAO

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Spiking neural networks: Towards bio-inspired multimodal perception in robotics

arXiv

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arXiv 2024年

作者： Oikonomou, Katerina Maria Balaska, Vasiliki Tsintotas, Konstantinos A. Mavridis, Christos N. Kansizoglou, Ioannis Gasteratos, Antonios The Department of Production and Management Engineering Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece The Department of Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden

Spiking neural networks (SNNs) have captured apparent interest over the recent years, stemming from neuroscience and reaching the field of artificial intelligence. However, due to their nature SNNs remain far behind in achieving the exceptional performance of deep neural networks (DNNs). As a result, many scholars are exploring ways to enhance SNNs by using learning techniques from DNNs. While this approach has been proven to achieve considerable improvements in SNN performance, we propose another perspective: enhancing the biological plausibility of the models to leverage the advantages of SNNs fully. Our approach aims to propose a brain-like combination of audio-visual signal processing for recognition tasks, intended to succeed in more bio-plausible human-robot interaction applications. © 2024, CC BY-NC-SA.

关键词： Adversarial machine learning

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Quantitative Progress Evaluation of Post-stroke Patients Using a Novel Bimanual Cable-driven Robot

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Journal of Bionic engineering 2021年第6期18卷 1331-1343页

作者： Thiago Alves Rogério Sales Gonçalves Giuseppe Carbone Laboratory of Automation and Robotics School of Mechanical EngineeringFederal University of UberlândiaUberlândia38400-902Brazil Department of Mechanical Energy and Management EngineeringUniversity of Calabria87036RendeItaly

Rehabilitation is the most effective way to reduce motor impairments in post-stroke *** process demands several hours with a specialized *** resources and personnel shortages,the literature reports a high interest in robotic assisted rehabilitation ***,cable-driven robotic architectures are attracting significant research interest for post-stroke ***,the existing cable-driven robots are mostly unilateral devices allowing the rehabilitation only of the most affected *** leaves unaddressed the rehabilitation of bimanual activities,which are predominant within the common Activities of Daily Living(ADL).Thus,this paper presents a specific novel design to achieve bimanual rehabilitation tasks that has been named as BiCAR ***,this paper provides a full insight on the BiCAR system as well as on its dedicated developed software *** particular,BiEval software has been developed as based on a serious game strategy and a virtual reality environment to track the patient exercising duration,motion ranges,speeds,and forces over time for achieving a quantitative assessment of the rehabilitation ***,the paper presents the BiCAR/BiEval capabilities by referring to a pilot Randomized Controlled Trial(RCT).The clinical trials have been used to validate the BiCAR/BiEval in terms of engineering feasibility and user acceptance to achieve an innovative cost-oriented integrated hardware/software device for the bimanual assistive rehabilitation of post-stroke patients.

关键词： Cable-driven robots Low cost Stroke rehabilitation Bimanual Progress tracking Serious games

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Digital Twin Definition for Detecting Abnormal Perspiration Patterns in High-Stress Professions 23

Digital Twin Definition for Detecting Abnormal Perspiration ...

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23rd RoEduNet Conference: Networking in Education and Research, RoEduNet 2024

作者： Vizitiu, Cristian Turcin, Ioan Dominey, Kevin Alexander Softic, Selver Dinculescu, Adrian-Catalin Bocanet, Vlad I. Dugan, Cosmin Moraru, Sorin-Aurel The Space Applications and Technologies Laboratory Institute of Space Science-Subsidiary of INFLPR Magurele Romania Transilvania University of Brasov Faculty of Electrical Engineering and Computer Science Department of Automatics and Information Technology Brasov Romania CAMPUS 02 University of Applied Sciences Department Automation Technology Graz Austria CAMPUS 02 University of Applied Sciences Department IT and Business Informatics Graz Austria Robotics and Production Management Technical University of Cluj-Napoca Cluj-Napoca Faculty of Industrial Engineering Department of Manufacturing Engineering Romania Bucharest University Emergency Hospital Bucharest Romania

ISBN: (纸本)9798331540388

Perspiration is a physiological response in high-stress situations, that also plays a key role in thermoregulation and stress management. Understanding perspiration patterns is used for assessing physiological responses and optimizing human performance especially in professions such as emergency medical services and military operations, as well as for individuals exposed to Isolated Confined Extreme (ICE) environments such as astronauts and space analog mission participants. This paper defines a Digital Twin (DT) model, that will be integrated with an existing physical sweat simulator module (SSM) prototype. Its goal is to simulate human perspiration under high-stress conditions by integrating environmental, physiological, and psychological data. The system architecture is developed via Model-Based Systems engineering (MBSE). The scope of the designed virtual model is to accurately reflect physical perspiration, detect abnormal perspiration patterns, and generate recommendations regarding stress countermeasures. The goal is to influence the behavioral and/or physiological state of the physical twin, thereby creating a dynamic, responsive system. Machine learning (ML) models will be used to identify patterns in the environmental, physiological, and psychological inputs and predict sweat volume, chemical composition, and regional sweat distributions across the body. ML will also be used to detect abnormal perspiration patterns based on input data. The application of generative AI for creating test data from simulation templates is anticipated to facilitate the comparative analysis of two to three distinct profiles, such as astronauts, soldiers, and paramedics or firefighters, using simulated values. The SSM, with its integrated sensors, will serve the dual purpose of validating the model and providing realistic physical simulation. © 2024 IEEE.

关键词： Physiological models

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A Framework for Active Vision-Based Robot Planning using Spiking Neural Networks

A Framework for Active Vision-Based Robot Planning using Spi...

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Mediterranean Conference on Control and automation (MED)

作者： Katerina Maria Oikonomou Ioannis Kansizoglou Antonios Gasteratos Laboratory of Robotics and Automation Democritus University of Thrace Xanthi Greece Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece

ISBN: (数字)9781665406734

ISBN: (纸本)9781665406741

Robust and energy-efficient robot planning is of utmost importance for mobile robots since the dynamic changes of the environment entail robotic agents with high adaptation capacities, so as to excel in their tasks. In this work, we introduce a hybrid spiking and deep neural network architecture for actor-critic control of a 6-DOF robot arm. Our method firstly involves autonomous object detection via active vision exploration and thereafter, the entire hybrid architecture is described. In specific, the actor utilises an integrated-and-fire model for action generation, while the critic a deep neural one for action evaluation. Lastly, the benefits of this approach in terms of energy efficiency are extensively discussed.

关键词： Deep learning automation Neural networks Object detection Manipulators Energy efficiency Planning

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