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Transcranial Direct Current Stimulation Associated with Visual and Auditory Cueing during Gait Training: A Case Study with a Parkinson's Disease Patient

Transcranial Direct Current Stimulation Associated with Visu...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Angie Pino Patricio Barria Karim Baleta Rolando Aguilar José M. Azorín Marcela Múnera Carlos A. Cifuentes Club de Leones Cruz del Sur Rehabilitation Center Punta Arenas Chile Department of Electrical Engineering University of Magallanes Punta Arenas Chile Department of Systems Engineering and Automation Miguel Hernández University of Elche Elche Spain Bristol Robotics Laboratory University of the West of England Bristol UK

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Parkinson's Disease (PD) is a neurodegenerative disorder that leads to motor dysfunctions and restricts walking independence. Approaches such as transcranial direct current stimulation (tDCS) and cueing gait are potential rehabilitation strategies that have proven efficient in improving motor function in individuals with PD in preliminary and separate studies. This case study with a single PD patient explores the feasibility and potential effects of combining anodal tDCS (a-tDCS) with visual and auditory cues during 15 gait training sessions. The patient was assessed before the first intervention and after completing all training weeks regarding kinematic parameters of gait, neuronal activation, muscle strength, and functional skills. Results showed a beneficial impact in significantly increasing the participant's speed, stride length, step length, and muscle strength. Furthermore, the decrease in beta brain activity and increase in the delta were associated with a reduction in the effects of the disease, improved motor performance, and shorter functional task times. This study suggests the a-tDCS strategy with double cueing gait can lead to therapeutic potential in relieving the disease's motor symptoms.

关键词： Training Visualization Motor drives Parkinson's disease Kinematics Muscles Motors Neurorehabilitation Transcranial direct current stimulation Robots

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Robust-Tube Model Predictive Control for Piezoelectrically Actuated Fast Mechanical Switches for Hybrid Circuit Breakers

Robust-Tube Model Predictive Control for Piezoelectrically A...

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Artificial Intelligence and automation Control (AIAC), International Conference on

作者： Yu Chai Chen Yang Huijuan Dong Jie Zhao Yuri A. W. Shardt Bingfeng Ju ZJU-Hangzhou Global Scientific and Technological Innovation Center Hangzhou China State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin China Department of Automation Engineering Technical University of Ilmenau Ilmenau Germany School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (数字)9798331529147

ISBN: (纸本)9798331529154

Safeguarding high voltage direct current (HVDC) systems against direct current faults is an inherently demanding task. A promising solution to tackle this challenge is the application of piezoelectrically actuated fast mechanical switches (PA-FMS). However, mechanical resonance and random vibrations in the travel curves of P A-FMS have a substantial impact on their control accuracy. To tackle this challenge, a robust-tube model predictive control (RTMPC) strategy is developed. RTMPC drives the disturbed system state into the minimal robustly positively invariant (mRPI) set centered on the target and ensures that constraints are satisfied. The error feedback gain is calculated using the proposed linear matrix inequality (LMI) based method to minimize the effect of disturbances on the trajectory. An efficient outer approximation method is used to compute the mRPI set. Simulation results show that the critically damped travel curves have been achieved, along with a reduction of the external disturbances by 98%.

关键词： Vibrations Uncertain systems Uncertainty Simulation Switches High-voltage techniques Predictive models Trajectory Approximation methods Predictive control

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SoftGPT: Learn Goal-oriented Soft Object Manipulation Skills by Generative Pre-trained Heterogeneous Graph Transformer

arXiv

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arXiv 2023年

作者： Liu, Junjia Li, Zhihao Lin, Wanyu Calinon, Sylvain Tan, Kay Chen Chen, Fei The Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong The Department of Computing The Hong Kong Polytechnic University Hong Kong The Idiap Research Institute Martigny Switzerland

Soft object manipulation tasks in domestic scenes pose a significant challenge for existing robotic skill learning techniques due to their complex dynamics and variable shape characteristics. Since learning new manipulation skills from human demonstration is an effective way for robot applications, developing prior knowledge of the representation and dynamics of soft objects is necessary. In this regard, we propose a pre-trained soft object manipulation skill learning model, namely SoftGPT, that is trained using large amounts of exploration data, consisting of a three-dimensional heterogeneous graph representation and a GPT-based dynamics model. For each downstream task, a goal-oriented policy agent is trained to predict the subsequent actions, and SoftGPT generates the consequences of these actions. Integrating these two approaches establishes a thinking process in the robot’s mind that provides rollout for facilitating policy learning. Our results demonstrate that leveraging prior knowledge through this thinking process can efficiently learn various soft object manipulation skills, with the potential for direct learning from human demonstrations. Copyright © 2023, The Authors. All rights reserved.

关键词： Robot applications

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An Intelligent Robot Assisting Medical Practitioners to Aid Potential Covid-19 Patients 6

An Intelligent Robot Assisting Medical Practitioners to Aid ...

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6th International Conference on Image Information Processing, ICIIP 2021

作者： Sowrabh, S. Rameshkumar, Ashith Athira, P.V. Jishnu, T.B. Anish, M.N. UPES MTech in Robotics and Automation School of Engineering Dehradun India NSSCE Bachelor of Technology in Instrumentation Control Engineering Department Palakkad India Assistant System Engineer-trainee TCS Limited Kochi India NSSCE Instrumentation Control Engineering Department Palakkad India

ISBN: (纸本)9781665433617

The purpose of this paper is to develop a robot-nurse capable of assisting human health professionals in a COVID-19 isolation unit such that health care professionals may discontinue wearing the personal protective kit that they previously wore when dealing with patients at isolation units. The robot can perform most of the tasks carried out by human nurses in a hospital. It can take a patient's temperature without touching them, administer medicines at the appropriate time, assist in sanitizing the individual and their hands, and offer a section for sterilizing medical equipment. [14] © 2021 IEEE.

关键词： Nursing

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CAIS: Culvert Autonomous Inspection Robotic System

CAIS: Culvert Autonomous Inspection Robotic System

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

作者： Chuong Phuoc Le Pratik Walunj An Duy Nguyen Yongyi Zhou Binh Nguyen Thang Nguyen Anton Netchaev Hung Manh La Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA Department of Engineering Texas A&M University–Corpus Christi Corpus Christi TX USA USACE Engineer Research and Development Center (ERDC) Information Technology Lab Vicksburg MS USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Culverts, essential components of drainage systems, require regular inspection to ensure optimal functionality. However, culvert inspections pose numerous challenges, including accessibility, manpower, defect localization, and reliance on superficial assessments. To address these challenges, we propose a novel Culvert Autonomous Inspection Robotic System (CAIS) equipped with advanced sensing and evaluation capabilities. Our solution integrates an RGBD camera, deep learning, lighting systems, and non-destructive evaluation (NDE) techniques to enable accurate and comprehensive condition assessments. We present a pioneering Partially Observable Markov Decision Process (POMDP) framework to resolve uncertainty in autonomous inspections, especially in confined and unstructured environments like culverts or tunnels. The framework outputs detailed 3D maps highlighting visual defects and NDE condition assessments, demonstrating consistent and reliable performance in both indoor and outdoor scenarios. Additionally, we provide an open-source implementation of our framework on GitHub, contributing to the advancement of autonomous inspection technology and fostering collaboration within the research community. Source codes are available * .

关键词： Visualization Uncertainty Three-dimensional displays Source coding Markov decision processes Robot vision systems Inspection Sensors Reliability Software development management

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A Super-Twisting Observer Design for Thrust-Loss Fault Tolerant Control of Quadrotor Vehicles

A Super-Twisting Observer Design for Thrust-Loss Fault Toler...

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IEEE Conference on Decision and Control

作者： D. M. K. K. Venkateswara Rao Hamed Habibi Prathyush P. Menon Christopher Edwards Holger Voos Automation and Robotics Research Group Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Luxembourg Cooperative Robotics and Autonomous NEtworks (CRANE) lab at the Centre for Future Clean Mobility University of Exeter Engineering Research Centre Exeter UK Structure and Dynamics Research Group CEMPS University of Exeter Exeter UK Department of Engineering Faculty of Science Technology and Medicine (FSTM) University of Luxembourg

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

In this paper, we tackle the thrust loss problem in a quadrotor unmanned aerial vehicle, by the design of a super-twisting observer in the body frame, achieving finite-time convergence. On this basis, we design a super-twisting low-level controller to track the desired trajectory and compensate for the loss effects. Then, the acceleration commands are designed, and the thrust, Euler angles, and angular speeds are computed. The stability and the finite-time convergence of the resultant cascade system are studied, and we show the recovery of the nominal behavior, as well as the weak recovery of the separation principle. Finally, high-fidelity experimental analyses are conducted, to evaluate the performance of the proposed algorithm in the presence of a variety of thrust loss situations.

关键词： Accuracy Trajectory tracking Fault tolerant control Observers Autonomous aerial vehicles Stability analysis Trajectory Quadrotors Convergence

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Hybrid Unmanned Aerial Underwater Vehicles: A Survey on Concepts and Technologies

SSRN

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SSRN 2023年

作者： Pinheiro, Pedro Miranda Dias de Oliveira Evald, Paulo Jefferson Grando, Ricardo Bedin Neto, Armando Alves Jorge Drews, Paulo Lilles Intelligent Robotics and Automation Group Federal University of Rio Grande Rio Grande Brazil Intelligent Systems and Control Group Federal University of Pelotas Pelotas Brazil Department of Robotics Technological University of Uruguay Rivera Uruguay Department of Electronic Engineering Federal University of Minas Gerais Belo Horizonte Brazil

Hybrid Unmanned Aerial Underwater Vehicle (HUAUV) is a class of multi-modal mobile robots characterized by the ability to fly and navigate underwater, as well as being able to perform the transition between both environments. The development of modern vehicles of this class is driven by the enhancement of mobile robotic systems, sensors, and control techniques. However, the class definition and hierarchy for these vehicles are still vague and undefined. Therefore, this work presents a survey of concepts and prototypes of HUAUVs, developed from 2010 until 2022. The search for works was performed in relevant international conference proceedings and journals. We present a classification of state-of-the-art concepts and prototypes of HUAUVs based on specific main features of vehicles, allowing the identification of trends in HUAUV development, and an actuator-based taxonomy for HUAUVs. Hence, our categorization provides a more cohesive background for future works, as well as a better understanding of the main features of hybrid vehicles. Finally, a pragmatic approach to future applications is provided, given the current trends in Mobile robotics, focusing on unmanned aerial/underwater vehicles. © 2023, The Authors. All rights reserved.

关键词： Antennas

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Active Visuo-Haptic Object Shape Completion

arXiv

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arXiv 2022年

作者： Rustler, Lukas Lundell, Jens Behrens, Jan Kristof Kyrki, Ville Hoffmann, Matej The Department of Cybernetics Faculty of Electrical Engineering CTU Prague Czech Republic The Intelligent Robotics Group Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo02150 Finland The Czech Institute of Informatics Robotics and Cybernetics CTU Prague Czech Republic

Recent advancements in object shape completion have enabled impressive object reconstructions using only visual input. However, due to self-occlusion, the reconstructions have high uncertainty in the occluded object parts, which negatively impacts the performance of downstream robotic tasks such as grasping. In this work, we propose an active visuo-haptic shape completion method called Act-VH that actively computes where to touch the objects based on the reconstruction uncertainty. Act-VH reconstructs objects from point clouds and calculates the reconstruction uncertainty using IGR, a recent state-of-the-art implicit surface deep neural network. We experimentally evaluate the reconstruction accuracy of Act-VH against five baselines in simulation and in the real world. We also propose a new simulation environment for this purpose. The results show that Act-VH outperforms all baselines and that an uncertainty-driven haptic exploration policy leads to higher reconstruction accuracy than a random policy and a policy driven by Gaussian Process Implicit Surfaces. As a final experiment, we evaluate Act-VH and the best reconstruction baseline on grasping 10 novel objects. The results show that Act-VH reaches a significantly higher grasp success rate than the baseline on all objects. Together, this work opens up the door for using active visuo-haptic shape completion in more complex cluttered scenes. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep neural networks

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A Soft Micro-Robotic Catheter for Aneurysm Treatment: A Novel Design and Enhanced Euler-Bernoulli Model with Cross-Section Optimization

A Soft Micro-Robotic Catheter for Aneurysm Treatment: A Nove...

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IEEE International Conference on robotics and automation (ICRA)

作者： Emanuele Nicotra Chi Cong Nguyen James Davies Phuoc Thien Phan Trung Thien Hoang Bibhu Sharma Adrienne Ji Kefan Zhu Trung Dung Ngo Van Anh Ho Hung Manh La Nigel H. Lovell Thanh Nho Do Graduate School of Biomedical Engineering Faculty of Engineering UNSW Sydney Kensington Campus Australia More-Than-One Robotics Laboratory Faculty of Sustainable Design Engineering University of Prince Edward Island Charlottetown Canada Soft Haptics Laboratory School of Materials Science Japan Advanced Institute of Science and Technology Kawaguchi Saitama Japan Department of Computer Science and Engineering Advanced Robotics and Automation (ARA) Lab University of Nevada Reno NV USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Aneurysms, balloon-like bulges in blood vessels, present a significant health risk due to their potential to rupture, leading to life-threatening internal bleeding. Current treatments often involve delivering embolic materials or metal coils to fill these bulges, occluding them from the pressure of blood flow. However, clinical micro-catheters that deploy embolic materials used today face limitations, primarily their rigidity and the lack of active control over the bending tip of the catheter. This paper introduces a new soft micro-robotics catheter, with diameter of only 0.8 mm, equipped with a hollow channel. With this new design, the new device can induce bending motions at its tip for active steerability to reach desired aneurysm targets and then perform the delivery of embolic materials and tools. To enhance the control and precise navigation during procedures, a robust mathematical model and image processing techniques are also introduced and validated. Experiments are also performed to characterise and validate the model’s accuracy and the steerability and navigation capabilities of the new micro-catheter.

关键词： Performance evaluation Navigation Metals Aneurysm Bending Mathematical models Rigidity

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HiDe-LLaVA: Hierarchical Decoupling for Continual Instruction Tuning of Multimodal Large Language Model

arXiv

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arXiv 2025年

作者： Guo, Haiyang Zeng, Fanhu Xiang, Ziwei Zhu, Fei Wang, Da-Han Zhang, Xu-Yao Liu, Cheng-Lin SAIS UCAS United Kingdom MAIS Institute of Automation Chinese Academy of Sciences China SAI UCAS United Kingdom Centre for Artificial Intelligence and Robotics HKISI-CAS Hong Kong Department of Computer and Information Engineering Xiamen University of Technology China

Instruction tuning is widely used to improve a pre-trained Multimodal Large Language Model (MLLM) by training it on curated task-specific datasets, enabling better comprehension of human instructions. However, it is infeasible to collect all possible instruction datasets simultaneously in real-world scenarios. Thus, enabling MLLM with continual instruction tuning is essential for maintaining their adaptability. However, existing methods often trade off memory efficiency for performance gains, significantly compromising overall efficiency. In this paper, we propose a task-specific expansion and task-general fusion framework based on the variations in Centered Kernel Alignment (CKA) similarity across different model layers when trained on diverse datasets. Furthermore, we analyze the information leakage present in the existing benchmark and propose a new and more challenging benchmark to rationally evaluate the performance of different methods. Comprehensive experiments showcase a significant performance improvement of our method compared to existing state-of-the-art methods. Our code will be public available. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive Learning

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