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检索条件"机构=Advanced Engineering Robotics"

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Assessing Parkinson's Disease Severity Using Channel Attention and Graph Attention Networks on Kinect Skeleton Images 19

Assessing Parkinson's Disease Severity Using Channel Attenti...

引用

19th Iranian Conference on Intelligent Systems, ICIS 2024

作者： Fadaie, Fatemeh Aghapanah, Hamed Sedighifard, Alireza Shirvan, Hadi Niknam AI and Robotics University of Isfahan Department of Computer Engineering Isfahan Iran School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Department of Bioelectrics and Biomedical Engineering Isfahan Iran Iran University of Science and Technology Department of Artificial Intelligence Isfahan Iran University of Tehran Faculty of New Sciences and Technologies Isfahan Iran

ISBN: (纸本)9798331507565

Parkinson's Disease (PD), a prevalent global neurological disorder, presents with motor symptoms like rigidity and tremors. Accurate severity assessment is vital for intervention evaluation. This study uses computer vision, specifically Kinect cameras and deep learning, to develop a system for assessing the severity of Parkinson's disease. Leveraging Kinect cameras' Precision in tracking key motion patterns, the system utilizes convolutional neural networks, namely Spatial-Temporal Graph Convolutional Network (STGCN) and Two-Stream Adaptive Graph Convolutional Network (2SAGCN), to detect movement anomalies and provide quantitative severity measures. A novel model is proposed, representing an enhanced version of 2SAGCN featuring a channel and graph attention module. Parkinson's severity classification comprises 11 classes based on 9 motion categories, totaling 99 classes. Three-dimensional transfer learning and data augmentation improve model Accuracy. This research advances non-invasive, quantitative Parkinson's assessment using innovative technology and advanced neural network models. The program is hosted on GitHub for implementation and result observation. The 10-fold cross-validation results of the best model, incorporating 2SAGCN structure, Graph, and Channel Attention, yield Accuracy, Precision, Recall, and F1-Score of Confidence Intervals 94.12± 0.14, 99.6 ± 0.20, 98.9 ± 0.10,99.2 ± 0.80 respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Positional Tracking of Physical Objects in an Augmented Reality Environment Using Neuromorphic Vision Sensors

Positional Tracking of Physical Objects in an Augmented Real...

引用

International Conference on Machine Learning and Applications (ICMLA)

作者： Flore Stécie Norcéide Emi Aoki Vinh Tran Masoumeh Farhadi Nia Charles Thompson Kavitha Chandra Center for Advanced Computation and Telecommunications (CACT) Collaborative Assistive and Rehabilitative Robotics Engineering (CARE) University of Massachusetts Lowell Lowell USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

Immersive systems such as Augmented Reality (AR) devices are being integrated across multiple domains, from manufacturing to health, environment and education. The Industry 5.0 focus on human-centric interfaces to these systems and the desire to enable them for enhancing the skill-sets of the user, present new challenges. One of these is the accurate positional tracking of objects within the AR user's environment that is relevant in real-time applications. Multiple co-located frame based cameras are typical solutions proposed for object tracking in AR. The advent of commercial neuromorphic vision sensors (NVS) in recent years, introduce a novel, event-based approach to computer vision. Inspired by biological vision, these systems reduce the amount of data transferred and enhance power efficiency when tracking in real-time. This work leverages the strengths of AR and NVS technologies to develop an integrated system for tracking physical objects and providing users with required information to interact with these objects in a virtual environment. The computational pipeline from training on a deep neural network, coordinate transformations from image, to physical and virtual coordinate systems and testing the utility of the system for an AR system user is presented. The NVS system shows significant improvement over frame camera based systems in responding to varied lighting conditions.

关键词： Training Neuromorphics Pipelines Lighting Medical treatment Artificial neural networks Vision sensors Cameras Real-time systems Augmented reality

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TTCDist: Fast Distance Estimation From an Active Monocular Camera Using Time-to-Contact

TTCDist: Fast Distance Estimation From an Active Monocular C...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Levi Burner Nitin J. Sanket Cornelia Fermüller Yiannis Aloimonos Perception and Robotics Group Electrical and Computer Engineering University of Maryland College Park Robotics Engineering Worcester Polytechnic Institute Perception and Robotics Group University of Maryland Institute for Advanced Computer Studies University of Maryland College Park

Distance estimation from vision is fundamental for a myriad of robotic applications such as navigation, manipu-lation, and planning. Inspired by the mammal's visual system, which gazes at specific objects, we develop two novel constraints relating time-to-contact, acceleration, and distance that we call the $\tau$ -constraint and $\Phi$ -constraint. They allow an active (moving) camera to estimate depth efficiently and accurately while using only a small portion of the image. The constraints are applicable to range sensing, sensor fusion, and visual servoing. We successfully validate the proposed constraints with two experiments. The first applies both constraints in a trajectory estimation task with a monocular camera and an Inertial Measurement Unit (IMU). Our methods achieve 30-70% less average trajectory error while running $25\times$ and $6.2\times$ faster than the popular Visual-Inertial Odometry methods VINS-Mono and ROVIO respectively. The second experiment demonstrates that when the constraints are used for feedback with efference copies the resulting closed loop system's eigenvalues are invariant to scaling of the applied control signal. We believe these results indicate the $\tau$ and $\Phi$ constraint's potential as the basis of robust and efficient algorithms for a multitude of robotic applications.

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Nonlinear Disturbance Observer-Based Sliding Mode Controller for Underwater Vehicles 14

Nonlinear Disturbance Observer-Based Sliding Mode Controller...

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14th International Conference on Signal Processing Systems, ICSPS 2022

作者： Kong, Shihan Zhang, Ranran Liu, Mingxin Yu, Junzhi College of Engineering Peking University Department of Advanced Manufacturing and Robotics Beijing China PLA92942 Beijing China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China

ISBN: (纸本)9798350336313

To improve the performance of rejecting disturbances for underwater vehicles, a nonlinear disturbance observer-based sliding mode controller is proposed. Specifically, the proposed control framework simultaneously utilizes the insensitivity to the disturbances of the sliding mode controller and the disturbance compensation ability of the disturbance observer. Besides, a series of rigorous mathematical analyses are provided to indicate the stability of the proposed controller. Finally, the results of the simulation demonstrate the feasibility and effectiveness of the proposed controller. © 2022 IEEE.

关键词： Controllers

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Cam-Driven Frequency Up-Conversion Mechanism For Kinetic Energy Harvesting

Cam-Driven Frequency Up-Conversion Mechanism For Kinetic Ene...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Heng Zhao Tianyi Tang Yunfei Li Mingqi Mei Huicong Liu School of Mechanical and Electrical Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou CHINA State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin CHINA

ISBN: (数字)9798350380200

ISBN: (纸本)9798350380217

This paper proposes an ocean wave energy harvester (WEH) with a cam-driven frequency up-conversion mechanism. It utilizes a planar pendulum structure to achieve motion response to ultra-low-frequency wave excitation and then converts the mechanical energy of the pendulum into high-frequency and high-power electrical energy output through the cam-driven frequency upconversion mechanism. WEH’s superior design enables high-power output even under excitation below 1 Hz. At the excitation frequency of 0.75 Hz and resistance load of $20 \Omega$, the maximum power output can reach 461.4 mW. Due to its high output power, the WEH can be integrated into the ocean observation buoy to power the sensors, thereby enabling long-term, unattended operation of the buoy in remote offshore areas.

关键词： Temperature sensors Resistance Ocean waves Frequency conversion Sensor systems Sensors Power systems Wave energy conversion Power generation Ocean temperature

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Motion Planning of Mobile Robot with Non-holonomic Constraints 18

Motion Planning of Mobile Robot with Non-holonomic Constrain...

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18th IEEE Conference on Industrial Electronics and Applications, ICIEA 2023

作者： Zhang, Kewen Liu, Weilong Wang, Jin Chen, Leijie Lu, Guodong Zhejiang University of Technology Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education College of Mechanical Engineering Hangzhou310023 China Robotics Institute of Zhejiang University Hangzhou310027 China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Hangzhou310027 China Robotics Research Center of Yuyao City Ningbo315400 China

ISBN: (纸本)9798350312201

This paper proposes a modified spatio-temporal A∗ path planning algorithm for the trajectory planning of mobile robots with non-holonomic constraints. Most path planning algorithms treat mobile robots as to be with holonomic constraints. However, wheeled mobile robots are affected by non-holonomic constraints, which leads to trajectory unreachability. To solve this problem, an improved spatio-temporal A∗ algorithm based on non-holonomic constraints is given, which considers the efficiency, completeness and smoothness of the path. At the same time, by improving the construction form of collision constraint and adding spatio-temporal constraints to the path, the success rate and solving speed of the path planning algorithm for non-holonomic constrained mobile robot are increased. Finally, simulation experiments are given to indicate that the proposed planning algorithm provides an optimized path, while improving the planning success rate and reducing the computational time. © 2023 IEEE.

关键词： Motion planning

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Proprioceptive-Based Control Strategy to Assist Walking and Carrying Tasks in Back-Support Exoskeletons

Proprioceptive-Based Control Strategy to Assist Walking and ...

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

作者： Vasco Fanti Sergio Leggieri Darwin G. Caldwell Christian Di Natali Department of Advanced Robotics Istituto Italiano di Tecnologia Genova Italy Department of Informatics Bioengineering Robotics and Systems Engineering (DIBRIS) Università degli Studi di Genova (UniGe) Genova Italy

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Back-support exoskeletons are becoming very popular in workplaces to provide assistance to workers during heavy-duty activities and to mitigate risks of work-related injuries. Lifting, lowering, and carrying loads are the most common operations in Manual Material Handling (MMH). Although many back-support exoskeletons have been developed to assist workers in MMH tasks, the primary concern addresses mainly lifting and lowering activities. This work aims to fill the gap in MMH assistance by proposing a control strategy for supporting workers also during walking and carrying. This control strategy for back-support exoskeletons generates assistive torques that comply with the user's natural gait and support the swing and stance phases. The presented Finite State Machine (FSM) algorithm detects successive early swings by relying on the actuators' angular velocities to provide the required assistance. Experiments on eight subjects evaluate the control strategy effectiveness in performing walking and carrying activities. The results on the metabolic consumption show a metabolic cost reduction of 50% when comparing the performance with and without the exoskeleton. In addition, the tests prove that the energy expenditure is comparable during walking tasks and it is lower during carrying tasks despite the additional exoskeleton weight.

关键词： Legged locomotion Actuators Costs Exoskeletons Employment Materials handling Automata Biomechatronics Angular velocity Injuries

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Learning Complicated Manipulation Skills Via Deterministic Policy with Limited Demonstrations

Learning Complicated Manipulation Skills Via Deterministic P...

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International Conference on advanced robotics (ICAR)

作者： Haofeng Liu Jiayi Tan Yiwen Chen Marcelo H Ang Department of Mechanical Engineering National University of Singapore Advanced Robotics Centre College of Design and Engineering National University of Singapore

Deep reinforcement learning, when combined with demonstrations, can effectively formulate policies for manipulators. However, the practical collection of ample high-quality demonstrations is time-consuming, and demonstrations generated by humans may not perfectly correspond with the operational demands of robots. These challenges are intensified by issues such as non-Markovian processes and excessive reliance on demonstrations. Our study indicates that in manipulation tasks, reinforcement learning (RL) agents are sensitive to the quality of demonstrations and struggle to adapt to those derived from humans. As a result, leveraging low-quality or scarce demonstrations to assist reinforcement learning in developing superior policies presents a significant challenge. In some cases, dependence on limited demonstrations may paradoxically impair performance. To address these challenges, we propose a novel algorithm, TD3fG (TD3 learning from a generator). This algorithm facilitates a seamless transition from learning from experts to learning from experience, enabling agents to assimilate prior knowledge while mitigating the negative impacts of the demonstrations. Our algorithm demonstrates notable improvement in the Adroit manipulator and MuJoCo tasks, even with limited demonstrations of mixed failure trajectory.

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Self-Tuning PID Controller for an Arm-Angle Adjustable Quadrotor Using an Actor-Critic-Based Neural Network

Self-Tuning PID Controller for an Arm-Angle Adjustable Quadr...

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Iranian Conference on Intelligent Systems (ICIS)

作者： Arshia Rezaei Amin Talaeizadeh Aria Alasty Department of Mechanical Engineering Advanced Research Lab for Control and Agricultural Robotics (Sharif AgRoLab) Sharif University of Technology Tehran Iran

ISBN: (数字)9798331507565

ISBN: (纸本)9798331507572

The increasing prevalence of multirotors necessitates the development of new configurations for specific applications. Innovative morphing of multirotors has enhanced their stability, maneuverability, agility, and overall performance. The utilization of Artificial Intelligence (AI) in robotics has enabled robots to exhibit autonomous behaviors in areas such as control, path planning, state estimation, system identification, and image processing. This paper introduces a novel approach to improving the control system of an adjustable arm-angle quadrotor through the integration of a self-tuning Proportional-Integral-Derivative (PID) controller, utilizing an Actor-Critic-based Neural Network (NN). Arm-angle adjustable quadrotors, with their complex and variable dynamics, present challenges in achieving optimal control performance across different arm angles. Traditional PID controllers often struggle to adapt efficiently to these dynamics. To address this issue, the Actor-Critic framework from reinforcement learning is employed to develop a self-tuning mechanism for the PID controller. The Actor network learns the optimal control actions, while the Critic network evaluates the performance of the control system, enabling continuous improvement. Extensive Software-in-the-Loop (SIL) and Hardware-in-the-Loop (HIL) tests demonstrate the effectiveness and feasibility of the proposed approach in achieving robust and adaptive control for tilt-arm quadrotors under diverse operating conditions. The results highlight the superiority of the Actor-Critic-based self-tuning PID controller over conventional methods, showcasing its potential for real-world devlopment in autonomous aerial systems.

关键词： Uncertainty Optimal control Reinforcement learning Artificial neural networks Autonomous aerial vehicles Real-time systems Adaptive control Robots Tuning Quadrotors

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A Wave Energy Harvester With Vertical Pendulum And Mag-Boost Mechanism

A Wave Energy Harvester With Vertical Pendulum And Mag-Boost...

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Micro and Nanotechnology for Power Generation and Energy Conversion Applications (PowerMEMS)

作者： Tianyi Tang Yunfei Li Heng Zhao Lining Sun Huicong Liu State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin CHINA School of Mechanical and Electrical Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou CHINA

ISBN: (数字)9798350380200

ISBN: (纸本)9798350380217

This paper presents an ultra-low-frequency and high-power wave energy harvester (WEH). It utilizes a vertical pendulum structure to achieve motion response to ultra-low-frequency wave excitation and then converts the mechanical energy of the pendulum into high-frequency and high-power electrical energy output through the MagBoost Mechanism. The design of WEH ensures stable output even under excitation conditions below 1 Hz. At the excitation frequency of 1.4 Hz and resistance load of $20 \Omega$, the maximum power output can reach 1.16 W with a power density of $0.54 \mathrm{~mW} / \mathrm{cm}^{3}$. The high energy density allows stable power supplementation for ocean observation buoy within a limited volume, thereby enabling long-term, unattended operation of the buoy in remote offshore areas.

关键词： Resistance Power system measurements Density measurement Oceans Frequency conversion Sensor systems Sensors Mechanical energy Wave energy conversion Power generation

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