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12th International Conference on CYBER Technology in automation, Control, and Intelligent Systems, CYBER 2022

作者： Li, Wayne Jason Tian, Yuqing Kwok, Chun Xi, Ning St. Stephen's College Stanley Hong Kong Department of Industrial and Manufacturing Systems Engineering The University of Hong Kong Hong Kong Robotics and Automation The University of Hong Kong Hong Kong

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

For more than a decade, researchers have been attempting to synchronize humanoid robot motion with musical beats, including incorporating 'emotion' of the music into the movement of robots. However, the cost the humanoid robots capable of executing motions using real-time recognition of musical notes is typically in the order of thousands of US$, which is extremely difficult for students to explore as 'education robots', especially for students from the developing or underdeveloped countries around the world. We report here our work on exploring the possibility of applying the 'robot-music synchronization' technology to an affordable robot, i.e., US$50, to proliferate the educational value of this concept. We have chosen the mBot as the educational robot platform and showed that it can recognize the frequency of the musical sound signals in real-time and subsequently control the robot's LED (light-emitting diode) colors and motor functions for three different musical sound sources: 1) individual musical notes, 2) a rock-n-roll song, and 3) a classical music song. The limitations of the sound sensor used to collect real-time musical sound input and the deficiencies of the mBot's computing power are discussed in this paper. © 2022 IEEE.

关键词： Anthropomorphic robots

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Improving Road Safety With Enhanced Vehicle Detection for Night-time Traffic in Mirpur

Improving Road Safety With Enhanced Vehicle Detection for Ni...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Usama Younis Faisal Riaz Umar Raza Gokhan Atali Hamid Rabnawaz Control Automotive and Robotics Lab Affiliated Lab of National Centre of Robotics and Automation (NCRA) HEC Islamabad Pakistan Department of Engineering Manchester Metropolitan University Manchester United Kingdom Mechatronics Engineering Sakarya University of Applied Sciences Sakarya Turkey Selftech Pvt Ltd

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

This paper presents a study on improving night-time vehicle detection in the Mirpur region through a modified neural network model. Given the challenges of low visibility at night, the research focused on optimizing a deep learning algorithm to enhance detection accuracy. A dataset of 10,000 images covering various vehicle types was collected and annotated to train and test the model. The study found a 7% increase in detection performance compared to the standard model, highlighting the potential for tailored algorithms in specific environmental conditions. The findings suggest significant implications for road safety and autonomous driving systems, particularly in regions with similar night-time driving challenges. The research contributes to the broader field of computer vision by showing the effectiveness of model optimization in real-world conditions.

关键词： Accuracy Computational modeling Vehicle detection Neural networks Road safety Reliability robotics and automation Optimization Autonomous vehicles Standards

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An Energy-Efficient Communication Protocol for Power-Constrained IoT Networks: A Deep Reinforcement Learning Approach

An Energy-Efficient Communication Protocol for Power-Constra...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Syed Asad Ullah Syed Muhammad Khalid Umair Ahmed Korai Anayat Ullah Department of Electronic Engineering Balochistan University of Information Technology Engineering and Management Sciences (BUITEMS) Quetta Pakistan Control Automotive and Robotics Lab National Centre of Robotics and Automation (NCRA) BUITEMS Quetta Pakistan Department of Telecommunication Engineering Mehran University of Engineering and Technology Jamshoro Pakistan

Power-limited devices (or sensors) constrain the deployment of modern IoT networks, such as Next-Generation Industrial IoT (NG-IIoT). These networks are envisioned as the key enablers to facilitate connectivity to billions of devices for applications like smart industries, smart healthcare, etc. This paper presents an optimal up-link communication protocol for a power-limited unlicensed sensor operating among numerous licensed sensors communicating in a time division multiple access (TDMA)-based scheme. The transmission of the power-limited sensor is ensured by employing the non-orthogonal multiple access (NOMA) technique during the time slot of a licensed sensor. To ensure energy-efficient communication, we maximize the throughput of the power-limited sensor using a deep reinforcement learning (DRL) framework recognized as a combined experience replay deep deterministic policy gradient (CER-DDPG) algorithm. Our simulation results demonstrated that the CER-DDPG-based communication protocol outperforms the benchmark schemes, such as DDPG and stochastic algorithms, in terms of throughput.

关键词： Optical losses NOMA Protocols Simulation Reinforcement learning Throughput Energy efficiency

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Fault Type Classification in the Electric Distribution System Integrated Electric Vehicle Charging Demand

Fault Type Classification in the Electric Distribution Syste...

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International Electrical engineering Congress (iEECON)

作者： Nuttapong Bootthanu Chaiphan Prakaraphan Banyat Boribun Phaiboon Boupha Nopparat Thammawongsa Niwat Bhumiphan Department of Smart Electronics and Electric Vehicles Udon Thani Rajabhat University Udon Thani Thailand Department of Robotics and Automation Engineering Udon Thani Rajabhat University Udon Thani Thailand

ISBN: (数字)9798350383591

ISBN: (纸本)9798350383607

With the growing popularity of electric vehicles (EVs) and the integration of EV charging infrastructure into distribution systems, the need for efficient fault classification techniques has become paramount. This paper proposes a comprehensive approach for fault classifications in distribution systems that incorporate EV charging. To address this challenge, a fault classification algorithm by using differential evolution (DE) was proposed. By considering multiple data sources, including voltage measurements, and current waveforms, the algorithm achieves a high level of accuracy and robustness in fault classification. The results demonstrate its effectiveness in accurately identifying different fault types, including short circuits between line and line-to-ground faults. In conclusion, the proposed algorithm offers an effective solution for the accurate and fastly identification of various fault types, enabling utilities and operators to enhance the reliability and resilience of distribution systems in the context of increasing EV adoption.

关键词： Fault diagnosis Voltage measurement Soft sensors Reliability engineering Electric vehicle charging Robustness Classification algorithms

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Enhancing Human-Drone Interaction with Human-Meaningful Visual Feedback and Shared-Control Strategies

Enhancing Human-Drone Interaction with Human-Meaningful Visu...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Riccardo Franceschini Matteo Fumagalli Julian Cayero Becerra Eurecat Centre Tecnològic de Catalunya Robotics and Automation Unit Barcelona Spain Department of Electrical Engineering Automation and Control Group Danish Technical University Lyngby Denmark

Recent developments in the capabilities of unmanned aerial vehicles (UAVs) have made them suitable for use in various industrial settings. Their ability to access difficult and remote locations, as well as providing remote manipulation and visual inspection capabilities, make them valuable for various industrial applications. However, operating UAVs can be challenging, particularly in cluttered environments. This research aims to enhance the teleoperation experience by providing human-meaningful information on the remote user interface, thereby improving the operator’s situational awareness. Shared autonomy routines utilizing the previously collected information are also developed to further assist the operator with challenging control tasks. The proposed system has been tested in simulated environments and on actual hardware.

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Design and Develop a Vertical Box Smart Farm based on Programmable Logic Controller

Design and Develop a Vertical Box Smart Farm based on Progra...

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International Conference on Advanced robotics and Mechatronics (ICARM)

作者： Thananop Tummawai Peeraput Prakhummin Kittisak Ledkaew Anocha Phongsatja Witayakorn Lunsungyang Somboon Sukpancharoen Department of Mechanical Engineering Khon Kaen University Khon Kaen Thailand Department of Automation Robotics and Intelligent Systems Khon Kaen University Khon Kaen Thailand Department of Agricultural Engineering Khon Kaen University Khon Kaen Thailand

ISBN: (数字)9798350385724

ISBN: (纸本)9798350385731

Agriculture plays an indispensable role in daily life, and presently, soilless cultivation techniques such as hydroponics enjoy widespread popularity. In efforts to advance this methodology, we have implemented a sophisticated Programmable Logic Controllers (PLCs) system tailored for Nutrient Film Technique (NFT) hydroponic cultivation within a greenhouse setting. This system incorporates a diverse array of sensors designed to measure crucial parameters including water temperature, Electrical Conductivity (EC), and Potential of Hydrogen ion ($\mathbf{p H}$) levels. The PLCs is meticulously programmed to oversee and regulate the entire cultivation process seamlessly. Additionally, integration of a Raspberry Pi facilitates Internet of Things (IoT) functionalities, thereby enhancing convenience and enabling remote monitoring capabilities. We aim to conduct a comparative analysis between two distinct greenhouse systems: one under the control of the PLC, and the other devoid of such automated control, in order to discern and evaluate the disparities in plant growth and development between them. The study focuses on key variables such as canopy width and leaf count of Green Oak Lettuce over a 5 -week period, with the primary environmental factors influencing these variables being the levels of EC and pH in the nutrient solution. The findings of this study will provide valuable insights into the benefits of environmental control in greenhouse-based hydroponic cultivation of green oak lettuce, contributing to the advancement of sustainable and efficient agricultural practices.

关键词： Temperature sensors Temperature measurement Green products Programmable logic devices Hydroponics Nutrients Stability analysis Internet of Things Remote monitoring Sensor arrays

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Renderable Street View Map-Based Localization: Leveraging 3D Gaussian Splatting for Street-Level Positioning

Renderable Street View Map-Based Localization: Leveraging 3D...

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

作者： Howoong Jun Hyeonwoo Yu Songhwai Oh Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of Department of Intelligent Robotics & Mechanical Engineering Sungkyunkwan University (SKKU) Suwon-si Korea Republic of Department of Electrical and Computer Engineering (ECE) & Interdisciplinary Program in Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce a new method that first utilizes 3D Gaussian splatting in street-level localization problem. Robust localization with street-level real-world images such as street view is a major issue for autonomous vehicle, augmented reality (AR) navigation, and outdoor mobile robots. The objective is to determine the position and orientation of a query image that matches a street view database composed of RGB images. However, given the limited information available in the street view images, accurately determining the location solely based on this data presents a significant challenge. To address this challenge, we propose a novel method called renderable street view map-based localization (RSM-Loc). This approach enhances the localization process by augmenting 2D street view images into a renderable 3D map using 3D Gaussian splatting, to resolve street-level localization problems. Upon receiving a query RGB image without geometry information, the proposed method renders 2D images from a pre-made renderable map and compares image pose similarities between the rendered images and the query image. Through iterations of this process, the proposed method eventually estimates the pose of the given query image. The experimental results demonstrate that RSM-Loc outperforms the baselines with neural-field-based localization. Additionally, we conduct deep analysis on the proposed method to show that our method can serve as a new concept for the street-level localization problem.

关键词： Location awareness Visualization Matched filters Three-dimensional displays Image resolution Navigation Noise measurement Mobile robots Intelligent robots Image reconstruction

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Implementing Genetic Algorithms for Optimization in Neuro-Cognitive Rehabilitation robotics 1

Implementing Genetic Algorithms for Optimization in Neuro-Co...

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1st IEEE International Conference on Cognitive robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Abhang, L.B. Changala, Ravindra Ghosh, Anudeb Manage, Prabhakar S Rao, Vuda Sreenivasa Kiran Bala, B. Loni Under. SPPU University Pravara Rural Engineering College Automation and Robotics Dept Pune India Guru Nanak Institutions Technical Campus Department of Information Technology Hyderabad India Siksha - Bhavana Department of Environmental Studies Visva-Bharati Santiniketan India Kle Technological University's Msscet Belagavi Campus Department of Electronics and Communication Karnataka Belagavi India Education Foundation Department of Computer Science and Engineering Koneru Lakshmaiah A.P Vaddeswaram522302 India K.Ramakrishnan College of Engineering The Department Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350372748

The study explores the intersection of robotics and neuro-cognitive rehabilitation to offer personalized assistance for individuals with neurological disorders, emphasizing autism spectrum disorder (ASD). The proposed methodology integrates Genetic Algorithms (GAs) to optimize the performance of robotic interventions. The data collection phase involves compiling a comprehensive image dataset capturing facial expressions, gestures, and relevant visual cues associated with ASD. Leveraging robotic platforms designed for therapeutic interventions, feature extraction techniques identify intricate patterns within the data. Advanced algorithms, including GAs, classify the dataset into positive (ASD) and negative (non-ASD) categories. The framework introduces a Diagnosis Matrix for enhanced diagnostic precision, correlating observed robotic interactions with clinical assessments. An Ontology Knowledge base adapts responses based on evolving patient needs. The proposed method surpasses all others, achieving an accuracy of 95.08% and demonstrating superior precision, recall, and F1-score metrics. This indicates the efficacy of the proposed approach in achieving a well-balanced performance with high accuracy and robustness in correctly identifying positive instances. The results underscore the potential of the proposed method for classification tasks, showcasing its superiority in comparison to traditional SVM, CNN, and even a well-established deep learning architecture like VGG-16. The Receiver Operating Characteristic curve validates the model's discriminatory power. © 2024 IEEE.

关键词： Genetic algorithms

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无人飞行器在不确定扰动下的动态目标跟踪

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engineering 2024年第4期35卷 74-85页

作者： Yanjie Chen Yangning Wu Limin Lan Hang Zhong Zhiqiang Miao Hui Zhang Yaonan Wang School of Mechanical Engineering and Automation Fuzhou UniversityFuzhou 350108China Department of Computer Science Aberystwyth UniversityAberystwyth SY233DBUK School of Robotics Hunan UniversityChangsha 410082China College of Electrical and Information Engineering Hunan UniversityChangsha 410082China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan UniversityChangsha 410082China

This study proposes an image-based visual servoing(IBVS)method based on a velocity observer for an unmanned aerial vehicle(UAV)for tracking a dynamic target in Global Positioning System(GPS)-denied *** proposed method derives the simplified and decoupled image dynamics of underactuated UAVs using a constructed virtual camera and then considers the uncertainties caused by the unpredictable rotations and velocities of the dynamic target.A novel image depth model that extends the IBVS method to track a rotating target with arbitrary orientations is *** depth model ensures image feature accuracy and image trajectory smoothness in rotating target *** relative velocities of the UAV and the dynamic target are estimated using the proposed velocity *** to the velocity observer,translational velocity measurements are not required,and the control chatter caused by noise-containing measurements is *** integral-based filter is proposed to compensate for unpredictable environmental disturbances in order to improve the antidisturbance *** stability of the velocity observer and IBVS controller is analyzed using the Lyapunov *** simulations and multistage experiments are conducted to illustrate the tracking stability,anti-disturbance ability,and tracking robustness of the proposed method with a dynamic rotating target.

关键词： Unmanned aerial vehicle Visual servoing Velocity observer Target tracking

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Transfer Learning: An Approach on the Riemannian Manifold for Multiclass Motor Imagery EEG Brain-Computer Interface

Transfer Learning: An Approach on the Riemannian Manifold fo...

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

作者： Xu Sun Yaqi Chu Yang Luo Xingang Zhao Department of Control Engineering Northeastern University Shenyang China State Key Laboratory of Robotics Shenyang Institute of Automation (SIA) Chinese Academy of Sciences (CAS) Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences (CAS) Shenyang China

ISBN: (数字)9798350391916

ISBN: (纸本)9798350391923

Motor imagery (MI) EEG-based brain-computer interface (BCI) facilitates direct communication between the brain’s intentions and a computer. In essence, it allows a person to control external equipment by decoding EEG signals during the mental imagery of limb movements. However, due to the intricate individual variances in EEG signals, creating a general decoding model with parameters applicable to all subjects is exceptionally challenging. Consequently, a prolonged calibration process is necessary to gather labeled subject-specific data for each individual, making it less user-friendly. A potential solution to this problem is transfer learning, a methodology that transfers knowledge from related domains to a target domain. To tackle the problem, this paper proposes a novel transfer learning approach on the Riemannian manifold framework in the context of multiclass MI EEG-based BCI classification. Particularly, a user-specific frequency band selection (FBS) method with MI EEG class distinctiveness is utilized to improve the accuracy and efficiency of Riemannian space calculations, which is measured using the inter-class distance and intra-class variance on the manifold. Then, the Riemannian space Alignment (RA) strategy is used to calibrate the MI EEG variances of different subjects. The comparative experiments between the proposed approach and baseline/conventional methods are conducted on a public dataset with four-class motor imagery EEG, including two-class transfer learning scenario and four-class transfer learning scenario. proposed transfer learning approach is outperformed. Compared with the baseline/conventional methods using a fixed wide frequency band, the preliminary results suggests that the proposed approach can significantly improve the transfer learning performance for the MI EEG signals from different subjects in Riemannian space.

关键词： Manifolds Mechatronics Machine vision Transfer learning Motors Particle measurements Electroencephalography Brain-computer interfaces Frequency measurement Decoding

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