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Design and Implementation of a 6LoWPAN-Based Lightweight Wireless Embedded Internet Platform for IoT Applications

Journal of Internet technology 2023年第2期24卷 323-332页

作者： Xu, Sendren Sheng-Dong Gede Darco Pancev, I. Baihaqi, Muhammad Yeza Graduate Institute of Automation and Control National Taiwan University of Science and Technology Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taiwan

The Internet of Things (IoT) plays an important role in the revolution of the Internet with the rise of Industry 4.0. It is closely related to low-power embedded devices, which have not been entirely IP-enabled until now. The 6LoWPAN (acronym of IPv6 over low-power wireless personal area networks) is an IPv6 protocol for Wireless Embedded Internet. It can help enable even the minimum embedded systems with limited processing capabilities to participate in the IoT. This study investigates a reliable way of future IoT networking by designing and implementing a 6LoWPAN-based lightweight Wireless Embedded Internet platform, named Lowvy (abbreviated from low-power wireless connectivity). Lowvy is characterized by low power consumption, constrained memory, small size, a low-cost IPv6-enabled wireless embedded device, and a constrained application protocol (CoAP). We did different application experiments to test the performance of the platform in terms of received signal strength indicator (RSSI), power consumption, sensor integration, and internet integration application with 6LoWPAN. Comparing Lowvy with other wireless embedded IoT platforms, Lowvy is less expensive and more compact. Moreover, Lowvy has a powerful performance in terms of signal strength for a longer wireless distance transmission of about 25 to 30 meters and has a lower power consumption of 41.60 milliwatts. © 2023 Taiwan Academic Network Management Committee. All rights reserved.

关键词： Internet of things

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Dffraction-Based Overlay Metrology with Optical Convolution Layer

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SSRN 2023年

作者： Li, Jinyang Kuo, Hung-Fei Graduate Institute of Automation and Control The National Taiwan University of Science and Technology Taiwan Advanced Manufacturing Research Center National Taiwan University of Science and Technology Taiwan

Overlay is a crucial indicator of manufacturing processing between layers. Currently, diffraction-based overlay(DBO) is widely adopted in overlay measurement. In response to existing challenges in DBO metrology, this study applied the concept of optical computing to establish a DBO metrology with an optical convolution layer that can predict the overlay of a DBO target, thereby providing a promising in-line measurement method that increases the intuitiveness and efficiency of overlay measurements. In this study, pupil images from DBO metrology were directly output to the optical convolution layer and integrated with subsequent CNN layers in electronic hardware. A total of 630 pupil images with 21 types of designed overlay were generated for network training and testing. The experimental testing results revealed that the root mean square error of the predicted values of 90 pieces of testing pupil image data was 0.17 nm. The completed optical convolution layer is the fundamental unit for establishing an optical interconnection with the functions of weighting and matrix summation, which will contribute to further exploration of the DBO technique. © 2023, The Authors. All rights reserved.

关键词： Convolution

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Advancement in Smart Cane technology: Enhancing Mobility for the Visually Impaired Using ROS and LiDAR

Advancement in Smart Cane Technology: Enhancing Mobility for...

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International Conference on robotics and Artificial Intelligence (ICRAI)

作者： Saqib Zafar Hafiz Farhan Maqbool Naveed Ahmad Arham Ali Abdul Moeizz Faizan Ali Juri Taborri Stefano Rossi Department of Economics Engineering Society and Business Organization University of Tuscia Viterbo VT Italy Department of Mechanical Mechatronics and Manufacturing Engineering University of Engineering and Technology Lahore (Faisalabad Campus) Pakistan Human-Centred Robotics Lab National Center of Robotics and Automation Pakistan

ISBN: (数字)9798350355703

ISBN: (纸本)9798350355710

Visual impairment affects the ability to navigate independently in an unknown environment due to reliance on human/dog assistance, potential miscommunication, and discomfort during mobility. To make the visually impaired (VI) individuals self-reliant and confident in their mobility the proposed system has been developed that contains a light detection and ranging (LiDAR) sensor for obstacle detection and two omni-wheels coupled with the DC gear encoder motors for precise movement in any direction without lifting the cane. Hector Simultaneous Localization and Mapping (SLAM), A *, and steering wheel algorithms have been implemented in the robot operating system (ROS) for real-time environment mapping, efficient path planning, and navigating in an unknown indoor static environment, respectively. The Raspberry Pi 3B+ and Arduino Mega 2560 are used in the master-slave configuration for decision-making on path planning and controlling the maneuverability of the system in real-time. The proposed system in the paper is successfully tested on one subject in two indoor environments. The results demonstrate that the system serves as an assistive mobile robot, helping the VI individual to navigate confidently in an unknown environment and reach the destination without assistance.

关键词： Simultaneous localization and mapping Laser radar Navigation Operating systems Decision making Visual impairment Wheels Real-time systems Path planning robotics and automation

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Ant Colony Algorithm and Grey Relation Theory for Resources Optimal Allocation

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SSRN 2024年

作者： Huang, Lang Zhuang, Mengzhen Tang, Hongtao Guo, Shunsheng Wei, Shupeng Zhang, Liang Lei, Deming Hubei Digital Manufacturing Key Laboratory School of Mechanical and Electronic Engineering Wuhan University of Technology Wuhan430070 China Hubei Provincial Engineering Research Center of Robotics and Intelligent Manufacturing Wuhan430070 China School of Automation Wuhan University of Technology China Wuhan430070 China

Optimizing enterprise resource management is crucial for enhancing production efficiency and cost reduction, especially in the building materials and equipment manufacturing industry, due to its large-scale, small-batch, multi-variety, and multi-agent *** paper uses a hybrid intelligent algorithm to solve the resource optimization problem, and successfully applies it in practical cases. By using fuzzy clustering, we first establish a multi-objective, multi-agent manufacturing equipment resource allocation model. Then we present a solution based on grey correlation and an improved ant colony algorithm. Finally, the effectiveness and feasibility of the proposed method are validated by experimental results, which show the optimized allocation system can significantly enhance production efficiency and reduce costs, for resource management in the building materials and equipment manufacturing industry. © 2024, The Authors. All rights reserved.

关键词： Production efficiency

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An Untethered Soft Robotic Gripper with Adjustable Grasping Modes and Force Feedback

An Untethered Soft Robotic Gripper with Adjustable Grasping ...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Yang, Yang Zhu, Honghui Liu, Jia Li, Yingtian Zhou, Jianshu Ren, Tao Ren, Yi School of Automation Nanjing University of Information Science and Technology Jiangsu Nanjing210044 China Jiangsu Province Engineering Research Center of Intelligent Meteorological Exploration Robot China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China The Chinese University of Hong Kong Department of Mechanical and Automation Engineering Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Hong Kong School of Mechanical and Electrical Engineering Chengdu University of Technology Chengdu610059 China Advanced Manufacturing Lab Huawei Technologies Shenzhen China

ISBN: (纸本)9781665481090

In this paper, we propose a three-finger soft robotic gripper, which adopts an untethered and under-actuated design with simple structure and easy operation. The rotation of two servomotors is used to control the positions of the fingers, which provides the fingers with adjustable grasping modes and improves the adaptation of the gripper to objects with different shapes. A pressure sensor is integrated at the end of the finger to provide force feedback, which is used to detect the contact force when grasping the object and enables the gripper to provide the proper grasping force. The experimental section characterizes the bending of the soft robotic finger, and the gripper is integrated at the end of a collaborative robotic arm to demonstrate its adjustable grasping posture and force feedback function. The soft robotic gripper is expected to be used in a variety of scenarios, such as service robot, food industry or garbage sorting. © 2022 IEEE.

关键词： Grippers

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UAV Forest Fire Detection based on RepVGG-YOLOv5

UAV Forest Fire Detection based on RepVGG-YOLOv5

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Song, Kang Zhang, Yueyuan Lu, Bo Chi, Wenzheng Sun, Lining Department of Automation Engineering School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Ningbo Institute of Materi-als Technology & Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Soochow University Robotics and Microsystems Center School of Mechanical and Electrical Engineering Suzhou215137 China

ISBN: (纸本)9781665481090

The safety of forest resources is of great im-portance to natural and public safety, and the efficient and accurate detection of forest fires is an issue of close concern. Considering the limitations of traditional forest fire detection methods, such as the limited range of view from watchtowers and insufficient resolution of satellite images, a deep learning-based Unmanned Aerial Vehicle (UAV) fire detection system has been proposed. With its high mobility, images of forest fires can be captured by camera-equipped UAV and transmitted to the ground station in real time. The ground station uses a deep learning object detection algorithm to achieve the recognition of fire. In this paper, we choose the state-of-the-art object detection algorithm YOLOv5 for the UAV forest fire detection scenario, and to further improve the detection performance of YOLOv5, we propose RepVGG-YOLOv5 by drawing on the RepVGG network, a training-time multi-branch topology with an inference-time plain architecture. Then we test the models on a self-built dataset, and the experimental results show that the improved RepVGG-YOLOv5 model outperforms the YOLOv5 model in terms of detection performance and detection speed, which proves the feasibility of this paper's model in the UAV forest fire detection systems and the effectiveness of the model improvement. Furthermore, the great potential of this paper's object detection model for real-time applications of accurate forest fire detection has also been demonstrated. © 2022 IEEE.

关键词： Object detection

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Automated Disassembly Sequence Prediction for Industry 4.0 Using Enhanced Genetic Algorithm

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Computers, Materials & Continua 2021年第11期69卷 2531-2548页

作者： Anil Kumar Gulivindala M.V.A.Raju Bahubalendruni R.Chandrasekar Ejaz Ahmed Mustufa Haider Abidi Abdulrahman Al-Ahmari Department of Mechanical Engineering Industrial Robotics and Manufacturing Automation LaboratoryNational Institute of TechnologyKariakal609609India Department of Computer Science Engineering National Institute of TechnologyKariakal609609India Raytheon Chair for Systems Engineering Advanced Manufacturing InstituteKing Saud UniversityRiyadh11421Saudi Arabia Industrial Engineering Department College of EngineeringKing Saud UniversityRiyadh11421Saudi Arabia

The evolution of Industry 4.0 made it essential to adopt the Internet of Things(IoT)and Cloud Computing(CC)technologies to perform activities in the new age of *** technologies enable collecting,storing,and retrieving essential information from the manufacturing *** collected at sites are shared with others where execution automatedly *** obtained information must be validated at manufacturing to avoid undesirable data losses during the de-manufacturing ***,information sharing from the assembly level at the manufacturing stage to disassembly at the product end-of-life state is a major *** current research validates the information optimally to offer a minimum set of activities to complete the disassembly *** optimal disassembly sequence plan(DSP)can possess valid information to organize the necessary actions in ***,finding an optimal DSP is complex because of its combinatorial *** genetic algorithm(GA)is a widely preferred artificial intelligence(AI)algorithm to obtain a near-optimal solution for the DSP *** converging nature at local optima is a limitation in the traditional *** study improvised the GA workability by integrating with the proposed priori crossover *** optimality function is defined to reduce disassembly effort by considering directional changes as *** enhanced GA method is tested on a real-time product to evaluate the *** obtained results reveal that diversity control depends on the operators employed in the disassembly *** proposed method’s solution can be stored in the cloud and shared through IoT devices for effective resource allocation and disassembly for maximum recovery of the *** effectiveness of the proposed enhanced GA method is determined by making a comparative assessment with traditional GA and other AI methods at different population sizes.

关键词： automation internet of things disassembly sequence planning priori cross over operator enhanced GA disassembly predicates

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Usability Evaluation of FURS Robot Control Panel Interface Design Based on SUS 16th

Usability Evaluation of FURS Robot Control Panel Interface D...

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16th International Conference on Intelligent robotics and Applications, ICIRA 2023

作者： You, Yong Wei, Lai Zhu, Shiqiang Yu, Yongwu Jiang, Yunxiang Liu, Xiaolei Han, Jianda Chen, Lingkai Song, Wei Design Innovation Center China Academy of Art Hangzhou310024 China L’École de Design Nantes Atlantique China Academy of Art Hangzhou310024 China Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Hangzhou Yunphant Network Technology Co. Ltd. Hangzhou China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China College of Artificial Intelligence and Tianjin Key Laboratory of Intelligent Robotics Institute of Robotics and Automatic Information Systems Nankai University Tianjin300350 China

ISBN: (纸本)9789819964949

The objective of this study is to compare the interfaces of two FURS (Flexible Ureterorenoscopy) robotic operation consoles across generations. Analyzing functional requirements during surgery, the first-generation interface was designed. Evaluation of the first-generation interface, combined with user needs and design principles, informed the design of the second-generation interface, which aimed to improve the usability of the console during surgery. Both interface generations were assessed for usability and user experience using the System Usability Scale (SUS).The results indicate improvement in usability and user satisfaction following interface iteration. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Surgery

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Robust tensor decomposition via orientation invariant tubal nuclear norms

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Science China(Technological Sciences) 2022年第6期65卷 1300-1317页

作者： WANG AnDong ZHAO QiBin JIN Zhong LI Chao ZHOU GuoXu School of Automation Guangdong University of TechnologyGuangzhou 510006China School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Tensor Learning Team RIKEN Center for Advanced Intelligence ProjectTokyo 103-0027Japan Key Laboratory of Intelligent Perception and System for High-Dimensional Information Ministry of EducationNanjing 210094China Key Laboratory of Intelligent Detection and the Internet of Things in Manufacturing Ministry of EducationGuangzhou 510006China

Aiming at recovering an unknown tensor(i.e.,multi-way array)corrupted by both sparse outliers and dense noises,robust tensor decomposition(RTD)serves as a powerful pre-processing tool for subsequent tasks like classification and target detection in many computer vision and machine learning ***,tubal nuclear norm(TNN)based optimization is proposed with superior performance as compared with other tensorial nuclear norms for tensor ***,one major limitation is its orientation sensitivity due to low-rankness strictly defined along tubal orientation and it cannot simultaneously model spectral low-rankness in multiple *** this end,we introduce two new tensor norms called OITNN-O and OITNN-L to exploit multi-orientational spectral low-rankness for an arbitrary K-way(K≥3)*** further formulate two RTD models via the proposed norms and develop two algorithms as the ***,we establish non-asymptotic error bounds which can predict the scaling behavior of the estimation *** on real-world datasets demonstrate the superiority and effectiveness of the proposed norms.

关键词： tensor recovery t-SVD estimation error tensor completion

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Observer-based Fixed-time Attitude Tracking Control of Rigid Spacecraft with Output Constraints

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IEEE Transactions on automation Science and Engineering 2025年 22卷 15878-15888页

作者： Shen, Ganghui Cui, Bing Felicetti, Leonard Xia, Yuanqing Huang, Panfeng Northwestern Polytechnical University Research Center for Intelligent Robotics School of Astronautics Xi’an 710072 China Beijing Institute of Technology School of Automation Beijing 100081 China Cranfield University School of Aerospace Transport and Manufacturing Bedford Cranfield MK43 0AL United Kingdom

This paper investigates the fixed-time attitude tracking control problem for rigid spacecraft subject to external disturbance and output constraints. First, the state transformed function (STF) technique is employed to convert the constrained spacecraft error dynamics into an unconstrained one. Subsequently, a fixed-time disturbance observer (FXTDO) is designed to estimate and reconstruct the lumped disturbance of unconstrained system. Combined with the developed STF, FXTDO and fixed-time integral terminal sliding mode (FITSM) surface techniques, the proposed fixed-time control law provides zero-error attitude tracking with high control precision and chattering avoidance, while the output constraints are never transgressed. The fixed-time stability of the closed-loop system is conducted via the Lyapunov technique and bi-limit homogeneity theory, and the expression of convergence time is also presented. Simulations illustrate the efficiency of the investigated controller. © 2004-2012 IEEE.

关键词： Fixed-time disturbance observer Fixed-time integral terminal sliding mode Output constraints Rigid spacecraft Zero-error attitude tracking

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