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2021 5th international conference on automation, Control and Robots, ICACR 2021

2021 5th International Conference on Automation, Control and...

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5th international conference on automation, Control and Robots, ICACR 2021

ISBN: (纸本)9781665438490

the proceedings contain 34 papers. the topics discussed include: design and implementation of novel power electronic transformer for smart grid;fault tolerant control for a quad-rotor subjected to partial single-actuator fault;nonlinear model predictive control using state estimation for stabilization of vehicle dynamics to avoid the secondary accident;design, analysis and obstacle avoidance control of a smart wheelchair;design of fractional order controller for PMSM based on the improved particle swarm optimization algorithm;a novel reversal method for measurement of rotary manipulator geometric errors;and defect classification using pressure change of sleeve soldering machine.

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Tomato Fruit Maturity Detection Method Based on YOLOV4 and Statistical Color Model 11

Tomato Fruit Maturity Detection Method Based on YOLOV4 and S...

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11th IEEE Annual international conference on CYBER Technology in automation, Control, and Intelligent systems, CYBER 2021

作者： Zhou, Xiaoliang Wang, Pengbo Dai, Guanglin Yan, Jiawen Yang, Zhan School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou215123 China

ISBN: (纸本)9781665425278

the ripening information of tomato fruit in greenhouse environment is closely related to production operation. Currently, greenhouse tomato ripening information is mainly carried out by manual inspection. In this paper, we present a new real-Time method of tomato fruit detection and its maturity measurement in natural greenhouse. More than 25,000 tomato fruits were tested in 1005 images by Yolov4 and the identification accuracy was 95%. After construction of single fruit data set, the color proportion analysis of RGB color features was analyzed. Combined with normalization and noise reduction processing of K-means clustering, we proposed the maturity detection method based on the proportion of R component in RGB space. Experiment results of scouting robot showed that the recognition and detection speed is 5-6 frames/s. © 2021 IEEE.

关键词： Greenhouses

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Embodied Neuromorphic Artificial Intelligence for robotics: Perspectives, Challenges, and Research Development Stack

Embodied Neuromorphic Artificial Intelligence for Robotics: ...

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international conference on Control, automation, robotics and Vision (ICARCV)

作者： Rachmad Vidya Wicaksana Putra Alberto Marchisio Fakhreddine Zayer Jorge Dias Muhammad Shafique eBrain Lab Division of Engineering New York University (NYU) Abu Dhabi United Arab Emirates Khalifa University United Arab Emirates

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

Robotic technologies have been an indispensable part for improving human productivity since they have been helping humans in completing diverse, complex, and intensive tasks in a fast yet accurate and efficient way. therefore, robotic technologies have been deployed in a wide range of applications, ranging from personal to industrial use-cases. However, current robotic technologies and their computing paradigm still lack embodied intelligence to efficiently interact with operational environments, respond with correct/expected actions, and adapt to changes in the environments. Toward this, recent advances in neuromorphic computing with Spiking Neural Networks (SNN) have demonstrated the potential to enable the embodied intelligence for robotics through bio-plausible computing paradigm that mimics how the biological brain works, known as “neuromorphic artificial intelligence (AI)”. However, the field of neuromorphic AI-based robotics is still at an early stage, therefore its development and deployment for solving real-world problems expose new challenges in different design aspects, such as accuracy, adaptability, efficiency, reliability, and security. To address these challenges, this paper will discuss how we can enable embodied neuromorphic AI for robotic systems through our perspectives: (P1) Embodied intelligence based on effective learning rule, training mechanism, and adaptability; (P2) Cross-layer optimizations for energy-efficient neuromorphic computing; (P3) Representative and fair benchmarks; (P4) Low-cost reliability and safety enhancements; (P5) Security and privacy for neuromorphic computing; and (P6) A synergistic development for energy-efficient and robust neuromorphic-based robotics. Furthermore, this paper identifies research challenges and opportunities, as well as elaborates our vision for future research development toward embodied neuromorphic AI for robotics.

关键词： Training Accuracy Service robots Neuromorphic engineering Spiking neural networks Benchmark testing Energy efficiency Security Artificial intelligence Optimization

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An Improved YOLOv7-tiny-based Lightweight Network for the Identification of Fish Species

An Improved YOLOv7-tiny-based Lightweight Network for the Id...

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robotics and Computer Vision (ICRCV), international conference on

作者： Yongnan Yang Xiaomin Wang College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

In order to improve the speed of object detection, an improved YOLOv7-tiny-based lightweight network is proposed in this paper. the lightweight model, named as YOLOv7-tinyMobileOne model, is constructed by introducing a variant of MobileOne to replace the backbone network of YOLOv7-tiny. this model reduces the number of parameters and calculation expenses in the inference phase by pruning the network branch structure. Meanwhile, it alleviates the delay phenomenon of terminal devices. then the proposed model achieves a marked improvement in detection efficiency, suitable for deployment on terminal devices. To verify the performance of the proposed model, we collected and annotated 7085 images of 17 fish species, and the following conclusions can be drawn from this dataset: (1) compared to YOLOv7, the parameter amount and the computational cost of YOLOv7-tiny-MobileOne decreased by 87.67% and by 89.94%, respectively, while the FPS of YOLOv7tiny-MobileOne increased by 284%; (2) compared to YOLOv7tiny, the parameter amount, and the computational cost of YOLOv7-tiny-MobileOne decreased by 25.45% and by 21.21%, respectively, while the FPS of YOLOv7-tiny-MobileOne increased by 9.94%. the experiments demonstrate that the improved model has a smaller volume and can achieve approximate accuracy while significantly improving identification speed, providing considerations for model deployment of terminal devices.

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DgrCLR: Dual Gradient Reinforcement Contrastive Learning for Coal Gangue Classification

DgrCLR: Dual Gradient Reinforcement Contrastive Learning for...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Hongxi Li Wenbo Zhu Zhen Zhu Juqing Yang Jianwen Chen Haibing Li Zhongbo Zhang School of Electronic Information Engineering Foshan University Foshan China School of Mechatronic Engineering and Automation Foshan University Foshan China Electronic Information school Foshan polytechnic Foshan China

In the application of coal gangue visual classification, the influence of strong vibration and uneven illumination in an industrial environment causes the fuzzy boundary of the target and reduces the degree of discrimination of sample features. the existing contrastive learning methods cause apparent gradient degradation due to model and design limitations, leading to fuzzy sample classification failure. Getting rid of the samples with poor annotation effects and alleviating gradient degradation are critical issues in improving the accuracy of coal gangue classification. this paper proposes DgrCLR, a dual gradient reinforcement coal gangue classification algorithm based on improved contrastive learning. By designing the Hybrid reinforcement module and Multi-scale feature fusion module, the gradient degradation is alleviated, the dependence on labeled samples is reduced, and the detail representation ability of the model is improved. the experimental results show that the highest accuracy of the DgrCLR model for coal gangue classification is 85.4%, much higher than that of traditional supervised deep learning models. In addition, the gradient strength of DgrCLR is 20 times or higher than that of SimCLR in some model layers by gradient strength summation comparison.

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Large-Scale Multi-Objective Evolutionary Algorithm Based on Multi-Group Co-Evolution

Large-Scale Multi-Objective Evolutionary Algorithm Based on ...

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robotics, Intelligent Control and Artificial Intelligence (RICAI), international conference on

作者： Changyong Zhang Chunting Zhang School of Electronic Information and Automation Civil Aviation University of China Tianjin China

Multi-objective evolutionary algorithms currently face scalability challenges and struggle to maintain a balance between convergence and distribution in addressing large-scale multi-objective optimization problems. To address this limitation and more comprehensively explore the exponentially growing decision space, we introduce a large-scale multi-objective evolutionary algorithm based on multi-group co-evolution. this algorithm initiates by creating two independent subpopulations. the first subpopulation employs a multi-strategy, multi-objective evolutionary algorithm grounded in dominance relationships to preserve diversity within the decision space. Simultaneously, the second subpopulation utilizes a decomposition-based multi-objective evolutionary algorithm with virtual target vectors to facilitate a rapid convergence toward the target solution. As the populations progress to a specific stage, they are merged, and outstanding individuals are selected based on an enhanced hypervolume parameter. Subsequently, the populations undergo multiple loops with a reset based on predefined rules. throughout different evolutionary stages, the sizes of the two populations dynamically adjust to maintain a balance between population convergence and distribution. To validate the effectiveness of our proposed algorithm, we compare it with five advanced large-scale multi-objective evolutionary algorithms and evaluate its performance on benchmark test problems LSMOPI to LSMOP9. the experimental results clearly demonstrate its superiority in solving large-scale multi-objective problems.

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A 5G-Based Switching Method Between a Local Controller and an Edge Controller

A 5G-Based Switching Method Between a Local Controller and a...

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IEEE international conference of Intelligent Robotic and Control Engineering (IRCE)

作者： Xiulian Wang Jinggao Lin Xi Jin Changqing Xia Chi Xu College of Automation and Electrical Engineering Shenyang Ligong University China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

With the rapid development of edge computing, industrial automation has evolved a two-layer distributed computing architecture, including local controllers communicating through wired networks and edge controllers communicating wirelessly. the edge controllers in existing studies use WIFI, which has a limited working distance and requires independent network deployment, so they are unsuitable for future large-scale complex industrial production. At present, 5G technology is undergoing rapid development in the field of industrial automation, with wide coverage and excellent mobility, gradually becoming suitable for future large-scale and complex industrial production scenarios. therefore, this paper proposed an SW5G method based on 5G, which combined the local controller with the edge controller. the controllers were switched according to the constantly changing 5G network transmission delay, thereby optimizing the overall system control performance. Finally, simulation experiments demonstrated good control performance of SW5G in various network scenarios.

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A Novel Cosine-Modulated-Polynomial Chaotic Map to Strengthen Image Encryption Algorithms in IoT Environments 28th

A Novel Cosine-Modulated-Polynomial Chaotic Map to Strengthe...

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28th international conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Khan, Muhammad Shahbaz Ahmad, Jawad Al-Dubai, Ahmed Pitropakis, Nikolaos Driss, Maha Buchanan, William J. School of Computing Engineering and the Built Environment Edinburgh Napier University EdinburghEH10 5DT United Kingdom Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia RIADI Laboratory University of Manouba Manouba2010 Tunisia

With the widespread use of the Internet of things (IoT), securing the storage and transmission of multimedia content across IoT devices is a critical concern. Chaos-based Pseudo-Random Number Generators (PRNGs) play an essential role in enhancing the security of image encryption algorithms. this paper introduces a novel 1-dimensional cosine-modulated-polynomial chaotic map to be used as a PRNG in image encryption algorithms. the proposed map utilizes a cosine function to modulate the outcome of a polynomial expression, resulting in complex chaotic behaviour. the designed map acts as a self-modulating system and offers a larger chaotic range, reduced structural complexity, and enhanced chaotic properties, such as aperiodicity, unpredictability, ergodicity, and sensitivity to control parameters and initial conditions, in comparison to the traditional 1-dimensional chaotic maps. An extensive evaluation is performed to gauge the chaotic behaviour of the proposed map, including bifurcation diagrams, chaotic trajectory analysis, fixed point and stability analysis, Lyapunov Exponent, Kolmogorov Entropy and NIST SP800-22 tests demonstrating its effectiveness to be used as a secure PRNG in image encryption algorithms. © 2024 the Authors.

关键词： Polynomials

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Large Fixture Calibration Method Based on Point Cloud Scanning for Robotic Drilling

Large Fixture Calibration Method Based on Point Cloud Scanni...

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automation, Control and robotics Engineering (CACRE), international conference on

作者： Yongzhuo Gao Haibo Gao Dexin Cheng Wei Dong the State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798350350302

ISBN: (纸本)9798350350319

the rocket fuel tank requires the splicing of multiple sub-parts, which is difficult to be processed in an integrated manner. Among them, the short shell parts of the storage tank play a linking role and need to be drilled on the surface. Due to the large size of the tooling of the parts, there are problems such as large installation and positioning errors and low precision of robot automatic drilling. therefore, a robot automatic hole making calibration method is proposed to solve the current problems. In order to achieve automated control, a large-scale robot automatic drilling system must have large-scale flexible tooling, control algorithms, human-computer interaction interfaces, execution units, and integrated tool rooms. It is essential to build effective calibration procedures as a foundation for this automation. the calibration plane is fitted, positioned, and calibrated using the point cloud scanning method. Subsequently, the hand-eye calibration method is employed to establish the relationship between the coordinate systems. Using this approach, the calibration process is ultimately finished, resulting in the successful implementation of automated drilling. the experimental findings demonstrate that, in the designated quadrant, the drilling rate of the robot can achieve 5 holes per minute. Furthermore, the average variance of the hole position is below 0.2mm, and the highest error is 0.5mm. these results align with the requirements of practical production.

关键词： Drilling Point cloud compression Rockets automation Service robots Robot kinematics Splicing

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5G Light-Emitting Biomedical Robots for Hospital Disinfection

5G Light-Emitting Biomedical Robots for Hospital Disinfectio...

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IEEE international conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： Ugochukwu O. Matthew Renata L. Rosa Nwamaka U. Okafor Jazuli S. Kazaure Ogobuchi Daniel Okey Matthew Abiola Oladipupo Lateef Olawale Fatai Victor Nosakhare Oriakhi Demostenes Z. Rodriguez Computer Science Dept Federal University of Lavras Minas Gerais Brazil School of Elect Electronics Engineering University College Dublin Dublin Ireland Electrical Engineering Dept Hussaini Adamu Federal Polytechnic Kazaure Nigeria Center for Engineering Federal University of ABC Sao Paulo Brazil Data Science Department University of Salford Manchester England UK Robotics and Automation University of Salford Manchester England UK

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

Hospital-acquired infections (HAIs) pose a significant challenge to healthcare systems worldwide, exacerbated by the COVID-19 pandemic. Current disinfection methods often fall short in ensuring comprehensive sterilization of high-contact areas, leading to persistent risks. this study addresses this gap by leveraging 5G biomedical internet of things (IoT) robots equipped with ultra-violet light emitting diodes (UV-LEDs) for effective disinfection. Our approach focuses on destroying the genetic material of pathogens, thereby preventing their spread within medical facilities. By integrating autonomous IoT 5G disinfection robots, we aim to enhance hospital sanitation and continuous infectious disease surveillance. A thorough review of nine biomedical studies underscores the efficacy of UV-based disinfection methods. We demonstrate that advancements in 5G infrastructure will significantly boost IoT and 5G medical robotic innovations, facilitating their application in both hospital settings and industrial healthcare automation. thus, this paper presents a robust framework for utilizing 5G and IoT technologies in medical robotics to improve healthcare safety and efficacy.

关键词： COVID-19 5G mobile communication Hospitals Pandemics Service robots Robot kinematics Surveillance Light emitting diodes Safety Standards

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