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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Sun, Zheng Dong, Zhipeng Wang, Shixiong Chen, Fei T-Stone Robotics Institute The Chinese University of Hong Kong Department of Mechanical and Automation Engineering SAR Hong Kong Hong Kong

ISBN: (纸本)9781665481090

Hyperspectral imaging has become essential in fields such as waste sorting and agriculture due to its ability to capture detailed spectral information. However, conventional hyperspectral imaging systems typically rely on mechanical movements, such as conveyor belts or linear stages, to scan the target objects, which limits their applicability in environments with static targets or space constraints. To address these challenges, we present PRISM (Polyhedral Reflective Imaging Scanning Mechanism), a novel system that facilitates rapid and high-precision imaging of stationary objects by leveraging a polyhedral reflective structure, thereby eliminating the need for external relative motion. Experimental evaluations demonstrate that PRISM can achieve full spatial-spectral data of 512 by 727 pixels with 448 bands in 2.01 seconds, while maintaining high spectral accuracy, with a reflectance standard deviation of 0.015. We further validate PRISM's effectiveness through textile waste sorting experiments, where PRISM accurately differentiates between various material types at both the pixel and object levels, showcasing its potential for practical applications in industrial settings. © 2024 IEEE.

关键词： Prisms

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Signal Processing for Acoustic Emission Signature Analysis and Defect Detection 26

Signal Processing for Acoustic Emission Signature Analysis a...

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26th International Conference on Digital Signal Processing and its Applications, DSPA 2024

作者： Altay, Yeldos A. Kuzivanov, Dmitry O. Altay, Dinara A. Fedorov, Alexey V. Satbayev University Department of Robotics and Technical Means of Automation Almaty Kazakhstan ITMO University Faculty of Control Systems and Robotics Saint-Petersburg Russia Military Engineering Institute of Radio Electronics and Communications Department of Natural Sciences Almaty Kazakhstan

ISBN: (纸本)9798350361728

This article presents the results of implementing a digital signal processing method for analyzing the acoustic emission (AE) signature and detecting defects such as cracks and pores. The signal processing method is based on a cascade connection of digital high-pass filters and ensures the selection of informative signals and increases the signal-to-interference ratio. In order to control the formation of defects in the process of selective laser melting, the paper presents the results of experimental testing of the cascade filtration method for detecting defects in the internal structure such as cracks and pores. The signal components recorded during the development of defects during the growth of additive manufacturing products were extracted. AE signatures were analyzed and statistical relationships were assessed. The relationship between the parameters of the AE signal and the values of laser radiation power has been identified, which characterizes the process of defect formation. © 2024 IEEE.

关键词： High pass filters

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Fuzzy Inference based Pedestrian and Cyclist Tracking in a Compressed Video

Fuzzy Inference based Pedestrian and Cyclist Tracking in a C...

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2024 IEEE Conference on Engineering Informatics, ICEI 2024

作者： Praveenkumar, S.M. Patil, Prakashgouda Karadgi, Sachin Hiremath, P.S. KLE Technological University Department of Computer Applications Hubballi India KLE Technological University Center for Automation and Robotics Research Hubballi India

ISBN: (纸本)9798331505776

In recent times, the artificial intelligence (AI) based technologies are pervading the various human activities, for instance, smart manufacturing plants and autonomous driving vehicles (ADVs) on roads. With the advent of Industry 4.0, it is of common occurrence that human workers and autonomous mobile robots (AMRs) coexist and collaborate in the various tasks of smart manufacturing plants. The humans could be either walking or riding a bicycle in case of large area plants such as paper manufacturing industries. Thus, the industrial safety and traffic safety become cause of primary concern in the usage of AMRs and ADVs, respectively, which entails the need for equipping these intelligent devices with computer vision for detecting the humans, who are either walking or cycling, in their path of mobility, and thus avoid their collision. The integration of such detection systems is crucial for maintaining safe interactions between humans and machines. In the present study, a novel methodology based on fuzzy inferencing is designed for video analytics in the compressed domain, aimed at real-time detection and tracking of pedestrians and cyclists, by utilizing motion vectors (MV) and Transformer Prediction Heads-You Only Look Once version five (TPH-YOLOv5). The video compression formats MPEG-4 and H. 264 are considered. The proposed method can be adapted to any compression format and video camera settings without any prior setup. In the literature, many algorithms for tracking of pedestrians and cyclists in a compressed domain video are experimented, but these have some limitations such as (i) a fixed interval for keyframe occurrence, (ii) utilization of only P frames, and (iii) setup for only a single video codec. The proposed method is designed to address these limitations by employing keyframe intervals of variable length, utilizing P and B frames, and setting up two codecs MPEG-4 and H.264. Further the event box prediction in video frames is done by fuzzy inferencing.

关键词： Bicycles

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Autonomous Navigation and Mapping of Snake Robot for Urban Search and Rescue

Autonomous Navigation and Mapping of Snake Robot for Urban S...

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2023 International Conference on robotics and automation in Industry, ICRAI 2023

作者： Ullah, Syed Izzat Mahmood, Tallat Anayatullah A&M University-Corpus Christi Department of Computing Sciences Texas Pakistan National Centre of Robotics and Automation Control Automotive and Robotics Lab Rawalpindi Pakistan

ISBN: (纸本)9781665464727

In an Urban Search and Rescue situation, under extreme time pressure, rescue workers have to locate and extract the trapped people in collapsed structures. Due to the lack of medical treatment, food, and water, the victim's mortality rate dramatically increases over time. Rescue operations for both rescue workers and victims might be as dangerous as the initial event. For such situations, snake robots which are inspired by their biological counterparts, are shown to be a good option in the literature, to help the rescue workers in positioning the victims or delivering life-saving drugs to extend the life of the victims for some time. However, current research mainly focuses on mechanical design, control mechanisms, and gait generation. To alleviate this concern, we have integrated state-of-the-art methods to develop an autonomous snake robot that can navigate in an unknown environment while also generating a 3D map, to provide a better idea of the environment to the rescue workers. A simulated maze environment is implemented and demonstrated by using the CoppeliaSim simulation, running on Robot Operation System (ROS) and Linux OS. The simulation result shows the effectiveness of the proposed autonomous navigation system for the snake robot to plan an obstacle-free path from the robot's current position to the goal position without an apriori knowledge of the environment. © 2023 IEEE.

关键词： Motion planning

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Natural Disaster Damage Assessment using Semantic Segmentation of UAV Imagery

Natural Disaster Damage Assessment using Semantic Segmentati...

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2023 International Conference on robotics and automation in Industry, ICRAI 2023

作者： Asad, Muhammad Haroon Asim, Malik Muhammad Awan, Muhammad Naeem Mumtaz Yousaf, Muhammad Haroon University of Engineering and Technology Computer Engineering Department Taxila Pakistan National Center of Robotics and Automation University of Engineering and Technology Swarm Robotics Lab Taxila Pakistan

ISBN: (纸本)9781665464727

Numerous natural disasters due to climate change pose major threats to the sustainability of public infrastructure and human lives. For emergency rescue and recovery during a disaster, a rapid and accurate evaluation of disaster damage is essential. In recent years, the Transformer has gained popularity in a number of tasks related to computer vision, which offers tremendous potential for improving the accuracy of disaster damage assessments. Our research aims to determine whether Vision Transformer (ViT) can be used to assess natural disaster damage on high-resolution Unmanned Aerial Vehicle (UAV) data in comparison with conventional deep-learning semantic segmentation techniques. We discuss if Transformer can perform better than CNNs in accurately assessing the damage caused in order to bridge the gap. Detailed performance comparison of state-of-art deep learning semantic segmentation models (UNET, Segnet, PSPNet, Deeplabv3+) and Transformer framework (SegFormer) for damage assessment is presented. The experimentation is performed on both natural disaster damage datasets (RescueNet, FloodNet). The study supported SegFormer as the most appropriate model for estimating disaster damage, with mIoUs of 96% on the RescueNet dataset and 82.22% on the FloodNet dataset, respectively. The Transformer is capable of outperforming conventional segmentation CNNs in understanding the entirety of the scene and assessing the severity of the damage, based on both quantitative evaluation and visual results. © 2023 IEEE.

关键词： Disasters

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Vertical Air Gap Integration in Micromachined Millimeter-Wave Patch Antenna with Inset-Fed Technique for RF Enhancement 3

Vertical Air Gap Integration in Micromachined Millimeter-Wav...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Badrinathan, B. Anitha, G. Saveetha School of Engineering Department of Automation and Robotics Engineering Sciences Saveetha University Saveetha Institute of Medical and Technical Tamilnadu Chennai India

ISBN: (纸本)9798350354171

This work proposes an innovative antenna design for millimeter-wave applications. For improved performance, the proposed antenna employs a bulk micromachining structure combined with an inset-fed technique. The design is fully simulated using the HFSS software using a substrate material having a dielectric constant of 12.9. A vertical air gap is introduced beneath the patch element through the simulated micromachining structure to enhance the RF performance of the antenna. The designed antenna dimensions are 1.2mm × 1.7mm × 0.27mm and are intended to function at 60GHz within the millimeter-wave frequency spectrum. Outstanding performance metrics are shown by the simulation results, such as a maximum gain of 4.82 dBi, a return loss of -60.95 dB and a voltage standing wave ratio (VSWR) of 1 at the desired operating frequency of 60 GHz. The simulated design of the antenna exhibits promising potential for integration into emerging millimeterwave wireless systems and devices. © 2024 IEEE.

关键词： Gallium arsenide

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Odometry and Inertial Sensor-based Localization of a Snake Robot

Odometry and Inertial Sensor-based Localization of a Snake R...

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2023 International Conference on robotics and automation in Industry, ICRAI 2023

作者： Shahid, Ramsha Baloch, Anam Tahir, Humayun Ullah, Anayat National Centre of Robotics and Automation Control Automotive and Robotics Lab Rawalpindi Pakistan Balochistan University of IT Engineering and Management Sciences Department of Electronic Engineering Quetta Pakistan

ISBN: (纸本)9781665464727

Terrain adaptability gives snake robots an edge over wheeled mobile robots. Snake robot's applications expansion to disaster management is due to its limbless and modular design. Replicating the navigation and locomotion of a biological snake have been in the spotlight for many researchers. In this study, a robust localization algorithm of a snake robot is developed. A sensor fusion-based algorithm using probabilistic approaches: UKF (Unscented Kalman Filter) and EKF (Extended Kalman Filter) has been proposed. Odometry is fused with one and two IMUs (Inertial Measurement Units) with both probabilistic approaches. Evaluation of results in a simulation environment showed that the fusion of two IMUs and odometry using EKF outer performs in terms of accuracy when odometry is fused with one IMU using EKF. Furthermore, the fusion of two IMUs and odometry with UKF is computationally expensive resulting in a large convergence time which is not the best suitable approach that can be utilized for the snake robot. © 2023 IEEE.

关键词： Extended Kalman filters

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Deep Learning-Based Approach for Frost Quantification in Domestic Refrigerators 6

Deep Learning-Based Approach for Frost Quantification in Dom...

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6th International Conference on robotics and automation in Industry, ICRAI 2024

作者： Akbar, Hassan Hassan, Huzzam Malik, Anjum Naeem Nawaz, Tahir Habib CEME NUST National Centre of Robotics and Automation Department of Mechatronics Engg. Islamabad Pakistan CEME NUST Department of Mechatronics Engg. Islamabad Pakistan

ISBN: (纸本)9798350355703

Frost formation in refrigerators disrupts heat ex-change, lowering system efficiency and causing operational is-sues, making defrosting essential for maintaining performance. However, modern defrost systems rely on sensors for frost detection. The sensory approach is reliable but it could degrade over time in changing environmental conditions. To address this limitation, the paper proposes a real-time frost quantification method using digital image processing and deep learning. A custom Convolutional Neural Network (CNN) model is designed to classify frost thickness in RGB images captured inside a refrigerator. We have used a custom dataset, collected from a real refrigeration environment and made a comparison with other methods previously used for frost thickness estimation. The dataset is collected from several experiments to ensure its diversity such as illumination and environmental changes. This method enables precise frost detection, targeted for optimal defrost control for improved efficiency. Results indicate that the CNN model achieves a high test accuracy of 98.10%. The model's performance surpasses traditional methods, demonstrating its robustness in distinguishing varying frost levels from 0 to 4 (mm). This method enables precise frost thickness estimation and is a suitable choice for defrost control to improve the energy efficiency of the refrigerator. © 2024 IEEE.

关键词： Convolutional neural networks

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Modeling a fuzzy expert system for predicting customers’ behavioral intention for restaurant business through parallel prediction of perceived values and satisfactions

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International Journal of Information Technology (Singapore) 2025年第2期17卷 1007-1014页

作者： Akhtaruzzaman, M. Shahryer, Fahim Halder, Sachitra Tanvin, Jamal Uddin Department of Computer Science and Engineering Military Institute of Science and Technology (MIST) Mirpur Cantonment Dhaka 1216 Bangladesh Quantum Robotics and Automation Research Group (QRARG) Dhaka Bangladesh

This paper focuses on the modeling of a recommendation process for the restaurant industry. It is a challenge job for a restaurant owner to ascertain the intentions of customers to evaluate their services, further improve their profit margin. So, the objective of this study is to design a fuzzy-based prediction model of customers’ intentions for a fine dining experience. To facilitate a comparative analysis and to identify the optimal approach, various membership functions (MF) and de-fuzzification methods are employed along with the analysis of the correlational behavior among various input subsets. Results have shown that CoA method with triangular MF are well enough to implement the model. The fuzzy-correlation analysis presents various levels of membership strengths for a same recommendation score in some cases. Moreover, the evaluation of the proposed model based on a custom dataset shows prominent results with as 0.938. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Customer intension Customer satisfaction FIS Perceived value Recommendation

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Effect of Pneumatic Based Hybrid Robotic Gripper for Safer Handling of Complex Geometries 11

Effect of Pneumatic Based Hybrid Robotic Gripper for Safer H...

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11th IEEE International Conference on Advances in Computing and Communications, ICACC 2024

作者： Sivaraj, Santhosh Ravi, Ajay Jose, Bibin Joe Kaliyannan, Gobinath Velu Sivakumar, Rajkamal Amalraj, Riya Catherine Easwari Engineering College Department of Robotics and Automation TamilNadu Chennai600089 India Kongu Engineering College Department of Mechatronics Engineering TamilNadu Perundurai638060 India

ISBN: (纸本)9798350379136

The present research work mainly concentrates on development of hybrid grippers with the integration of hard and soft components for achieving object manipulation. By combining pneumatic actuation with mechanical fingers, complex geometries can be handled effectively. Pneumatic hybrid gripper fabricated with PLA and silicone rubber offers adaptability and versatility. Resistive sensors over silicone surface of gripper lead to cost-effective force detection and tactile sensing. The proposed gripper was designed and analyzed using Fusion 360 and ANSYS. Inflatable silicone rubber and gripper body were fabricated using liquid casting and Fused Deposited Modelling 3D printing techniques. Real-time feedback sensors were employed with fabricated gripper to get better performance. Testing proves its capability to handle loads up to 500g with a force of 3.3N. Finally, the assembled hybrid gripper offers a versatile solution for robotic manipulation, ensuring stability and minimizing damage across various industries. © 2024 IEEE.

关键词： Silicones

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