The control and movement of automated robots constitutes a fundamental field in mobile robotics. The system uses a set of algorithms and techniques to guide the precise movement and navigation of robots. Convergence, ...
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ISBN:
(数字)9798350355284
ISBN:
(纸本)9798350355291
The control and movement of automated robots constitutes a fundamental field in mobile robotics. The system uses a set of algorithms and techniques to guide the precise movement and navigation of robots. Convergence, refers to the robot's ability to follow and approach a desired trajectory accurately over time. This concept implies that any initial deviation from the planned trajectory must be corrected progressively, so that the robot adjusts and maintains it's course towards the destination.
Affordable high-resolution cameras and state-of-the-art computervisiontechniques have led to the emergence of various vision-based tactile sensors. However, current vision-based tactile sensors mainly depend on geom...
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Affordable high-resolution cameras and state-of-the-art computervisiontechniques have led to the emergence of various vision-based tactile sensors. However, current vision-based tactile sensors mainly depend on geometric optics or marker tracking for tactile assessments, resulting in limited performance. To solve this dilemma, we introduce optical interference patterns as the visual representation of tactile information for flexible tactile sensors. We propose a novel tactile perception method and its corresponding sensor, combining structural colors from flexible blazed gratings with deep learning. The richer structural colors and finer data processing foster the tactile estimation performance. The proposed sensor has an overall normal force magnitude accuracy of 6 m N, a planar resolution of 79 μm and a contact-depth resolution of 25 μm. This work presents a promising tactile method that combines wave optics, soft materials and machine learning. It performs well in tactile measurement, and can be expanded into multiple sensing fields.
vision tracking is a key component of a video sequence. It is the process of locating single or multiple moving objects over time using one or many cameras. The latter's function consists of detecting, categorizin...
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vision tracking is a key component of a video sequence. It is the process of locating single or multiple moving objects over time using one or many cameras. The latter's function consists of detecting, categorizing, and tracking. The development of the trustworthy solution for video sequence analysis opens up new horizons for a variety of applications, including intelligent transportation systems, biomedical, agriculture, human-machine interaction, augmented reality, video surveillance, robots, and many crucial research areas. To make efficient models, there are challenges in video observation to deal with, such as problems with the environment, light variation, pose variation, motion blur, clutter, occlusion, and so on. In this paper, we present several techniques that addressed the issues of detecting and tracking multiple targets on video sequences. The proposed comparative study relied on different methodologies. This paper's purpose is to list various approaches, classify them, and compare them, using the Weighted Scoring Model (WSM) comparison method. This includes studying these algorithms, selecting relevant comparison criteria, assigning weights for each criterion, and lastly computing scores. The obtained results of this study will reveal the strong and weak points of each algorithm mentioned and discussed.
The aluminium manufacturing industries face numerous challenges when it comes to sorting metal scraps, particularly when scraps are small in size. Traditional methods like flotation and gravity separation are ineffici...
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ISBN:
(数字)9798331540364
ISBN:
(纸本)9798331540371
The aluminium manufacturing industries face numerous challenges when it comes to sorting metal scraps, particularly when scraps are small in size. Traditional methods like flotation and gravity separation are inefficient, energy-intensive, and often fail when dealing with size variations. Recent advancements in computervision and deep learning algorithms, including intelligent robotic systems, offer promising solutions. However, challenges such as accurate detection in complex environments, varying lighting conditions, and overlapping objects still persist. Newer techniques like YOLO (You Only Look Once) for real-time object detection, and Transfer Learning to improve model accuracy with smaller datasets, have shown potential. This research aims to overcome these challenges by developing an AI-powered, computervision-based sorting system that can identify and classify aluminium scraps by color and shape at high speed. The authors developed a custom dataset and trained models using CNN architectures such as DenseNet-161, ResNet-152, and VGG 19. By employing ensemble techniques, this research achieved an 75% of accuracy.
Nowadays, with the significant development of artificial intelligence and especially after the emergence of deep learning, vision systems specifically the vision of robots have become more interesting. One of the most...
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ISBN:
(纸本)9781665454520
Nowadays, with the significant development of artificial intelligence and especially after the emergence of deep learning, vision systems specifically the vision of robots have become more interesting. One of the most important challenges of computervision and robotics is object detection. In fact, high computational complexity bothers object detection techniques that rely on region proposals or predefined anchors. We propose an optimized method upon a deep reinforcement learning framework along with multi-stage approaches for object detection as well as train two intelligent agents for a more accurate search of objects and localize them in images. Several candidate bounding-boxes are proposed to the algorithm in the first stage, and the perfect boxes are selected for the final boxes around the objects in the second stage using an optimal approach. Our approach was assessed using two renowned datasets in the object detection field, PASCAL VOC 2007 and PASCAL VOC 2012. According to these evaluations and experiments, the proposed method not only outperforms similar non-region proposal methods but also does well detecting small objects.
The proceedings contain 75 papers. The special focus in this conference is on intelligent Systems and Machine Learning. The topics include: Mining Ancient Medicine Texts Towards an Ontology of Remedies – A Semi-autom...
ISBN:
(纸本)9783031350771
The proceedings contain 75 papers. The special focus in this conference is on intelligent Systems and Machine Learning. The topics include: Mining Ancient Medicine Texts Towards an Ontology of Remedies – A Semi-automatic Approach;a Novel Oversampling Technique for Imbalanced Credit Scoring Datasets;a Blockchain Enabled Medical Tourism Ecosystem;Measuring the Impact of Oil Revenues on Government Debt in Selected Countries by Using ARDL Model;diagnosis of Plant Diseases by Image Processing Model for Sustainable Solutions;face Mask Detection: An Application of Artificial Intelligence;a Critical Review of Faults in Cloud Computing: Types, Detection, and Mitigation Schemes;video Content Analysis Using Deep Learning Methods;prediction of Cochlear Disorders Using Face Tilt Estimation and Audiology Data;F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets;quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges;multivariate Analysis and Comparison of Machine Learning algorithms: A Case Study of Cereals of America;competitive Programming Vestige Using Machine Learning;machine Learning techniques for Aspect Analysis of Employee Attrition;AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance;Real-Time Identification of Medical Equipment Using Deep CNN and computervision;design of a intelligent Crutch Tool for Elders;an Approach to New Technical Solutions in Resource Allocation Based on Artificial Intelligence;gesture Controlled Power Window Using Deep Learning;novel Deep Learning techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition;a Novel Model to Predict the Whack of Pandemics on the International Rankings of Academia.
The proceedings contain 75 papers. The special focus in this conference is on intelligent Systems and Machine Learning. The topics include: Mining Ancient Medicine Texts Towards an Ontology of Remedies – A Semi-autom...
ISBN:
(纸本)9783031350801
The proceedings contain 75 papers. The special focus in this conference is on intelligent Systems and Machine Learning. The topics include: Mining Ancient Medicine Texts Towards an Ontology of Remedies – A Semi-automatic Approach;a Novel Oversampling Technique for Imbalanced Credit Scoring Datasets;a Blockchain Enabled Medical Tourism Ecosystem;Measuring the Impact of Oil Revenues on Government Debt in Selected Countries by Using ARDL Model;diagnosis of Plant Diseases by Image Processing Model for Sustainable Solutions;face Mask Detection: An Application of Artificial Intelligence;a Critical Review of Faults in Cloud Computing: Types, Detection, and Mitigation Schemes;video Content Analysis Using Deep Learning Methods;prediction of Cochlear Disorders Using Face Tilt Estimation and Audiology Data;F2PMSMD: Design of a Fusion Model to Identify Fake Profiles from Multimodal Social Media Datasets;quantum Data Management and Quantum Machine Learning for Data Management: State-of-the-Art and Open Challenges;multivariate Analysis and Comparison of Machine Learning algorithms: A Case Study of Cereals of America;competitive Programming Vestige Using Machine Learning;machine Learning techniques for Aspect Analysis of Employee Attrition;AI-Enabled Automation Solution for Utilization Management in Healthcare Insurance;Real-Time Identification of Medical Equipment Using Deep CNN and computervision;design of a intelligent Crutch Tool for Elders;an Approach to New Technical Solutions in Resource Allocation Based on Artificial Intelligence;gesture Controlled Power Window Using Deep Learning;novel Deep Learning techniques to Design the Model and Predict Facial Expression, Gender, and Age Recognition;a Novel Model to Predict the Whack of Pandemics on the International Rankings of Academia.
This research presents a novel approach for real-time inverse kinematic function generation for robotic joints using Generative Adversarial Networks (GANs) integrated with advanced computervisiontechniques. This pro...
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ISBN:
(数字)9798331528713
ISBN:
(纸本)9798331528720
This research presents a novel approach for real-time inverse kinematic function generation for robotic joints using Generative Adversarial Networks (GANs) integrated with advanced computervisiontechniques. This project includes an automated [15] model that moves based on human hand movements. The system uses Modern computervisiontechniques to precisely interpret human hand motions, which enables a robot to imitate hand movements in real-time, mainly in the x and Y axes. The proposed technique leverages GANs for modeling complex, high-dimensional kinematic transformations, allowing for accurate and effortless computation of joint configurations determined by end-effector positions. Thus, if a human hand performs a wave motion, the robotic arm will move from left to right, replicating the wave action. Using sensors, inverse kinematics, and AI/ML algorithms, the outcome shows significant improvements in motion accuracy and computational efficiency over usual inverse kinematic solutions, showcasing the model’s potential in human-robot interactions.
Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable au...
Fully autonomous mobile robots have the potential to revolutionize various industries, from warehouse management to hospital logistics and last-mile deliveries. However, a significant obstacle to achieving reliable autonomy lies in the high computational and energy requirements. In response to this challenge, our paper introduces two innovative algorithms: the Pure Image Segmentation Approach (PISA) and the UNet Based Approach to Semantic Segmentation (UBASS). PISA leverages classical computervisiontechniques, offering a fresh perspective on solving crucial tasks such as object detection, object avoidance, and lane detection. In contrast, UBASS harnesses the power of deep learning algorithms for semantic segmentation, unlocking new capabilities in robot perception. Our experiments showcase the effectiveness of these algorithms, demonstrating their accuracy and computational efficiency. Notably, PISA and UBASS outperform or match traditional techniques, including End-to-End Deep Learning and Canny Edge Detection, in terms of both task performance and resource utilization. This research contributes to the advancement of autonomous mobile robotics by offering practical and efficient solutions for navigation and perception challenges. By combining classic and contemporary approaches, we aim to inspire further research in the field, ultimately paving the way for more accessible and dependable autonomous mobile robots.
With the advent of Industry 5.0 and the rise of human-centered intelligent manufacturing, people have paid increasing attention to the issue of security in human-machine collaboration. Developing safe human-robot coop...
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ISBN:
(数字)9798350363173
ISBN:
(纸本)9798350363180
With the advent of Industry 5.0 and the rise of human-centered intelligent manufacturing, people have paid increasing attention to the issue of security in human-machine collaboration. Developing safe human-robot cooperation in constrained environments has emerged as the primary area of research interest. vision systems with deep learning have gradually supplanted more conventional approaches, such electronic wearables, electronic fences, and lidar techniques, to ensure safe collaboration. Object identification and posture estimation are two techniques that are currently in use to forecast distances in space more accurately. These techniques can track the approximate locations of humans and robots in real-time, significantly lowering the likelihood of safety incidents. Still, more accurate evaluation of the relative positions of humans and robots is needed for effective collaboration. This paper suggests SCC-HRNet, an efficient key point recognition technique. SCC-HRNet is able to find the important feature points of both humans and robots more precisely in dual-camera human-robot safe collaboration scenarios. Using our human-robot collaboration dataset, SCC-HRNet outperforms other algorithms with an average precision gain of 1.6%, correctly identifying key points.
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