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vision-based Robot Handwriting Skill Reproduction and Generalization 27

Vision-based Robot Handwriting Skill Reproduction and Genera...

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27th International conference on Mechatronics and Machine vision in Practice (M2VIP)

作者： Li, Xian Dai, Shi-Lu Yang, Chenguang South China Univ Technol Coll Automat Sci & Engn Guangzhou 510640 Peoples R China Foshan Newthinking Intelligent Technol Co Ltd Foshan 528231 Peoples R China

ISBN: (纸本)9781665431538

Robot skills transfer can increase the availability and acceptability of robots. However, the physical contact between robots and humans when teaching is unsafe. Teaching based on teleoperation can avoid contact, but the requirement of the extra input device is inconvenient. In this paper, a possible solution is provided to address the problems by proposing a novel robot skill learning framework to realize handwriting skill transfer and generalization. Instead of teaching hand-to-hand, the computer vision technique is utilized to recognize the human motion in the demonstration stage, which makes skill transfer more intuitive and natural. this approach detects continuous curves by finding the connection point in pictures written by humans, and then the continuous curves are used for teaching so that robots can learn the human skills and further generalize the learned handwriting skills. Finally, an experiment that the robot writes the Chinese character "Yang" is conducted to validate the effectiveness of the presented approach.

关键词： Cell proliferation

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A Novel computer vision Algorithm for Enhancing Image Recognition and Classification

A Novel Computer Vision Algorithm for Enhancing Image Recogn...

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IEEE International conference on Power, intelligent Computing and Systems (ICPICS)

作者： Herun Zhong School of Arts and Design Hubei University of Technology Wuhan China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

computer vision focuses on optimizing computers to understand and interpret visual data from photos or movies, while image recognition specializes in detecting and categorizing objects or patterns in photographs. Technological advancements have revolutionized the field of computer science and found applications in various fields such as robotics, healthcare, security, and entertainment. this article offers a comprehensive overview of the novel approaches of computer vision algorithms widely used in enhancing image recognition and classification. the discipline deals with extracting, analyzing, and understanding information from images or videos by developing algorithms that enable robots to see and comprehend visual information similar to humans. Image recognition primarily emphasizes automatic identification and categorization of objects or patterns in pictures. According to findings, significant developments have been made due to large datasets availability along with improvements in deep learning techniques leveraging enhanced processor power. Convolutional neural networks are now the dominant approach for image recognition problems within deep learning field attaining consistently high-performance levels across several benchmarks.

关键词： Deep learning computer vision Visualization Image recognition Benchmark testing Visual systems Classification algorithms Object recognition Convolutional neural networks Videos

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Development of computer vision Based Aluminium Scrap Sorting Using Real Time Metal Scrap Dataset

Development of Computer Vision Based Aluminium Scrap Sorting...

引用

Sustainable Expert Systems (ICSES), International conference on

作者： Adhithya S J. Kathirvelan School of Electronics Engineering Vellore Institute of Technology Vellore India

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 computer vision 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, computer vision-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.

关键词： Deep learning YOLO computer vision Accuracy Service robots Computational modeling Aluminum Real-time systems Recycling Sorting

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Towards Latency Efficient DRL Inference: Improving UAV Obstacle Avoidance at the Edge through Model Compression

Towards Latency Efficient DRL Inference: Improving UAV Obsta...

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International conference on intelligent Transportation

作者： Patrick McEnroe Shen Wang Madhusanka Liyanage School of Computer Science University College Dublin Dublin Ireland

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

It is vital that autonomous Unmanned Aerial Vehicles (UAVs) are able to avoid obstacles effectively. When avoiding such obstacles it is important that movement decision are made fast (i.e. inference latency is low) so that crashes are avoided. When deep reinforcement learning (DRL) is being leveraged as the method of obstacle avoidance one way of reducing this inference latency is to deploy the DRL model at the edge (e.g., on-UAV). However, even if the DRL model is small enough to be deployed on-UAV, the inference latency can be too high. there is a lack of research that examines reducing DRL inference time of UAVs when avoiding obstacles. thus, this paper examines various model compression techniques to improve the inference speed of such DRL models deployed at the edge. We propose an approach that combines these model compression techniques and apply it to a well performing Dueling Double Deep Q-Network (D3QN) baseline model. On the Nvidia Jetson Orin Nano and Nvidia Jetson Nano edge devices we show that, relative to our baseline model, this combined model compression approach reduces inference latency by 38.61% and 53.18% while only reducing the success rate by 2.34% and 5% respectively. All our code is open-sourced.

关键词： Performance evaluation Codes Navigation Autonomous aerial vehicles Deep reinforcement learning Nanoscale devices Inference algorithms Collision avoidance Autonomous robots Accidents

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Reexamining Indoor Mobile robots from a Cognitive Perspective

Reexamining Indoor Mobile Robots from a Cognitive Perspectiv...

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Robotics and computer vision (ICRCV), International conference on

作者： Fei Song Jinyu Li Fengzhen Tang Yandong Tang Bailu Si State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Shenyang China University of Chinese Academy of Sciences Beijing China School of Systems Science Beijing Normal University Beijing China

the field of traditional mobile robot navigation has undergone a gradual transformation, evolving into a standardized and procedural research domain. through a fresh cognitive perspective on this navigation process, a bridge emerges between robotic researchers and cognitive neuroscientists, thereby fostering the growth of interdisciplinary exploration. this article meticulously examines three pivotal phases of robot navigation: information acquisition, simultaneous localization and mapping (SLAM), and path planning. these phases are intricately linked to pertinent cognitive research. Furthermore, a comprehensive survey of existing biomimetic and neuromorphic algorithms is presented, highlighting their applications in augmenting robot navigation capabilities. Ultimately, this article charts a visionary course for the future of robot navigation, envisioning its potential in facilitating more intelligent and efficient navigation techniques.

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VC-Gaussian: vision-Centric Gaussian Splatting for Dynamic Autonomous Driving Scenes

VC-Gaussian: Vision-Centric Gaussian Splatting for Dynamic A...

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International conference on intelligent Transportation

作者： Jiusi Li Tuopu Wen Kun Jiang Jinyu Miao Yining Shi Xuhe Zhao Zhigang Fan Diange Yang School of Vehicle and Mobility Tsinghua University Beijing China Zongmu Technology

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Reconstructing dynamic traffic scenes has a wide range of applications in the development of modern autonomous driving systems. Recently, novel-view synthesis techniques, such as Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3D GS), have emerged as promising paradigms for the re-construction of 3D scenes. However, previous works in this area highly rely on LiDAR to provide accurate geometric prior and motion cues across frames to reconstruct dynamic objects, hindering the use of overwhelming vision data from mass-production vehicles. In this paper, we propose a novel vision-Centric reconstruction framework based on 3D GS, VC-Gaussian, which allows high-quality novel-view synthesis and dynamic scene reconstruction for autonomous driving. A composite Gaussian model is designed to represent background and foreground objects separately. To facilitate the initialization and optimization process of Gaussians without LiDAR, we leverage easy-to-obtain monocular geometric prior including metric depth and normal. Experimental results on the real autonomous driving dataset demonstrate that our method out-performs other reconstruction methods with monocular vision inputs and even is competitive with LiDAR-based methods.

关键词： Measurement Solid modeling three-dimensional displays Laser radar Dynamics Reconstruction algorithms Neural radiance field Vehicle dynamics Image reconstruction Autonomous vehicles

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Leveraging Bayesian Optimization to Enhance Self-Supervised Federated Learning of Monocular Depth Estimators

Leveraging Bayesian Optimization to Enhance Self-Supervised ...

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International conference on intelligent Transportation

作者： Elton F. De S. Soares Emilio Vital Brazil Carlos Alberto V. Campos Brazilian Research Lab (BRL) IBM Research and the Federal University of State of Rio de Janeiro (UNIRIO) Rio de Janeiro Brazil BRL UNIRIO

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Current investigations in computer vision on intelligent transportation systems have notably centered on depth estimation from images, primarily because of its cost-efficiency and diverse range of applications. Monocular methods for estimating depth have garnered significant interest due to their dependence on a single camera, which provides considerable versatility compared to binocular techniques necessitating two fixed cameras. Despite leveraging sophisticated self-supervised deep neural network learning techniques with surrogate tasks such as pose estimation and semantic segmentation, essential requirements for the practical deployment of autonomous vehicles are often neglected. these entail safeguarding data privacy, minimizing network usage, distributing computational expenses, and fortifying resilience against connectivity challenges. Recent investigations underscore the efficacy of combining federated learning with Bayesian optimization to meet these criteria without compromising the model's effectiveness. therefore, we present BOFedSCDepth, an innovative approach that combines Bayesian optimization, federated learning, and deep self-supervision to train monocular depth estimators with improved effectiveness and efficiency compared to the current state-of-the-art method in self-supervised federated learning. Evaluation experiments conducted on the KITTI dataset showcase the superiority of our method, achieving a communication cost reduction of up to 33.3% and linear computational costs at the central server with no extra burden on autonomous vehicles.

关键词： Data privacy computer vision Costs Federated learning Computational modeling Depth measurement Bayes methods Optimization Autonomous vehicles intelligent transportation systems

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Hy-Tech Shopping Cart Using Object Detection and Human Following Technology

Hy-Tech Shopping Cart Using Object Detection and Human Follo...

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International conference on ICT in Business Industry & Government (ICTBIG)

作者： Anita Joshi Tanishq thuse Prashant thorve Raj thakur Anshul thote Isha thakur Department Of Engineering Science and Humanities Vishwakarma Institute Of Technology Pune India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

Recent advancements in artificial intelligence and robotics have transformed the retail industry, leading to innovative solutions that enhance the shopping experience. Our paper presents the High-Tech Shopping Cart, an intelligent system that leverages object detection and human-following technology to provide a seamless, hands-free shopping experience. the cart utilizes state-of-the-art object detection algorithms to identify and track items selected by shoppers while employing a robust human-following mechanism to navigate autonomously through stores. Our system integrates computer vision techniques, sensor fusion, and advanced path-planning algorithms within a modular framework, featuring cameras and depth sensors for real-time processing. Extensive experiments demonstrate high accuracy in object detection and reliable navigation in complex environments. User satisfaction surveys indicate a positive reception, highlighting the cart's potential to significantly enhance the shopping experience.

关键词： Surveys Navigation Service robots Object detection Sensor fusion Robot sensing systems Robustness Real-time systems Object recognition intelligent systems

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AI-based vision system for collision detection in HRC applications 9

AI-based vision system for collision detection in HRC applic...

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9th CIRP conference on Assembly Technology and Systems, CATS 2022

作者： Makris, Sotiris Aivaliotis, Panagiotis Laboratory for Manufacturing Systems and Automation Department of Mechanical Engineering and Aeronautics University of Patras Patras26504 Greece

Human-Robot Collaboration (HRC) enabling mechanisms require real-time detection of potential collisions among human and robots. Taking under consideration the already existing standards and the literature, most of collision detection techniques require the integration of sensorial systems on the robot aiming to identify the contact events. this paper deals with a novel approach for the identification of human and robot collision based on vision systems. Moreover, Artificial intelligent (AI) algorithms are required to classify the captured data near real-time and to provide a score about the collision status (contact or non-contact) between a human and the robot. Accordingly, the AI models should be trained using the appropriate image data enabling an accurate classification. the proposed system has been developed in a lab environment. A detailed presentation of the system implementation, its performance and the potential integration in a real industrial environment are discussed in this paper. © 2022 Elsevier B.V.. All rights reserved.

关键词： computer vision

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EFormer-VPR: Fusing Events and Frames with Transformer for Visual Place Recognition

EFormer-VPR: Fusing Events and Frames with Transformer for V...

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International conference on intelligent Transportation

作者： Yangjing Zhang Yanqing Shen Ziyu Zhu Renwei Hai Shitao Chen Nanning Zheng National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi P.R. China

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Visual place recognition (VPR) is a challenging task faced by mobile robots and autonomous driving systems. In scenarios with glare or high-speed motion, image blurring makes it difficult for traditional cameras to perform reliable and accurate place recognition. In contrast, event cameras can capture target motion information without blur in high-speed motion scenes, but lack texture information in low-speed motion scenes. To leverage the complementary characteristics of these two sensors and improve the performance and robustness of VPR algorithms, we propose EFormer- VPR, which fuses target motion events and frames with transformer. this method firstly preprocesses the events stream within an adaptive time window using a clustering method, then uses transformer-based networks to extract features from motion frames and events separately and fuse them through a scoring module. Finally, features are aggregated using a VLAD layer and the whole pipeline is supervised by a triplet ranking loss. To verify the effectiveness of the proposed algorithm, we compare it with other VPR methods on the event-based driving datasets (Brisbane-Event-VPR, NeuroGPR) with challenging scenarios. Experimental results show that on the Brisbane- Event- Vprand NeuroGPR datasets, our method achieves state-of-the-art performance.

关键词： Image recognition Fuses Clustering methods Robot vision systems Clustering algorithms Feature extraction Transformers Cameras Data mining Visual place recognition

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