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A walking assistance robot based on bionic traction characteristics

A walking assistance robot based on bionic traction characte...

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computer Engineering and Intelligent Control (ICCEIC) International conference on

作者： Yang Yu Rui Zhang Ziyan Tang Han Wang Xiaona Zhuang Ruofan Zhao Yusen Ma Peilin Gao Zhanzhi Gong Haibo Chen Thirteenth Avenue Binhai New District Tianjin University of Science and Technology Tianjin China

With the continuous development of social modernization, the research of various types of mobility aids is in full swing. However, most of the assistive devices on the market at present have the characteristics of single function, weak adaptability and low reliability, which makes it impossible for people to establish a trusting relationship with assistive devices and break through psychological barriers when traveling. the wearable walker uses the principles of ergonomics, kinematics, dynamics, bionics and other principles to design a walker for use in families and communities to help the elderly with lower limb movement and walking dysfunction caused by neurological diseases. Our research interests mainly include assisted exoskeletons, rehabilitation assistive devices, human softening belt design, fall protection system, rehabilitation evaluation system, and provide a complete rehabilitation diagnosis and treatment plan to help the elderly help rehabilitate walkers and gradually recover their walking ability. the implementation of the project involves computer vision, natural language processing, and multi-module collaborative analysis, which will help drive further research and development of walk-assist robots.

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Collaborative Decision-Making Models for Flexible Manufacturing robotic System

Collaborative Decision-Making Models for Flexible Manufactur...

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International conference on Modern Electrical and Energy Systems (MEES)

作者： Oleksandr Tsymbal Artem Bronnikov Department of Computer-Integrated Technologies Automation and Mechatronics Kharkiv National University of Radio Electronics Kharkiv Ukraine

ISBN: (纸本)9798350346848

the proposed paper analyses the modern approaches to collaborative robotics. there is proposed a new model of collaborative decision-making, based on predicate logics. the developed models use descriptions for group of robots, for workspace objects (monitored by distributed computer vision system), for problem-solving procedures, goals, specifications of robots and their sensor systems. While decision-making engine for single robot includes the problem- solver and operator schemes for actions, the decision-making for two robots will be defined by decision-making possibilities of each robot and role of robot in group. Here, the behavior of robot in a group will depend on technical specifications and ability to execute tasks of robotic team, of subordination and relations inside group, of workspace states specifications. Execution of tasks inside workspace of Flexible Manufacturing System is monitored by distributed computer vision system, which supplies additional feedbacks in general control system. Collaboration Decision-making Model is implemented by problem-solving software.

关键词： computer vision Computational modeling Decision making Collaboration robot sensing systems Problem-solving Task analysis

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Phoneme-Viseme Mapping for Sinhala Speaking robot for Sri Lankan Healthcare Applications 4

Phoneme-Viseme Mapping for Sinhala Speaking Robot for Sri La...

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4th IEEE Eurasia conference on Biomedical Engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Wakkumbura, W.G.V.K. Madhubhashana, R.A.H. Alahakoon, P.M.K. Kumara, W.G.C.W. Hinas, M.N.A. South Eastern University of Sri Lanka Department of Electrical and Telecommunication Engineering Oluvil Sri Lanka South Eastern University of Sri Lanka Department of Computer Science and Engineering Oluvil Sri Lanka

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

Speech perception is considered entirely as an auditory process, but vision also has a significant influence on speech perception. In generating synthesized vocal systems, especially for robotic applications, inaccurate synchronization between voice and lip movements substantially decreases the speech understanding and the naturalness of face-to-face communication. Phoneme-viseme mapping is one of the most important approaches in the visual recognition of speech and visual speech synthesis applications. Although there are many phoneme-viseme mapping models for languages such as English, Indonesian, Arabic, German, no adequate phoneme-viseme mapping model is available for the Sinhala language. this research proposes a methodology for Sinhala static viseme classification and establishes a phoneme-viseme mapping model for the Sinhala language. the Sinhala language is a low-resource language that belongs to the Indo-European sub-family, it has some similarities to the languages like Hindi, Marathi, and Bengali. the traditional Sinhala phonetic alphabet consists of 40 phonemes including 14 vowels and 26 consonants. this paper outlines the analysis of geometrical lip movements and features of the speakers pronouncing Sinhala word sequences which have been recorded in optimal conditions. Viseme classes are obtained through a static viseme approach where K-means clustering techniques and Sinhala linguistic features are considered. the proposed model was validated through a subjective analysis method and this is expected to grow into a reference model for future research attempts, as well as for developing an instructional robotic face that will form the visual interface for Sinhala-speaking healthcare seekers. © 2022 IEEE.

关键词： Mapping

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Augmentation of a Virtual Reality Environment Using Generative Adversarial Networks 4

Augmentation of a Virtual Reality Environment Using Generati...

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4th IEEE International conference on Artificial Intelligence and Virtual Reality (AIVR)

作者： Franchi, Valerio Ntagiou, Evridiki Univ Girona Comp Vis & Robot Inst ViCOROB Girona Spain European Space Agcy Dept Ground Syst Engn & Innovat Darmstadt Germany

ISBN: (纸本)9781665432252

Building a dataset to train machine and deep learning models has become a challenging task ever since more complex architectures and deeper neural networks have begun to be utilized with higher frequency, especially across computer vision applications in various fields. this hardship is prompted by the lack of data readiness and the difficulty associated with gathering enough of it, in order to avoid unbalanced datasets and overfitting during training. Occasionally, simulation environments are employed as a result of the unavailability of time and resources to collect real-world data. We present an application of Generative Adversarial Network (GAN) data augmentation in a vision-based planetary rover localisation application. Due to the absence of extra-terrestrial data, the system was trained on several artificially-built lunar terrains inside a Virtual Reality (VR) simulation environment. In order to further enhance our dataset, a GAN was utilized to augment the data retrieved from the simulation, while simultaneously averting a reduction in image quality, which is common with other forms of data augmentation. Despite the fact that GAN was trained with only a small number of images, it was able to recreate areas of the VR environment with high precision. Additionally, training the system with the GAN enhanced data improved the output compared to employing only basic data augmentation techniques.

关键词： virtual reality generative adversarial networks computer vision rover localisation

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An Intelligent Anti-theft Cargo Cart with AI Tracking and Camera Pan/Tile Locking Technology

An Intelligent Anti-theft Cargo Cart with AI Tracking and Ca...

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Electronics, Circuits and Information Engineering (ECIE), International conference on

作者： Chang-Hsi Wu Shao-Hsuan Lin Sian-Ci Cheng Rui-Ting Jiang Xiang-Yu Xie Institute of Electronic Engineering Lunghwa UST Taoyuan Taiwan

ISBN: (数字)9798350388312

ISBN: (纸本)9798350388329

this innovative research and development work integrates the mechanism and hardware specification design of Autonomous Mobile robots (AMR), PCB hardware and motor PID control firmware design for four-wheel drive tracking vehicles camera gimbal target recognition and aiming technology, AI vision tracking technology, and anti-theft video recording. It realizes smart vehicle services in large indoor areas such as stores, airports, exhibition halls, or medical institutions, offering personal belongings Cargo, automatic follow and obstacle avoidance, and theft prevention through interaction with a mobile app and the follow cart. through practical tests, we successfully integrated the aforementioned functionalities using the NVIDIA Jetson Xavier NX single-board computer. Achieved a maximum vehicle speed of 1 meters per second, endurance of 1 hour, trajectory tracking error within 75 cm under a refresh rate of 10 Hz, camera gimbal target locking and aiming time within 1 second, and AI vision tracking image processing speed of 8 FPS.

关键词： Target tracking Trajectory tracking Target recognition Prevention and mitigation robot vision systems Cameras Hardware

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Automatic Assembly of Bolts and Nuts Based on Machine vision Recognition 4

Automatic Assembly of Bolts and Nuts Based on Machine Vision...

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2021 4th International conference on Mechatronics and computer Technology Engineering, MCTE 2021

作者： He, Haocheng College of Mechatronics and Control Engineering Shenzhen University Shenzhen518060 China

In this study, based on the computer vision technology, we developed a recognition system for nuts positioning to complete the automatic bolt assembly part of the automatic production line. the actual image of the nut was captured by an industrial camera, which would be processed by the following edge detection and Hough circle transformation. After that, the coordinates of the nuts were obtained in the pixel scale. Finally, the real position of nuts would be fed back to the robot arm, according to which the automatic assembly of the bolt would be completed. this computer vision based recognition system is an indispensable part for the efficiency and accuracy improvement of automatic production line. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Bolts

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Depth Estimation in Static Monocular vision with Stereo vision Assisted Deep Learning Approach

Depth Estimation in Static Monocular Vision with Stereo Visi...

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computer, Control and robotics (ICCCR), International conference on

作者： Juzheng Zhang Xiao Da Terrence Fu Sutthiphong Srigrarom Mechanical Engineering National University of Singapore Singapore

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

this paper introduces a method for depth estimation that combines the strengths of geometric and deep learning approaches, tailored for use with fisheye cameras. By leveraging the wide field of view (FOV) provided by fisheye lenses and the accuracy of deep learning models, we aim to overcome the limitations inherent in traditional monocular and stereo vision depth estimation techniques. Our approach integrates a geometry-based stereo vision model with unparallel camera axes, specifically designed for fisheye cameras, with state-of-the-art deep learning methods for monocular depth estimation. We utilize existing datasets and pre-trained networks, including vision transformers and Fully Convolutional Networks (FCNs), to address the challenges of data scarcity and computational resource limitations. the methodology involves calibration of fisheye cameras, depth estimation from geometric and learning-based methods, and a novel combination of these methods to achieve higher accuracy and a broader FOV than currently possible with either approach alone. Our experiments, conducted using an RGB-D camera for validation, demonstrate the potential of our method to enhance depth map quality significantly. this research not only fills a gap in the literature by merging geometric and learning-based depth estimation for fisheye cameras but also proposes a practical application within the drone project's obstacle detection function. Future work will explore the refinement of the geometric model, the integration of different learning-based methods, and the development of a custom network to further improve depth estimation accuracy and applicabllity.

关键词： Deep learning Learning systems Accuracy Estimation Cameras Transformers robot sensing systems

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A Case Study in Generating 3D Maps with Collaborative robots

A Case Study in Generating 3D Maps with Collaborative Robots

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Electrical, computer, Communications and Mechatronics Engineering (ICECCME), International conference on

作者： theodor-Radu Grumeza thomas-Andrei Lazăr Isabela Drămnesc Gabor Kusper Konstantinos Papadopoulos Ioannis Lefkos Nikolaos Fachantidis Department of Computer Science West University of Timisoara Timisoara Romania Department of Computer Science Eszterhazy Karoly Catholic University Eger Hungary LIRES Lab University of Macedonia Thessaloniki Greece

ISBN: (数字)9798350391183

ISBN: (纸本)9798350391190

this research presents a case study on the creation of detailed 3D maps using the Agilex Scout Mini robot equipped with a 3D LiDAR, aimed at improving autonomous navigation in indoor environments. ROS (robot Operating System) is employed for real-time data acquisition and processing, and the DBSCAN clustering algorithm for segmenting point cloud data. these maps were subsequently integrated into the humanoid robot Pepper, developed by Aldebaran robotics, enabling Pepper to navigate autonomously and deliver location-based services at the West University of Timisoara. Our results show a point cloud density of approximately 5,000 points per cubic meter, highlighting the LiDAR sensor's capability to capture fine environmental details. Efficient clustering was achieved with an average computation time of 3 seconds per chunk, and path planning demonstrated a high smoothness score of 0.8, improving navigation efficiency. these findings contribute to the field by demonstrating the effectiveness of combining 3D mapping with autonomous robotics for reliable, real-time navigation. Moreover, the integration of Intel RealSense cameras is proposed as a future enhancement to improve object recognition and colour mapping, further expanding the system's potential applications. this case study not only advances academic understanding of autonomous robotic systems but also presents practical and scalable solutions for real-world navigation challenges, offering significant improvements in both accuracy and efficiency.

关键词： Point cloud compression three-dimensional displays Laser radar Navigation robot vision systems Clustering algorithms Real-time systems Path planning Reliability robots

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A Rain Image Generation Approach for Autonomous Driving Perception Sensors

A Rain Image Generation Approach for Autonomous Driving Perc...

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Electronic Information Engineering and computer Science (EIECS), 2021 International conference on

作者： Guozheng Song Shuang Song Sensor and Instrument Center Instrumentation Technology and Economy Institute Beijing China Xiamen University Xiamen China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

Proper rain image generation is important to assess the safety of autonomous perception system in rainy environment and improve perception performance. Rain image generation methods for autonomous driving vision sensors have become a research hotspot. the diversity of data can be extended and the performance of vision-based perception can be improved by effective real rain image generation. Although recent deep learning-based image generation of real rain has good performance, it is still an open problem for autonomous driving scenarios. Firstly, the existing rain images dataset only considers the streak characteristics produced by rain in modeling real rain features, and does not consider the interference of raindrops and fog on the image. Secondly, the distribution of the generated rain streaks, raindrops and fog lacks the influence of physical characteristics brought about by vehicle speed, rainfall and gravity during vehicle operation. Moreover, the lack of a real-time and efficient method for generating realistic raindrop images makes data enhancement and safety testing of sensing systems challenging. To solve the problem of real rain images generation for autonomous driving scenarios, we propose an efficient data generation method that combines physical properties and robot operating system to realize real-time generation of real rain for dynamic scenes and real-time data publish.

关键词： Rain Codecs Image synthesis vision sensors Real-time systems Safety Vehicle dynamics Autonomous vehicles Optical flow Testing

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Dyna VIO: Real-Time Visual-Inertial Odometry with Instance Segmentation in Dynamic Environments

Dyna VIO: Real-Time Visual-Inertial Odometry with Instance S...

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computer, Control and robotics (ICCCR), International conference on

作者： Feixiang Zheng Wanbiao Lin Lei Sun College of Artificial Intelligence Nankai University Tianjin China Shenzhen Research Institute Nankai University Shenzhen China

ISBN: (数字)9798350373141

ISBN: (纸本)9798350373158

Current state-of-the-art visual-inertial odometry algorithms work on the assumption that the observed features are absolutely static, which leads to their weakness in dynamic environments. In this paper, we propose a novel visual-inertial odometry framework that is capable of identifying and rejecting dynamic features, called DynaVIO. First, instance information of input images are collected to detect potential dynamic objects. then, consecutive masks of the same object are associated to maintain the information consistency of tracked features. Based on this, a dynamic probability propagation model is built to infer the state inconsistency between masks and static background, achieving fast and accurate identifications of dynamic objects. Finally, features on highly dynamic objects are rejected to avoid their influences on pose estimation tasks. Validation experiments on VIODE dataset proves that our algorithm perform better than other state-of-the-art methods.

关键词： Accuracy Heuristic algorithms Semantics Pose estimation robot vision systems Real-time systems Odometry

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