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Development of Unmanned Cargo VTOL Aircraft 1

59th Symposium on engineering Modelling in Mechanics, 2020

作者： Czyba, Roman Gorol, Zbigniew Lemanowicz, Marcin Pawlak, Mariusz Sikora, Adam Faculty of Automatic Control Electronics and Computer Science Department of Automatic Control and Robotics Silesian University of Technology Akademicka Street 2A Gliwice44-100 Poland Faculty of Mechanical Engineering Department of Theoretical and Applied Mechanics Silesian University of Technology Akademicka Street 2A Gliwice44-100 Poland Faculty of Chemistry Department of Chemical Engineering and Process Design Silesian University of Technology Akademicka Street 2A Gliwice44-100 Poland

ISBN: (数字)9783030684556

ISBN: (纸本)9783030684549

The aim of the paper is to present a design cycle regarding to the construction of an unmanned cargo aircraft with own weight up to 25 kg, operating range of up to 100 km and AGL operating ceiling of up to 1 km. The task of the project team was to plan the design cycle, perform analytical calculations and CFD simulations, and finally to build an unmanned aircraft capable of lifting cargo. At the same time, a number of assumptions and technical regulations regarding constructions had to be met, which are specified in legal regulations regarding the given class of unmanned aerial vehicles. In order to achieve optimal UAV aerodynamic properties, including maximization of the payload while minimizing the own weight, aerodynamic calculations and CFD simulations were performed. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Unmanned aerial vehicles (UAV)

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Mobile Robot Navigation Based on Deep Reinforcement Learning with 2D-LiDAR Sensor using Stochastic Approach

Mobile Robot Navigation Based on Deep Reinforcement Learning...

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IEEE International Conference on Intelligence and Safety for robotics (ISR)

作者： Han Beomsoo Ankit A. Ravankar Takanori Emaru Faculty and Graduate Division of Human Mechanical Systems and Design Research group of Biomechanics and Robotics School of Engineering Hokkaido University Sapporo Hokkaido Japan

In recent years, there has been a significant progress in mobile robotics and their applications in different fields. Currently, mobile robots are employed for applications such as service robots for delivery, exploration, mapping, search and rescue, and warehouses. Recent advances in computing efficiency and machine learning algorithms have increased the variations of intelligent robots that can navigate autonomously using sensor data. Particularly, reinforcement learning has recently enjoyed a wide variety of success in controlling the robot motion in an unknown environment. However, most of the reinforcement learning-based navigation gets the path plan with a deterministic method, which results in some errors. Therefore, we present a navigation policy for a mobile robot equipped with a 2D range sensor based on the Proximal Policy Optimization of a stochastic approach. The tested algorithm also includes a stochastic operation, which simplifies the policy network model. We trained a differential drive robot in multiple training environments, and based on such stochastic learning, the training data accumulates faster than before. We tested our algorithm in a virtual environment and present the results of successful planning and navigation for mobile robots.

关键词： Location awareness Training Navigation Stochastic processes Training data Reinforcement learning Robot sensing systems

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Integrated framework for optimizing the viscoelastic response of laminated composites under hygrothermal conditions

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engineering Structures 2025年 338卷

作者： Ly, Duy-Khuong Vu, Ho-Nam Topal, Umut Han, Jang-Woo Lee, Jaehun Nguyen, Sy-Ngoc Laboratory for Computational Mechanics Institute for Computational Science and Artificial Intelligence Van Lang University Ho Chi Minh City Viet Nam Faculty of Civil Engineering School of Technology Van Lang University Ho Chi Minh City Viet Nam Department of Civil Engineering Faculty of Technology Karadeniz Technical University Trabzon Turkey Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul04620 Korea Republic of Department of Mechanical Design Engineering Kumoh National Institute of Technology 39117 Korea Republic of Ho Chi Minh City Viet Nam Vietnam National University-Ho Chi Minh City Ho Chi Minh City Viet Nam

This study introduces an innovative computational framework aimed at optimizing the stacking sequences and thickness of laminated composite structures to achieve optimal viscoelastic responses under various hygrothermal conditions. It is worth noting that computational work for viscoelastic problems typically demands substantial effort and time due to repetitive numerical steps. When incorporating optimization into these problems, the computational requirements increase considerably, making traditional methods less suitable for practical engineering applications. Therefore, the present framework utilizes a smoothed finite element technique named smoothed cell element-discrete shear gap with fast convergence. To reduce the computational workload and avoid the recursive algorithm of viscoelasticity, the computational processes are performed in the transformed Laplace domain to linearize the stress–strain relationship in an integral form. Moreover, the framework is integrated with the balance-enhanced symbiotic organism search algorithm, a robust metaheuristic, to create an efficient optimization computational framework. Through systematic analysis, this research contributes to the advancement of composite material design by offering a practical and effective solution for addressing both viscoelastic behavior and hygrothermal considerations in thin laminated structures. © 2025 Elsevier Ltd

关键词： Delamination

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Additive manufacturing of an automotive brake pedal by metal fused deposition modelling

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Materials Today: Proceedings 2021年 45卷 4601-4605页

作者： M.I.M. Sargini S.H. Masood Suresh Palanisamy Elammaran Jayamani Ajay Kapoor Faculty of Engineering Computing and Science Swinburne University of Technology Sarawak Malaysia Malaysia Automotive Robotics and IoT Institute Cyberjaya Malaysia Dept. of Mechanical and Product Design Engineering Swinburne University of Technology Hawthorn VIC Australia

This research is intended to study the possibility of implementing low-cost Additive Manufacturing (AM) technologies to produce metallic automotive brake pedal rapidly, covering the main aspects of design, material, and manufacturing. The main objective of this research is to explore new brake pedal design considering AM design freedom, parts consolidation with reduced mass, new AM material and application of metal-based AM fabrication technology. The most widely used AM technology, the Fused Deposition Modelling (FDM), has been used to produce metallic brake using the metal-polymer filaments followed by debinding and sintering processes. Finite Element Analysis (FEA) has been applied to analyze the feasibility of new brake pedal design for AM processing. The prototype of the FDM produced metal brake pedal has been physically tested to validate the FEA results and to evaluate the reliability of metal based FDM technology.

关键词： Additive Manufacturing Fused Deposition Modelling Automotive components Brake Pedal Finite Element Analysis

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Speech Enhancement System Using Lip-reading

Speech Enhancement System Using Lip-reading

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2020 IEEE International Conference on Artificial Intelligence in engineering and Technology, IICAIET 2020

作者： Matsui, Kenji Fukuyama, Kohei Nakatoh, Yoshihisa Kato, Yumiko O. Osaka Institute of Technology Faculty of Robotics Design Osaka Japan Kyushu Institute of Technology Electrical and Electronic Engineering Kitakyushu Japan School of Medicine St. Marianna University Kawasaki Japan

ISBN: (纸本)9781728169460

We have been developing a practical speech enhancement system that supports for laryngectomee. By interviewing users we captured essential issues, such as 'utilization of existing device', 'the appearance needs to be inconspicuous', and 'the device should be easy to use'. Considering those user's needs, we plan to use smart phone platform and develop speech enhancement application so that the users are just ordinary looking, and there is no need to buy any additional device. In order to realize such system, the key concept of our proposed system performs lip-reading and speech synthesis. In this study, we examined a lip-reading method that can recognize by registering the words that you want to speak and that is optimized for the user using a small amount of data. 36 viseme images were converted into very small data using VAE(Variational Auto Encoder), then the training data for word recognition model was generated. Viseme is a group of phonemes with identical appearance on the lips. Our viseme sequence representation with VAE was used to be able to adapt users with very small amount of training data set. Word recognition experiment using VAE encoder and CNN was performed with 20 Japanese words. The experimental result showed 65% recognition accuracy, and 100% including 1st and 2nd candidates. The lip-reading type speech enhancement seems appropriate for embedding mobile devices in consideration of both usability and small vocabulary recognition accuracy. © 2020 IEEE.

关键词： Smartphones

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2D Laser and 3D Camera Data Integration and Filtering for Human Trajectory Tracking

2D Laser and 3D Camera Data Integration and Filtering for Hu...

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IEEE/SICE International Symposium on System Integration

作者： Hamed Bozorgi Xuan Tung Truong Hung Manh La Trung Dung Ngo The More-Than-One Robotics Lab Faculty of Sustainable Design Engineering University of Prince Edward Island 550 University Ave Charlottetown Prince Edward Island Canada Advanced Robotics and Automation (ARA) Laboratory University of Nevada Reno USA

ISBN: (数字)9781728176581

ISBN: (纸本)9781728176598

This paper addresses a robust human trajectory tracking method through the data integration of 2D laser scanner and 3D camera. Mapping the deep learning-based 3D camera human detection onto the point cloud of the depth information to build up the 3D bounding box-represented human and using the state-of-the-art 2D laser-based leg detection are the main data streams of the human tracking system. The human-oriented global nearest neighbour (HOGNN) data association, inspired from the Hall's proxemics, was developed to improve both the 3D camera-based and 2D laser-based human detection techniques. The dual Kalman filters are employed to track the human trajectory in parallel. The integration of the 3D camera-based and 2D laser-based human tracking is the key function of the system providing the real-time feedback for both the HOGNN to reduce false-positives of the camera-based and laser-based human detection and the Kalman filter to enhance the quality of the human trajectory tracking under uncertain environmental conditions. We implemented the sensor integration on ROS and validated it through real-world experiments.

关键词： Three-dimensional displays Trajectory tracking Two dimensional displays Lasers Data integration Cameras Kalman filters

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Integration Of Computer Vision In A Testing Facility For Prosthetic Joint Inspection And Performance Assessment

Integration Of Computer Vision In A Testing Facility For Pro...

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International Conference on Mechatronics Technology (ICMT)

作者： Stephen Glanville Jun Jie Chong Aghil Jafari Appolinaire Etoundi Faculty of Environment and Technology Health and Applied Sciences and Bristol Robotics Laboratory University of the West of England Bristol United Kingdom Faculty of Mechanical Design and Manufacturing Engineering Newcastle University Singapore University of the West of England Bristol United Kingdom

ISBN: (纸本)9781665424608

This paper presents a computer vision system within a test facility for prosthetic joint inspection and performance by analysing range of motion data. Testing with individuals rather than using a testing facility can cause issues, such as irritation, pain and rubbing of the residual limb. The use of a computer vision system within a testing facility can assess the performance of a prosthetic device by capturing video, images, and calculating angle data for range of motion. Other benefits of using a testing facility include reduced testing times and energy for the end user and the ability to test rigorously. It can be concluded that the computer vision system can capturing accurate angle data for use within the testing facility. However, a discrepancy between 0.55 and 0.6 degrees was found when comparing the angle of the knee joint in the computer aided design model to the built environment. Further development of the code and the use of masking with colour coded targets would result in more accurate angle calculation and automation.

关键词： Knee Performance evaluation Computer vision Test facilities Pain Inspection Performance analysis

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BAN: A Universal Paradigm for Cross-Scene Classification Under Noisy Annotations From RGB and Hyperspectral Remote Sensing Images

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IEEE Transactions on Geoscience and Remote Sensing 2025年 63卷

作者： Chen, Wentang Wen, Yibin Zheng, Juepeng Huang, Jianxi Fu, Haohuan Sun Yat-sen University School of Artificial Intelligence Zhuhai519082 China Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Engineering Research Center for Design Engineering and Digital Twin of Zhejiang Province School of Mechanical Engineering Robotics Institute Hangzhou310027 China Southwest Jiaotong University Faculty of Geosciences and Engineering Chengdu610032 China China Agricultural University College of Land Science and Technology Beijing100107 China Ministry of Agriculture and Rural Affairs Key Laboratory of Remote Sensing for Agri-Hazards Beijing100125 China Tsinghua University Tsinghua Shenzhen International Graduate School Shenzhen518071 China Tsinghua University Ministry of Education Key Laboratory for Earth System Modeling Department of Earth System Science Beijing100190 China National Supercomputer Center in Shenzhen Shenzhen518071 China

While domain adaptation (DA) methods have made significant strides in remote sensing community, most current works assume that the source domain labels are accurate. However, limited emphasis has been placed on the scenario where source data are mislabeled with noisy annotations, which is more common in real applications and referred to as noisy DA (NDA). This article formulates remote sensing cross-scene classification on NDA scenarios and proposes a novel network called bilateral adaptation network (BAN), which consists of two parts: 1) forward learning (FL), which utilizes a model learning from the noisy source domain and transfers knowledge to target domain;and 2) backward learning (BL), which utilizes a dual model to acquire knowledge from the target domain and transfer it to source domain. We conduct two parts alternately and adopt a symmetrical Kullback-Leibler (KL) loss to align predictions of the model and its dual model in the same domain. This interactive strategy is able to explore bilateral relationships between domains, implicitly reducing label noise in the source domain. In addition, BAN could serve as a universal paradigm to not only improve the existing NDA methods but also enhance recent DA approaches. Comprehensive evaluations on three publicly available RGB-band remote sensing datasets and two hyperspectral datasets validate the superior effectiveness of our proposed BAN. BAN improves the average accuracy by 6.70%-15.70% on RGB datasets and overall accuracy (OA) by 1.36%-3.14% on hyperspectral datasets with flip-20% noise compared to other state-of-the-art DA and NDA approaches. Promising results indicate the potential of our approach in tackling more general and practical problems with noisy source domain. © 2025 IEEE.

关键词： Contrastive Learning

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Efficient Visual Perception of Human-Robot Walking Environments Using Semi-Supervised Learning

Efficient Visual Perception of Human-Robot Walking Environme...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Dmytro Kuzmenko Oleksii Tsepa Andrew Garrett Kurbis Alex Mihailidis Brokoslaw Laschowski Department of Mathematics National University of Kyiv-Mohyla Academy Kyiv Ukraine Department of Mathematical Methods of System Analysis Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Department of Computer Science University of Toronto Toronto Canada Department of Systems Design Engineering University of Waterloo Waterloo Canada Temerty Faculty of Medicine University of Toronto Toronto Canada Institute of Biomedical Engineering University of Toronto Toronto Canada Toronto Rehabilitation Institute Toronto Canada Department of Mechanical and Industrial Engineering Robotics Institute University of Toronto Toronto Canada

Convolutional neural networks trained using supervised learning can improve visual perception for human-robot walking. These advances have been possible due to largescale datasets like ExoNet and StairNet - the largest open-source image datasets of real-world walking environments. However, these datasets require vast amounts of manually annotated data, the development of which is time consuming and labor intensive. Here we present a novel semi-supervised learning system (ExoNet-SSL) that uses over 1.2 million unlabelled images from ExoNet to improve training efficiency. We developed a deep learning model based on mobile vision transformers and trained the model using semi-supervised learning for image classification. Compared to standard supervised learning (98.4%), our ExoNet-SSL system was able to maintain high prediction accuracy (98.8%) when tested on previously unseen environments, while requiring 35% fewer labelled images during training. These results show that semi-supervised learning can improve training efficiency by leveraging large amounts of unlabelled data and minimize the size requirements for manually annotated images. Future research $\text{will}$ focus on model deployment for onboard real-time inference and control of human-robot walking.

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DECOMPOSITION OF INDUSTRIAL SYSTEMS FOR ENERGY EFFICIENCY OPTIMIZATION WITH OPTTOPO

arXiv

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arXiv 2022年

作者： Thiele, Gregor Johanni, Theresa Sommer, David Krüger, Jörg Department for Process Automation and Robotics Fraunhofer Institute for Production Systems and Design Technology IPK Germany Faculty of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany Weierstrass Institute for Applied Analysis and Stochastics Berlin Germany Institute for Machine Tools and Factory Management Technical University of Berlin Berlin Germany

The operation of industrial facilities is a broad field for optimization. Industrial plants are often a) composed of several components, b) linked using network technology, c) physically interconnected and d) complex regarding the effect of set-points and operating points in every entity. This leads to the possibility of overall optimization but also to a high complexity of the emerging optimization problems. The decomposition of complex systems allows the modeling of individual models which can be structured according to the physical topology. A method for energy performance indicators (EnPI) helps to formulate an optimization problem. The optimization algorithm OptTopo achieves efficient set-points by traversing a graph representation of the overall system. © 2022, CC BY.

关键词： Optimization

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