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International Conference on System Science and engineering (ICSSE)

作者： Shang-Wen Wong Yu-Chen Chiu Chi-Yi Tsai Doctoral Program in Robotics College of Engineering Tamkang University New Taipei City Taiwan Department of Electrical and Computer Engineering Tamkang University New Taipei City Taiwan

This paper proposes a pose estimation system for robot grasping based on a novel Object Affordance Detection and Segmentation (OADS) network. The proposed system consists of four modules: (1) OADS network; (2) point cloud extraction; (3) object pose estimation; (4) grasp pose estimation. Based on the OADS network, the proposed system achieves affordance-based object pose estimation results. The proposed grasp pose estimation system is evaluated on a laboratory-made dual-arm robot. Experimental results show that the proposed system achieves high detection rate and high accuracy in affordance detection and segmentation tasks, leading to a high success rate in object grasping tasks with lab-made dual-arm robot.

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Dual-UNet: An End-to-End Deep Learning Framework for the Automatic Chronic Diabetic Wound Segmentation

Dual-UNet: An End-to-End Deep Learning Framework for the Aut...

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Computing and Information Technology (ICCIT-1441), 2020 International Conference on

作者： Mustafa Alhababi Gregory Auner Hafiz Malik Lubna Alazzawi Muteb Aljasem Electrical and Computer Engineering Wayne State University Detroit MI USA Department of Smart Sensors and Integrated Microsystems Program Wayne State University Detroit MI USA Electrical and Computer Engineering University of Michigan-Dearborn Dearborn MI USA Electronics and Computer Engineering & Robotics Engineering Bowling Green State University Bowling Green OH USA

ISBN: (数字)9798350353839

ISBN: (纸本)9798350353846

Foot ulcers are one of the chronic wounds commonly occurring in diabetic patients and are associated with a high risk of major or minor amputations. Evaluation and measurement of such wounds are done manually by experts to assess the wound healing and suggest further treatment. Manual assessment methods can cause infections at times and are less feasible in the existence of pandemic situations. The development of artificial intelligence has brought a revolution in many domains of medical imaging. Deep learning-based computer-aided approaches can be applied to assist clinicians in assessing and monitoring chronic wounds. Wound segmentation helps to measure wound area which is useful for assessing its healing progress. In this research work, a deep learning-based segmentation method, namely Dual-UNet is presented for precisely segmenting the diabetic foot ulcers images. The foot ulcer images are passed to Dual-UNet to generate the mask image having the segmented wound area. We proposed two modified UNets each having a customized encoder and decoder with skip connections, atrous spatial pyramid pooling (ASPP) block, and output block to improve the segmentation results. We assessed our framework by performing experimentation on two benchmark datasets, named the foot ulcer segmentation (FUSeg) challenge and the AZH segmentation dataset. Additionally, we have also assessed our Dual-UNet for cross-corpora validation to demonstrate the generalization capability of the proposed approach. The qualitative and quantitative results highlight the accuracy and effectiveness of the Dual-UN et framework for segmenting chronic diabetic wounds. Future research should focus on the development of large and diverse wound datasets and improving the results for cross-corpora evaluation.

关键词： Deep learning Image segmentation Pandemics Computational modeling Manuals Wounds Diabetes Decoding Information technology Monitoring

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Development of Fish Separator Conveyor based on Fish Identification System 3

Development of Fish Separator Conveyor based on Fish Identif...

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3rd International Conference on Eco engineering Development, ICEED 2019

作者： Dzulfiqar Agung, Muhammad Rangga Elian, Adrian Adhe Astuti, Winda Puji, Muhammad Nurul Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia

Indonesia as the largest archipelago and maritime country in the world has a very large marine resources. Fish is one of the marine wealthties (marine biota), which have large numbers in Indonesia. Fishermen is an important role in the process of fishing, however fish caught by fishermen are often mixed with other types of fish. Currently, the process of fist separation is done manually, which takes longer time and many error occur due to fatigue. In this work the conveyer separator based on the fish identification system is developed. The fish conveyer separator is designed and developed. The fish identification system is based on Fast Fourier Transform (FTT) technique and Artificial Neural Network (ANN). The system is working properly, since it resulting the training and testing accuracy of 80% and 66%, respectively. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fast Fourier transforms

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3D Printed 6-Axis Collaborative Arm Robot Using Force Limiting Feature for Service Robot 3

3D Printed 6-Axis Collaborative Arm Robot Using Force Limiti...

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3rd International Conference on Eco engineering Development, ICEED 2019

作者： Saerang, Raymond Wiliem, David Tinggogoy, Feraldy Carles, Deddi Dani, M. Suwandi, Agri Oey, Endra Tan, Sofyan Automotive and Robotics Engineering Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia

Collaborative arm robot is a robot that can work alongside humans. By using force limiting (FL) feature, where the force of the arm robot is limited and controlled, the robot can sense collision and stop its movement when it occurs. The idea is to apply the mentioned concept for service purposes. In this research a 6-axis arm robot with collision detection is developed. The design of the arm robot adopted the design of Universal Robot's UR3 with each joint capable of 360° rotation. The design approach is chosen for its flexibility to work in tight space with a maximum reach of half meter, as it is intended to work closely with human. This initial version robot is not intended to carry heavy load or move at high precision. With a maximum load of one Kg, this arm robot targets numerous applications that help human to do complex repetitive tasks, extend the reach of human operators, and help humans on many daily chores that usually does not involve heavy loads. The whole frame, which also serve as the cover of the robot, was made by 3D printing with multiple materials that fit to the strength and temperature requirement at each part of the robot. In order to detect collision, the arm robot employed smart servos at each joint with the ability to detect overcurrent and overvoltage, which allow the implementation of force limiting (FL) feature that will stop the robot in case of collision. The resulting arm robot managed to lift1 kg load and able stop its movement in the event of collision. Future research to improve the movement precision of the arm robot by adaptive methods is promising. © 2020 Published under licence by IOP Publishing Ltd.

关键词： 3D printers

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Exploring the Science and Art of UAV Light Painting: From Equations and Pixels to Long-Exposure Photography

Exploring the Science and Art of UAV Light Painting: From Eq...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Leonardo A. Fagundes-Junior Celso O. Barcelos Raquel F. Vassallo Alexandre S. Brandão Department of Electrical Engineering Robotics Specialization Center Graduate Program on Computer Science Federal University of Viçosa Viçosa MG Brazil Department of Electrical Engineering Federal University of Espirito Santo Vitória ES Brazil

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The use of Unmanned Aerial Vehicles (in short, UAVs, aka drones) for cultural and entertainment purposes, such as drone light shows, has grown exponentially. One such innovative and creative application is the visual arts using drones to explore long-exposure photography. Light painting are generally performed indoors and outdoors in a dedicated space with a human using a moving light source to create spectacular images and save the movement perception in a picture. In this work, we propose a robotic perception system designed to choreograph UAV movements based on time-parametric curves or image edges, serving as reference motions. Our framework begins by processing a digital image, extracting its contours through boundary tracing, and subsequently generating a safe, navigable, and precise path for UAV motion planning. This process involves optimizing waypoints within the UAV workspace to determine a feasible trajectory that encompasses all designated points or computes safe trajectories utilizing established mathematical equations. The validation of the motion planning is performed through light painting, where the UAV can either fly through the motion reference to mimic the original image. The generated trajectories on light painting mode by physical robots are compared against the ground-truth demonstrating the accuracy of the applied control scheme.

关键词： Photography Visualization Art Autonomous aerial vehicles Trajectory Planning Robots

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License plate recognition system based on principal component analysis and one-against-one multi-class support vector machine 3

License plate recognition system based on principal componen...

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3rd International Conference on Eco engineering Development, ICEED 2019

作者： Kosasih, Kevin Kristofer Astuti, Winda Oey, Endra Automotive and Robotics Program Computer Engineering Department BINUS ASO School of Engineering Bina Nusantara University Jakarta11480 Indonesia

The license plate number which identifies the original registered place of the vehicle, as well as the usage of each vehicle which belongs to a private entity, a government vehicle, and a commercial vehicle, is identified in this work. Vehicle plate detection by a camera that mounted on parking gate. Detection process starts with collecting data using the camera. This work consists two important parts, namely image processing and classification system, respectively. In the image processing, the plate image capture by the camera is extracted to the feature space which will use as the input to the classification system. The classification system is then will identify the extracted input to the plat recognition identification. The Fast Fourier Transform (FFT) and Support Vector Machine (SVM) are applied as feature extraction technique and classification methods, respectively. The results of the training and testing phase are 80% and 66%, respectively. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Fast Fourier transforms

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Deep Transfer Learning-Based Demagnetization Analysis for Linear Oscillating Actuator Considering Circumferential Segmented Structure

Deep Transfer Learning-Based Demagnetization Analysis for Li...

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IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Ji-Hyeon Lee Soo-Hwan Park Du-Ha Park Jae-Hoon Jeong Myung-Seop Lim Department of Automotive Engineering (Automotive-Computer Convergence) Hanyang University Seoul Republic of Korea Department of Mechanical Robotics and Energy Engineering Dongguk University Seoul Republic of Korea H&A Fundamental Technology R&D Lab LG Electronics Seoul Republic of Korea

The linear oscillating actuator (LOA) achieves high efficiency and features a simple mechanical structure because it doesn’t require the conversion of rotational motion into linear motion. Therefore, the LOA is an appealing option for devices such as compressors, linear pump and automobile active suspension due to its high efficiency and power density. The stability of permanent magnets (PMs) can be impacted by different factors such as temperature, electromagnetic fields, and other external influences. In more severe cases, these factors can result in the occurrence of irreversible demagnetization, causing permanent damage to the magnetic properties of the PM. The irreversible demagnetization of permanent magnets impacts the electromagnetic functionality of the LOA, making it necessary to account for it during the design stage. However, the intricate configuration of the LOA such as divided outer stator and PMs diminishes accuracy of the 2-D finite element analysis (FEA). Despite its high computational cost for calculating accurate demagnetization ratio (DR), 3-D FEA is essential. Thus, we propose a demagnetization analysis based on transfer learning (TL) to reduce the computational cost associated with accurately calculating the 3-D FEA-based DR. This approach takes into account the permeance in the stator core and circumferential leakage flux. With TL, the parameters of pre-trained models learned from a source dataset in different but similar domains are transferred to learn the target dataset and effectively enhance the performance of neural network. The TL is conducted with a substantial dataset from 2-D FEA-based demagnetization ratio (DR) anda limited dataset from 3-D FEA-based DR. TL is a cognitive learning approach that utilizes knowledge acquired from a source task to enhance learning in a related but different target task were compared.

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Online 3D Bin Packing for Novel Objects Based on Deep Reinforcement Learning

Online 3D Bin Packing for Novel Objects Based on Deep Reinfo...

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International Conference on Advanced robotics and Intelligent Systems (ARIS)

作者： Shao-Yu Chien Ching-Chang Wong Doctoral Program in Robotics College of Engineering Tamkang University New Taipei City 25137 Taiwan Electrical and Computer Engineering Department Tamkang University New Taipei City 25137 Taiwan

In this paper, a method using deep reinforcement learning is proposed to deal with the 3D online bin packing problem. The packing objects are not limited to several specific or fixed cuboid objects, but are composed of more than a thousand objects and randomly generated cuboids, which make the trained policy network can handle novel unknown objects. In addition, the posture of the object in the box can be any angle, not limited to horizontal and vertical. In the proposed method, four voxel maps are used as inputs, and a Soft Actor-Critic (SAC) algorithm is used to train a policy network. On the other hand, in order to deal with various objects with irregular shapes, a packing task simulator with physics engine enable the policy network to learn the state of falling and stacking objects. In terms of training environment of deep reinforcement learning, the proposed method can be applied to boxes of different sizes because of the scalable image information. Moreover, a reward function and a training strategy with gradually increasing difficulty are proposed to effectively improve the learning of policy network. In terms of experimental results, the results on a random object bin packing task in a simulator illustrate the effectiveness of the proposed method.

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Introducing Spectral Attention for Long-Range Dependency in Time Series Forecasting

arXiv

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arXiv 2024年

作者： Kang, Bong Gyun Lee, Dongjun Kim, HyunGi Yoon, Sungroh Chung, DoHyun Interdisciplinary Program in Artificial Intelligence Seoul National University Korea Republic of Department of Electrical and Computer Engineering Seoul National University Korea Republic of Department of Future Automotive Mobility Seoul National University Korea Republic of

Sequence modeling faces challenges in capturing long-range dependencies across diverse tasks. Recent linear and transformer-based forecasters have shown superior performance in time series forecasting. However, they are constrained by their inherent inability to effectively address long-range dependencies in time series data, primarily due to using fixed-size inputs for prediction. Furthermore, they typically sacrifice essential temporal correlation among consecutive training samples by shuffling them into mini-batches. To overcome these limitations, we introduce a fast and effective Spectral Attention mechanism, which preserves temporal correlations among samples and facilitates the handling of long-range information while maintaining the base model structure. Spectral Attention preserves long-period trends through a low-pass filter and facilitates gradient to flow between samples. Spectral Attention can be seamlessly integrated into most sequence models, allowing models with fixed-sized look-back windows to capture long-range dependencies over thousands of steps. Through extensive experiments on 11 real-world time series datasets using 7 recent forecasting models, we consistently demonstrate the efficacy of our Spectral Attention mechanism, achieving state-of-the-art results. © 2024, CC BY-NC-ND.

关键词： Time series

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Renderable Street View Map-Based Localization: Leveraging 3D Gaussian Splatting for Street-Level Positioning

Renderable Street View Map-Based Localization: Leveraging 3D...

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

作者： Howoong Jun Hyeonwoo Yu Songhwai Oh Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of Department of Intelligent Robotics & Mechanical Engineering Sungkyunkwan University (SKKU) Suwon-si Korea Republic of Department of Electrical and Computer Engineering (ECE) & Interdisciplinary Program in Artificial Intelligence (IPAI) Seoul National University and Automation and Systems Research Institute (ASRI) and Sequor Robotics Inc. Seoul Korea Republic of

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this paper, we introduce a new method that first utilizes 3D Gaussian splatting in street-level localization problem. Robust localization with street-level real-world images such as street view is a major issue for autonomous vehicle, augmented reality (AR) navigation, and outdoor mobile robots. The objective is to determine the position and orientation of a query image that matches a street view database composed of RGB images. However, given the limited information available in the street view images, accurately determining the location solely based on this data presents a significant challenge. To address this challenge, we propose a novel method called renderable street view map-based localization (RSM-Loc). This approach enhances the localization process by augmenting 2D street view images into a renderable 3D map using 3D Gaussian splatting, to resolve street-level localization problems. Upon receiving a query RGB image without geometry information, the proposed method renders 2D images from a pre-made renderable map and compares image pose similarities between the rendered images and the query image. Through iterations of this process, the proposed method eventually estimates the pose of the given query image. The experimental results demonstrate that RSM-Loc outperforms the baselines with neural-field-based localization. Additionally, we conduct deep analysis on the proposed method to show that our method can serve as a new concept for the street-level localization problem.

关键词： Location awareness Visualization Matched filters Three-dimensional displays Image resolution Navigation Noise measurement Mobile robots Intelligent robots Image reconstruction

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