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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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Preliminary study of online real-time control system for lower extremity exoskeletons based on EEG and sEMG fusion

Preliminary study of online real-time control system for low...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Li, Wenju Shao, Keyong Zhu, Chi Ma, Yue Cao, Wujing Yin, Meng Yang, Lijun Luo, Mingxiang Wu, Xinyu Northeast Petroleum University School of Electrical Engineering Information Daqing China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong Provincial Key Lab of Robotics and Intelligent System Guangdong Shenzhen518055 China Maebashi Institute of Technology Department of Systems Life Engineering Maebashi3710132 Japan

ISBN: (纸本)9781665481090

Motion imaging (MI) is widely used in exoskele-ton robot control direction. However, online real-time con-trol of exoskeletons based on biological signals is difficult to achieve. Because the anti-interference of biological signals in real life is poor and affected by individual differences, the control of auxiliary equipment by the realization of human-machine interface is still facing great challenges. In this paper, a human-machine interface (HMI) based on hybrid biological signals is designed and online real-time control of lower extremity exoskeletons is realized. Based on a comprehensive analysis of human walking, the HMI uses the electromyography signals (upper extremities) and EEG signals generated during real exercise as input signals. Different analysis methods are designed for different modal signals. We extracted four features of sEMG signals to distinguish between different movement processes in the human body. For EEG signals, the use of Independent Component Analysis (ICA) increases the robustness of the system. Event-related desynchronization/synchronization (ERS/ERD) and power spectral densities are reused for analysis from the perspective of the interaction of multiple information in the channel, time and frequency domains. The human-machine interface integrates EEG and EMG multimodal biological signals, and the average recognition accuracy of different subjects has reached 93.7%. In order to increase the recognition accuracy of practical applications and reduce the differences between different individuals, a window voting mechanism is designed. Healthy subjects were tested, and the recognition accuracy of the lower limb exoskeleton online real-time assistance system reached 99%, with high accuracy and reliability. © 2022 IEEE.

关键词： Real time control

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A Scalable Real-time Semantic Segmentation Network for Autonomous Driving 1

A Scalable Real-time Semantic Segmentation Network for Auton...

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1st Workshop on Advanced Multimedia Computing for Smart Manufacturing and engineering, AMC-SME 2023

作者： Liu, Jun Wu, Chao Yuan, Geng Niu, Wei Zhang, Wenbin Song, Houbing Herbert Robotics Institute of School of Computer Science Carnegie Mellon University Pittsburgh United States Department of Electrical & Computer Engineering Northeastern University Boston United States School of Computing University of Georgia Athens United States Department of Computer Science Michigan Technological University Houghton United States Department of Information Systems University of Maryland Baltimore County Baltimore United States

ISBN: (纸本)9798400702730

Computational resources are limited on real-time embedded devices, so the available computing cost of deployment on the target platform must be considered. We develop a feature extraction module based on the MobileNet backbone that can be adjusted in terms of computational complexity and capacity using the depth multiplier parameter, classifier depth, and kernel depth. These three parameters allow us to control the count of channels within the network, effectively managing the model's capacity and computational requirements. To achieve semantic segmentation, we incorporate additional components, such as an extension module. This extension module typically includes 1x1 pointwise convolutional layers for pixel-level classification and a transposed convolutional layer for upsampling the output to the original input image size. By combining the feature extraction module with this extension module, we can create a complete architecture capable of performing semantic segmentation tasks. The feature extraction module provides the initial feature extraction and the extension module adds the necessary components for accurate pixel-wise classification and upsampling. Compared to Hardware-aware Neural Architecture Search (NAS), pruning, runtime pruning, and knowledge distillation methods, our model has several advantages in terms of modular design, structural controllability, ease of implementation, and cost-effectiveness. Our computational efficiency, as measured by FLOPS, is highly competitive. Our method is distinguished by solving the problem of MobileNet's inability to adjust the size and number of convolution kernels. It achieves this through adaptable parameter tuning, including MobileNet's depth multiplier, the kernel size in the FCN head's Separable Convolution layer, and the depth of the first Point-wise Convolution layer. These adjustments are customized to match hardware's max multiply-accumulates (MACs), optimizing network capacity and maximizing resource utilizat

关键词： Feature extraction

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Enhancing Precision Agriculture Through Human-in-the-Loop Planning and Control

Enhancing Precision Agriculture Through Human-in-the-Loop Pl...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Shankar A. Deka Sujet Phodapol Andreu Matoses Gimenez Victor Nan Fernandez-Ayala Rufus Wong Pian Yu Xiao Tan Dimos V. Dimarogonas Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems School of EECS Royal Institute of Technology (KTH) Stockholm Sweden Department of Cognitive Robotics Faculty of Mechanical Engineering TU Delft Delft Netherlands Department of Computer Science University of Oxford Oxford UK

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In this paper, we introduce a ROS based framework designed for the planning and control of robotic systems within the context of precision agriculture, with an emphasis on human-in-the-loop capabilities. Utilizing Linear Temporal Logic to articulate complex task specifications, our algorithm creates high-level robotic plans that are not only correct by design but also adaptable in real time by human operators. This dual-focus approach ensures that while humans have the flexibility to modify the high-level plan on-the-fly or even take over low-level control of the robots, the system inherently safeguards against any human actions that could potentially breach the predefined task specifications. We demonstrate our algorithm within the dynamic and challenging environment of a real vineyard, where the collaboration between human workers and robots is critical for tasks such as harvesting and pruning, and show the practical applicability and robustness of our software. This work marks a pioneering application of formal methods to complex, real-world agricultural environments.

关键词： Precision agriculture Heuristic algorithms Software algorithms Robustness Human in the loop Software Real-time systems Planning Robots Multi-agent systems

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Self-Healing First-Order Distributed Optimization with Packet Loss

arXiv

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arXiv 2023年

作者： Donato Ridgley, israel L. Freeman, Randy A. Lynch, Kevin M. Northwestern University EvanstonIL60208 United States Department of Electrical & Computer Engineering Department of Mechanical Engineering Northwestern Institute on Complex Systems United States Center for Robotics and Biosystems United States

We describe SH-SVL, a parameterized family of first-order distributed optimization algorithms that enable a network of agents to collaboratively calculate a decision variable that minimizes the sum of cost functions at each agent. These algorithms are self-healing in that their convergence to the correct optimizer can be guaranteed even if they are initialized randomly, agents join or leave the network, or local cost functions change. We also present simulation evidence that our algorithms are self-healing in the case of dropped communication packets. Our algorithms are the first single-Laplacian methods for distributed convex optimization to exhibit all of these characteristics. We achieve self-healing by sacrificing internal stability, a fundamental trade-off for single-Laplacian methods. © 2023, CC BY-SA.

关键词： Cost functions

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How to turn your camera into a perfect pinhole model

arXiv

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arXiv 2023年

作者： De Boi, Ivan Pathak, Stuti Oliveira, Marina Penne, Rudi InViLab University of Antwerp Groenenborgerlaan 171 Antwerp2020 Belgium Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3004-531 Portugal

Camera calibration is a first and fundamental step in various computer vision applications. Despite being an active field of research, Zhang's method remains widely used for camera calibration due to its implementation in popular toolboxes like MATLAB and OpenCV. However, this method initially assumes a pinhole model with oversimplified distortion models. In this work, we propose a novel approach that involves a pre-processing step to remove distortions from images by means of Gaussian processes. Our method does not need to assume any distortion model and can be applied to severely warped images, even in the case of multiple distortion sources, e.g., a fisheye image of a curved mirror reflection. The Gaussian processes capture all distortions and camera imperfections, resulting in virtual images as though taken by an ideal pinhole camera with square pixels. Furthermore, this ideal GP-camera only needs one image of a square grid calibration pattern. This model allows for a serious upgrade of many algorithms and applications that are designed in a pure projective geometry setting but with a performance that is very sensitive to nonlinear lens distortions. We demonstrate the effectiveness of our method by simplifying Zhang's calibration method, reducing the number of parameters and getting rid of the distortion parameters and iterative optimization. We validate by means of synthetic data and real world images. The contributions of this work include the construction of a virtual ideal pinhole camera using Gaussian processes, a simplified calibration method and lens distortion removal. Copyright © 2023, The Authors. All rights reserved.

关键词： Pinhole cameras

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Revisiting the K-nn algorithm for obstetric image segmentation

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International Journal of Hybrid Intelligent systems 2021年第1-2期17卷 1-13页

作者： Salgado, P. Azevedo Perdicoúlis, T.-P. CITAB-Department de Engenharias Ect Utad Quinta de Prados Vila Real Portugal Department of Electrical and Computer Engineering Ect Utad of Institute of Systems and Robotics University of Coimbra Coimbra Portugal

Medical image techniques are used to examine and determine the well-being of the foetus during pregnancy. Digital image processing (DIP) is essential to extract valuable information embedded in most biomedical signals. Afterwords, intelligent segmentation methods, based on classifier algorithms, must be applied to identify structures and relevant features from previous data. The success of both is essential for helping doctors to identify adverse health conditions from the medical images. To obtain easy and reliable DIP methods for foetus images in real-time, at different gestational ages, aware pre-processing needs to be applied to the images. Thence, some data features are extracted that are meant to be used as input to the segmentation algorithms presented in this work. Due to the high dimension of the problems in question, assemblage of the data is also desired. The segmentation of the images is done by revisiting the K-nn algorithm that is a conventional nonparametric classifier. Besides its simplicity, its power to accomplish high classification results in medical applications has been demonstrated. In this work two versions of this algorithm are presented (i) an enhancement of the standard version by aggregating the data apriori and (ii) an iterative version of the same method where the training set (TS) is not static. The procedure is demonstrated in two experiments, where two images of different technologies were selected: a magnetic resonance image and an ultrasound image, respectively. The results were assessed by comparison with the K-means clustering algorithm, a well-known and robust method for this type of task. Both described versions showed results close to 100% matching with the ones obtained by the validation method, although the iterative version displays much higher reliability in the classification. © 2021 - IOS Press. All rights reserved.

关键词： Feature extraction image segmentation K-nn algorithm magnetic resonance image ultrasound image

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AN ADDRESSABLE ELECTROWETTING VALVE FOR CENTRIFUGAL MICROFLUIDICS 26

AN ADDRESSABLE ELECTROWETTING VALVE FOR CENTRIFUGAL MICROFLU...

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26th International Conference on Miniaturized systems for Chemistry and Life Sciences, MicroTAS 2022

作者： Xia, Yanming Song, Chao Meng, Yingchao Xue, Peng deMello, Andrew J. Gao, Quan Stavrakis, Stavros Ma, Shenglin Cao, Xiaobao Department of Mechanical & Electrical Engineering Xiamen University Xiamen China Institute for Chemical and Bioengineering ETH Zürich Zürich8093 Switzerland Institute of Robotics and Intelligent Systems ETH Zürich Zürich8006 Switzerland Guangzhou Laboratory Guangdong Guangzhou510320 China

ISBN: (纸本)9781733419048

This paper presents and assesses an addressable electrowetting centrifugal (EWC) valve, which can rapidly and selectively open through (remote) control of the applied electric field. The utility of EWC valves is showcased by the implementation of fluid complexly controlling and CD4 cell antigens detection. © 2022 MicroTAS 2022 - 26th International Conference on Miniaturized systems for Chemistry and Life Sciences. All rights reserved.

关键词： Wetting

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Fast yet predictable braking manoeuvers for real-time robot control

arXiv

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arXiv 2023年

作者： Hamad, Mazin Gutierrez-Moreno, Jesus Kussaba, Hugo T.M. Mansfeld, Nico Abdolshah, Saeed Swikir, Abdalla Burgard, Wolfram Haddadin, Sami Robotics and Systems Intelligence Munich Institute of Robotics and Machine Intelligence Technical University of Munich Germany Franka Emika GmbH Munich Germany Department of Electrical and Electronic Engineering Omar Al-Mukhtar University Libya Department of Engineering University of Technology Nuremberg Germany Germany

This paper proposes a framework for generating fast, smooth and predictable braking manoeuvers for a controlled robot. The proposed framework integrates two approaches to obtain feasible modal limits for designing braking trajectories. The first approach is real-time capable but conservative considering the usage of the available feasible actuator control region, resulting in longer braking times. In contrast, the second approach maximizes the used braking control inputs at the cost of requiring more time to evaluate larger, feasible modal limits via optimization. Both approaches allow for predicting the robot's stopping trajectory online. In addition, we also formulated and solved a constrained, nonlinear final-time minimization problem to find optimal torque inputs. The optimal solutions were used as a benchmark to evaluate the performance of the proposed predictable braking framework. A comparative study was compiled in simulation versus a classical optimal controller on a 7-DoF robot arm with only three moving joints. The results verified the effectiveness of our proposed framework and its integrated approaches in achieving fast robot braking manoeuvers with accurate online predictions of the stopping trajectories and distances under various braking settings. © 2023, CC BY-NC-ND.

关键词： Braking

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Model-Free Predictive Control for Harmonic Suppression of PMSMs Based on Adaptive Resonant Controller

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IEEE Transactions on Energy Conversion 2025年

作者： Li, Teng Sun, Xiaodong Su, Zonghao Zha, Xuhao Dianov, Anton Prakht, Vladimir Demidova, Galina Ma, Jinlin Jiangsu University Automotive Engineering Research Institute Zhenjiang212013 China Wenzhou University Pingyang Institute of Intelligent Manufacturing Wenzhou325035 China Ural Federal University Department of Electrical Engineering Ekaterinburg620062 Russia ITMO University Faculty of Control Systems and Robotics Petersburg197101 Russia Jiangsu University School of Automotive and Traffic Engineering Zhenjiang212013 China

To simultaneously suppress the periodic and aperiodic disturbances experienced by permanent magnet synchronous motors in model predictive control, a model-free predictive current control with adaptive resonance control based on an ultra-local model is proposed. Firstly, the ultra-local models of the speed loop and the current loop are established respectively, and the unmodeled and uncertain unknown part F in the ultra-local model is estimated by using a high-order fast terminal sliding mode observer, and its stability is analyzed. Then, an adaptive resonant controller (ARC) is designed to suppress the periodic disturbance caused by the dead zone effect. The ARC and the model-free control based on the ultra-local model are combined to compensate for their respective shortcomings, achieving the suppression of periodic and aperiodic disturbances in the system. Finally, by comparing with the traditional duty cycle model predictive control and the model-free model predictive control without ARC, the proposed scheme is proved to be robust to periodic and aperiodic disturbances. © 2025 IEEE. All rights reserved.

关键词： Permanent magnets

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