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2022 Ural Symposium on Biomedical Engineering, Radioelectronics and Information Technology, USBEREIT 2022

作者： Sirenko, Pavel Zhariy, Sergey Fedotov, Nikolay Maurer, Danil Gerget, Olga Buller, Alexey The National Research Tomsk Polytechnic University School of Computer Science & Robotics Tomsk Russia Llc 'Laboratory of Medical Electronics 'BIOTOK' Tomsk Russia

ISBN: (数字)9781665460927

ISBN: (纸本)9781665460927

The publication is devoted to the development of a new stereofluoroscopy-based method of cardiac navigation, as well as the assessment of *** recent years, the improved efficiency of cardiac surgery is associated, first of all, with emerged high computer technologies, for example, with the processing of various kinds of video information received in real-time ***, there has been a tendency to use convolutional neural networks as an aid in diagnosing or monitoring the progress of an operation. The implementation of the cardiac navigation method using radiographic x-ray images is proposed based on these developing *** suggested method uses a software object detector based on a neural network and stereofluoroscopic images obtained on an X-ray complex consisting of two X-ray generators. The object detector model is responsible for detecting the catheter's coordinates in the left and right images. Then the third coordinate is calculated to construct the voxel. © 2022 IEEE.

关键词： Convolutional neural networks

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Collaborative Control of Aerial Robots for Inferring Human Intent from Gaze Following

Collaborative Control of Aerial Robots for Inferring Human I...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Chipade, Vishnu S. Gilbert, Alia Harari, Daniel Panagou, Dimitra University of Michigan Department of Aerospace Engineering Ann ArborMI United States University of Michigan Department of Robotics Ann ArborMI United States Weizmann Institute of Science Weizmann Ai Center Department of Computer Science and Applied Math Israel

ISBN: (纸本)9798350335446

In an ideal human-robot collaboration, autonomous robots work side-by-side with humans in a joint workspace, often performing complementary tasks to the humans. A robotic ability to infer human intention and goals directly from human behavior will facilitate the collaboration and maximize its efficiency. In this paper, we focus on inferring which object the human wants picked up next, based on what the human is looking at, by visually following the human gaze and head orientation. We develop a coordination protocol for a team of aerial robots to extract effective human head and gaze cues. The aerial robots are controlled to navigate around the human and collect data that improves the detection of the human's gaze and hence the intended object to be picked up. The effectiveness of the approach is shown using simulations in AirSim, a photo-realistic simulator. © 2023 IEEE.

关键词： Antennas

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Time-varying Control Barrier Function for Safe and Precise Landing of a UAV on a Moving Target

Time-varying Control Barrier Function for Safe and Precise L...

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

作者： Viswa Narayanan Sankaranarayanan Akshit Saradagi Sumeet Satpute George Nikolakopoulos Robotics and Artificial Intelligence Group of the Department of Computer Science Electrical and Space Engineering at Luleå University of Technology Sweden

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

In this article, we present a control barrier function (CBF)-based control strategy for safe and precise landing of an unmanned aerial vehicle (UAV) on a moving target. The CBF is time-varying, as it depends on the velocity of the landing platform and captures three crucial safety constraints: (a) collision avoidance with the landing platform, (b) precise vertical descent on a narrow landing platform, and (c) ground clearance throughout the landing maneuver. The proposed CBF’s parameters can be adjusted to set the desired width and height of the descending cone. A quadratic programbased CBF safety filter is designed, which takes a nominal position tracking control input and yields a minimally invasive control input that enforces the safety constraints throughout the landing maneuver. The controller’s feasibility is analyzed and its performance is validated through multiple experiments using a quadrotor UAV and an unmanned ground vehicle.

关键词： Uncertainty Minimally invasive surgery Autonomous aerial vehicles Land vehicles Safety Collision avoidance Intelligent robots Quadrotors

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ARI humanoid robot imitates human gaze behaviour using reinforcement learning in real-world environments

ARI humanoid robot imitates human gaze behaviour using reinf...

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IEEE-RAS International Conference on Humanoid Robots

作者： Khashayar Ghamati Abolfazl Zaraki Farshid Amirabdollahian School of Physics Engineering and Computer Science (SPECS) and Robotics Research Group of the University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9798350373578

ISBN: (纸本)9798350373585

This paper presents a novel approach to enhance the social interaction capabilities of the ARI humanoid robot using reinforcement learning. We focus on enabling ARI to imitate human attention/gaze behaviour by identifying salient points in dynamic environments, employing the Zero-Shot Transfer technique combined with domain randomisation and generalisation. Our methodology uses the Proximal Policy Optimisation algorithm, training the reinforcement learning agent in a simulated environment to maximise robustness in real-world scenarios. We demonstrated the efficacy of our approach by deploying the trained agent on the ARI humanoid and validating its performance in human-robot interaction scenarios. The results indicated that using the developed model, ARI can successfully identify and respond to salient points, exhibiting human-like attention/gaze behaviours, which is an important step towards acceptability and efficiency in humanrobot interactions. This research contributes to advancing the capabilities of social robots in dynamic and unpredictable environments, highlighting the potential of combining ZeroShot Transfer with domain randomisation and generalisation for robust real-world applications.

关键词： Training Heuristic algorithms Social robots Humanoid robots Reinforcement learning Robustness Optimization

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Research on natural environment protection based on blockchain technology to facilitate ecological balance and regional revitalization 4

Research on natural environment protection based on blockcha...

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4th International Conference on Industrial IoT, Big Data and Supply Chain, IIoTBDSC 2023

作者： Wan, Chenye Fu, Taoran Wang, Haochen Wang, Xing Luo, Junyou Wang, Ziqian School of Information Science and Technology Nantong University Nantong China School of Computer and Communication Hunan Institute of Engineering Xiangtan China School of Computational Science and Electronics Hunan Institute of Engineering Xiangtan China School of Robotics Hunan University Changsha China

ISBN: (纸本)9798350341690

Now the Masai Mara population continues to expand, the wildlife in the reserve is facing serious threats to their survival. The purpose of this report is to establish a management strategy model for local protected areas that balances the mutual interests between local people and wildlife in ***, Masai Mara divides the protected area into three parts, planting area, protected area, and livestock area, and then conducts correlation analysis on different areas, and concludes that the reduction in the number of wild animals is closely related to the degree of population density, and the area of cultivated land and forest area is highly correlated. So a nonlinear programming model of multiple strategies is established to plan the planting area. Then establish a comprehensive evaluation model based on the entropy weight method, quantify various strategies, and use gray correlation prediction to obtain solutions. This article introduces blockchain related technologies and uses its decentralization, security and transparency to build an architecture that contributes to the actual government protection of wild animals. © 2023 IEEE.

关键词： Conservation

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Adaptive Control of Euler-Lagrange Systems under Time-varying State Constraints without a Priori Bounded Uncertainty

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IFAC-PapersOnLine 2023年第2期56卷 3360-3365页

作者： Viswa Narayanan Sankaranarayanan Sumeet Satpute Spandan Roy George Nikolakopoulos Robotics and Artificial Intelligence Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden Robotics Research Center International Institute of Information Technology Hyderabad India

In this article, a novel adaptive controller is designed for Euler-Lagrangian systems under predefined time-varying state constraints. The proposed controller could achieve this objective without a priori knowledge of system parameters and, crucially, of state-dependent uncertainties. The closed-loop stability is verified using the Lyapunov method, while the overall efficacy of the proposed scheme is verified using a simulated robotic arm compared to the state of the art.

关键词： Adaptive control barrier Lyapunov function Euler-Lagrangian system

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Training Multi-Layer Perceptron with Enhanced Brain Storm Optimization Metaheuristics

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computers, Materials & Continua 2022年第2期70卷 4199-4215页

作者： Nebojsa Bacanin Khaled Alhazmi Miodrag Zivkovic K.Venkatachalam Timea Bezdan Jamel Nebhen Singidunum University Danijelova11000BelgradeSerbia National Center for Robotics and IoT Communication and Information Technology Research InstituteKing Abdulaziz City for Science and Technology(KACST)Riyadh12371Saudi Arabia Department of Computer Science and Engineering CHRIST(Deemed to be University)Bangalore560074India Prince Sattam Bin Abdulaziz University College of Computer Engineering and SciencesAlkharj11942Saudi Arabia

In the domain of artificial neural networks,the learning process represents one of the most challenging *** the classification accuracy highly depends on theweights and biases,it is crucial to find its optimal or suboptimal values for the problem at ***,to a very large search space,it is very difficult to find the proper values of connection weights and *** traditional optimization algorithms for this issue leads to slow convergence and it is prone to get stuck in the local *** commonly,back-propagation is used formulti-layer-perceptron training and it can lead to vanishing gradient *** an alternative approach,stochastic optimization algorithms,such as nature-inspired metaheuristics are more reliable for complex optimization tax,such as finding the proper values of weights and biases for neural network *** thiswork,we propose an enhanced brain storm optimization-based algorithm for training neural *** the simulations,ten binary classification benchmark datasets with different difficulty levels are used to evaluate the efficiency of the proposed enhanced brain storm optimization *** results show that the proposed approach is very promising in this domain and it achieved better results than other state-of-theart approaches on the majority of datasets in terms of classification accuracy and convergence speed,due to the capability of balancing the intensification and diversification and avoiding the local *** proposed approach obtained the best accuracy on eight out of ten observed dataset,outperforming all other algorithms by 1-2%on *** mean accuracy is observed,the proposed algorithm dominated on nine out of ten datasets.

关键词： Artificial neural network optimization metaheuristics algorithm hybridization brain storm optimization

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SEPL-Net: A Semantics-Enhanced Pseudo Labeling Network for Semi-Supervised Image Analysis

SEPL-Net: A Semantics-Enhanced Pseudo Labeling Network for S...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Xiao, Wenjing Hwang, Kai Chen, Min Li, Xianzhi School of Computer Science and Technology Huazhong University of Science and Technology China School of Data Science Chinese University of Hong Kong Hong Kong The Shenzhen Institute of Artificial Intelligence and Robotics for Society China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

As the mainstream solution for semi-supervised learning (SSL), pseudo-labeling-based approaches have achieved re-markable success. However, an obvious drawback of existing methods is that the valuable semantic relationships among categories are often ignored, thus leading to suboptimal encoded embeddings. To address this, we present a novel Semantics-Enhanced Pseudo Labeling Network, called SEPL-Net, for image analysis in a semi-supervised manner. SEPL-Net explores the prior knowledge of visual similarity between different classes to improve the quality of pseudo label decision making. Particularly, we encode semantic labels combined with the one-hot label to jointly train our network by exploiting their disagreement. To alleviate the difficulty of labeling unlabeled images due to the introduction of semantic labels, we further design different classifiers with differentiated strong augmentation modes to enable cooperative pseudo labeling. Extensive experimental results show that our SEPL-Net outperforms existing SSL methods with the averaged 1.84% accuracy improvement on image classification task. Code is available at https://***/sweetvicky/***. © 2022 IEEE.

关键词： Embeddings

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Hierarchical Pose Estimation and Mapping with Multi-scale Neural Feature Fields

arXiv

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arXiv 2024年

作者： Kruzhkov, Evgenii Savinykh, Alena Behnke, Sven Autonomous Intelligent Systems Computer Science Institute VI University of Bonn Bonn Germany Autonomous Intelligent Systems Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics Lamarr Institute for Machine Learning and Artificial Intelligence University of Bonn Bonn Germany

Robotic applications require a comprehensive understanding of the scene. In recent years, neural fields-based approaches that parameterize the entire environment have become popular. These approaches are promising due to their continuous nature and their ability to learn scene priors. However, the use of neural fields in robotics becomes challenging when dealing with unknown sensor poses and sequential measurements. This paper focuses on the problem of sensor pose estimation for large-scale neural implicit SLAM. We investigate implicit mapping from a probabilistic perspective and propose hierarchical pose estimation with a corresponding neural network architecture. Our method is well-suited for large-scale implicit map representations. The proposed approach operates on consecutive outdoor LiDAR scans and achieves accurate pose estimation, while maintaining stable mapping quality for both short and long trajectories. We built our method on a structured and sparse implicit representation suitable for large-scale reconstruction and evaluated it using the KITTI and MaiCity datasets. Our approach outperforms the baseline in terms of mapping with unknown poses and achieves state-of-the-art localization accuracy. © 2024, CC BY-NC-ND.

关键词： Mapping

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XR Teleoperation Demo Development

XR Teleoperation Demo Development

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Intelligent Metaverse Technologies & Applications (iMETA), International Conference on

作者： Matevž Borjan Zorec Farnaz Baksh Ulrich Norbisrath Robotics and Computer Engineering University of Tartu Tartu Estonia Institute of Computer Science University of Tartu Tartu Estonia

Developing teleoperation solutions can be challenging for students new to the field. Recent advances in autonomous driving led to a rising need for teleoperation experts. This paper presents a summarised and updated development and evaluation of RoverXR, a teleoperation demonstration for educational purposes featuring real-time direct visual feedback and showcasing the feasibility of fully open-source solutions for XR teleoperation setups. The conducted user evaluation reports an overall positive and engaging user experience showcasing the potential and challenges of low-latency, high-quality direct visual teleoperation combined with virtual scene representation. The developed teleoperation demonstration, documentation, and evaluation results are publicly available in the project's GitHub repository.

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