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9th IEEE International Smart Cities Conference, ISC2 2023

作者： Farman, Haleem Sedik, Admed Nasralla, Moustafa M. Esmail, Maged Abdullah College of Engineering Prince Sultan University Smart Systems Engineering Laboratory Department of Communications and Networks Engineering Riyadh Saudi Arabia Kafrelsheikh University Faculty of Artificial Intelligence Department of the Robotics and Intelligent Machines Kafrelsheikh Egypt

ISBN: (纸本)9798350397758

Facial Emotion Recognition (FER) plays a pivotal role in the realm of Smart Education Systems (SES), catering to the growing need for personalized and adaptive learning experiences. This paper presents a comprehensive review of the current state-of-the-art techniques and methodologies employed in FER in general and within the context of SES in particular. The primary objective of this review is to explore the FER technology for the enhancement of educational environments. The review begins by providing an insightful overview of the significance of emotions in education, leveraging deep learning approaches to elucidate the impact of emotional states on cognitive performance and academic outcomes. Subsequently, it explores the key components and working principles of FER systems, encompassing the extraction, representation, and classification of facial emotions. Furthermore, this paper highlights the updated literature in the field of FER applied to SES, ranging from real-time emotion analysis during classroom interactions to adapting instructional content based on learners' emotional states. Furthermore, the review critically analyzes the existing facial emotion recognition datasets utilized in facial emotion recognition research. The dataset is subcategorized in to two parts, images and videos, providing readers with convenience in understanding the various data sources employed in FER model training and evaluation. © 2023 IEEE.

关键词： Face recognition

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Cooperative Multi-source Data Trading

Cooperative Multi-source Data Trading

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Cheng, Jin Ding, Ningning Lui, John C. S. Huang, Jianwei The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China Hong Kong University of Science and Technology Data Science and Analytics Thrust Information Hub Guangzhou China The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong The Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network Csijri Joint Research Centre on Smart Energy Storage Shenzhen China

ISBN: (纸本)9798350351255

In the era of big data, data trading significantly enhances data-driven technologies by facilitating data sharing. Despite the clear advantages often experienced by data users when incorporating multiple sources, the topic of multi-source data trading remains largely unexplored. This paper designs a novel data trading framework, which enables multi-source data trading through multi-source cooperation. The proposed framework aims to improve data usage efficiency and increase seller revenue. In particular, we model data sellers' cooperative decisions through the Nash bargaining framework and systematically outline the interactions between sellers and buyers as a two-stage Stackelberg game. A key contribution of this work is the consideration of coupling among diverse data products, which is essential but often overlooked in prior studies. We properly classify data's utility into endogenous and relational categories to disentangle the coupling. Despite the inherent non-convex nature of the optimization problem, we methodically derive the closed-form optimal solutions by decomposing the problem into several subproblems. Interestingly, we reveal that, under our proposed framework, sellers' revenue initially remains steady with the increase of product coupling level, but begins to rise once the level exceeds a certain threshold due to the substitute effect. Finally, experimental results show that our proposed framework can improve the seller's profit by up to 46.32% compared to traditional data trading methods in the current data market. © 2024 IEEE.

关键词： Marketplaces

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Exploring the Developmental Aspects of the Uncanny Valley Effect on Children’s Preferences for Robot Appearance

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International Journal of Human-Computer Interaction 2025年第10期41卷 6366-6376页

作者： Chien, Sung-En Chen, Yu-Shan Chen, Yi-Chuan Yeh, Su-Ling Department of Psychology National Taiwan University Taiwan Ganzin Technology Inc Taiwan Department of Medicine Mackay Medical College Taiwan Graduate Institute of Brain and Mind Sciences National Taiwan University Taiwan Neurobiology and Cognitive Science Center National Taiwan University Taiwan Center for Artificial Intelligence and Advanced Robotics National Taiwan University Taiwan National Humanities Center Research Triangle Park NC United States

As robotic technology advances, robots are increasingly being designed and utilized for applications involving children across various fields. Since the acceptance of robots is profoundly affected by their appearance, understanding children’s preferences for the looks of robots becomes imperative. The uncanny valley effect (UVE) posits that the perceived likeability of robots increases as their appearance becomes more humanlike but then sharply drops when they approach near-perfect human similarity before rising once an extremely realistic likeness is achieved. Due to inconsistent results in the literature regarding when humans develop a sense of uncanniness toward humanlike robots, the present study aims to examine the emergence of the UVE in children. 67 1st-graders (aged six to seven years) and 95 4th-graders (aged nine to ten years) evaluated 12 robot faces on the scales of likeability, disgust, humanness, and intention to play with the robots (i.e., acceptance). The results demonstrated that the UVE was not observed in 1st-graders or 4th-grade boys. Nevertheless, a UVE-like pattern emerged among 4th-grade girls, particularly in their ratings of likeability and acceptance as a function of robots’ degree of humanness. A positive correlation between likeability and acceptance was consistently found across all age and gender groups. Additionally, negative correlations were found between likeability and disgust, as well as disgust and acceptance, specifically in the fourth graders but not in the first graders. These results suggest that the UVE may not be fully developed in 10-year-old children, with girls potentially exhibiting earlier development than boys. We conclude that age and gender differences should be considered when evaluating children’s preferences for robot appearance and their willingness to interact with robots. © 2024 Taylor & Francis Group, LLC.

关键词： Human robot interaction

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A Toeplitz Prior-Based Deep Learning Framework for DOA Estimation With Unknown Mutual Coupling 31

A Toeplitz Prior-Based Deep Learning Framework for DOA Estim...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Jiang, Zhuoqian Xin, Jingmin Zuo, Weiliang Zheng, Nanning Sano, Akira Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an710049 China National Engineering Laboratory for Visual Information Processing and Applications Xi'an Jiaotong University Xi'an710049 China Shunan Academy of Artificial Intelligence Zhejiang Ningbo315000 China Department of System Design Engineering Keio University Yokohama223-8522 Japan

ISBN: (纸本)9789464593600

In this paper, we explore the problem of direction-of-arrival (DOA) estimation with unknown mutual coupling using a deep learning (DL) framework which is based on Toeplitz prior. First, for source number estimation, we model it as a multilabel classification task and build a source number detection network (SNDN) to learn relevant information in the real sample covariance matrix. Next, taking full advantage of the Toeplitz structure, an ideal covariance reconstruction network (ICRN) is proposed to recover the ideal covariance matrix free from mutual coupling and noise interference. Furthermore, we design a database to store the parameters of ICRN after training on different numbers of sources, and its role is to load the corresponding parameters for ICRN according to the detection results of SNDN. Finally, the DOAs can be easily estimated from the restored covariance matrix by the MUSIC. The simulation results show our proposed approach not only outperforms the existing classical methods, but in some cases its DOA estimation accuracy can even exceed the Cramér-Rao Lower Bound. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Covariance matrix

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FlowNav: Combining Flow Matching and Depth Priors for Efficient Navigation

arXiv

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arXiv 2024年

作者： Gode, Samiran Nayak, Abhijeet Oliveira, Débora N.P. Krawez, Michael Schmid, Cordelia Burgard, Wolfram Artificial Intelligence and Robotics Lab Department of Computer Science and Artificial Intelligence University of Technology Nuremberg Germany Inria Ecole Normale Supérieure CNRS PSL Research University France

Effective robot navigation in unseen environments is a challenging task that requires precise control actions at high frequencies. Recent advances have framed it as an image-goal-conditioned control problem, where the robot generates navigation actions using frontal RGB images. Current state-of-the-art methods in this area use diffusion policies to generate these control actions. Despite their promising results, these models are computationally expensive and suffer from weak perception. To address these limitations, we present FlowNav, a novel approach that uses a combination of Conditional Flow Matching (CFM) and depth priors from off-the-shelf foundation models to learn action policies for robot navigation. FlowNav is significantly more accurate at navigation and exploration than state-of-the-art methods. We validate our contributions using real robot experiments in multiple unseen environments, demonstrating improved navigation reliability and accuracy. We make the code and trained models publicly available† Copyright © 2024, The Authors. All rights reserved.

关键词： Intelligent robots

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A CBF-Adaptive Control Architecture for Visual Navigation for UAV in the Presence of Uncertainties

A CBF-Adaptive Control Architecture for Visual Navigation fo...

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IEEE International Conference on robotics and Automation (ICRA)

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 Luleå University of Technology Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we propose a control solution for the safe transfer of a quadrotor UAV between two surface robots positioning itself only using the visual features on the surface robots, which enforces safety constraints for precise landing and visual locking, in the presence of modeling uncertainties and external disturbances. The controller handles the ascending and descending phases of the navigation using a visual locking control barrier function (VCBF) and a parametrizable switching descending CBF (DCBF) respectively, eliminating the need for an external planner. The control scheme has a backstepping approach for the position controller with the CBF filter acting on the position kinematics to produce a filtered virtual velocity control input, which an adaptive controller tracks to overcome modeling uncertainties and external disturbances. The experimental validation is carried out with a UAV that navigates from the base to the target using an RGB camera.

关键词： Visualization Uncertainty Target tracking Navigation Velocity control Robot vision systems Switches

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Collaborative Aerial 3D Printing: Leveraging UAV Flexibility and Mesh Decomposition for Aerial Swarm-Based Construction

Collaborative Aerial 3D Printing: Leveraging UAV Flexibility...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Marios-Nektarios Stamatopoulos Avijit Banerjee George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and Artificial Intelligence Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The article introduces a novel approach to foster collaborative aerial 3D printing by leveraging the dexterous flexibility of multiple UAVs working in tandem towards autonomous construction. This cooperative operation effectively overcomes payload limitations through synchronized deployment. Nevertheless, the transformation of UAVs into aerial construction agents poses a pivotal challenge, demanding efficient coordination of task planning and effective execution among multiple collaboratively working UAVs. In pursuit of addressing this challenge, the proposed innovative framework introduces a novel chunk-decomposition strategy supported by a reactive task assignment mechanism, dynamically allocating additive manufacturing tasks based on a dependency graph derived from decomposed chunks. Furthermore, the framework promotes parallelization by minimizing interdependencies thereby reducing the overall makespan. It also incorporates conflict resolution among UAVs during the assignment process by employing probabilistic fitness scores and penalizing the probability of conflicts. Conflicts that emerge during printing execution are addressed in a decentralized manner through trajectory sharing among UAVs. This entails dynamically determining one UAV suspending its movement until conflicts are resolved. The proposed framework's effectiveness is evaluated through a GAZEBO-based simulation setup, showcasing its potential in deploying multiple UAVs for the simultaneous printing of large-scale 3D structures. - Video overview: https://***/bLnzcLrD1NA

关键词： Solid modeling Three-dimensional displays Systematics Collaboration Three-dimensional printing Autonomous aerial vehicles Trajectory

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Towards Autonomous Robotic Ultrasound Scanning Using the Reinforcement Learning-Based Volumetric Data Navigation Method 5

Towards Autonomous Robotic Ultrasound Scanning Using the Rei...

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5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Shen, Cuifeng Deng, Zhaokun Wang, Jinzhi Wang, Shuangyi Chen, Chen Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong

ISBN: (纸本)9798350307320

Ultrasound scanning is an indispensable diagnostic tool in modern medicine, but obtaining an accurate standard plane relies heavily on the sonographer's skill and expertise. This paper presents a novel approach toward autonomous robotic ultrasound scanning using a reinforcement learning-based navigation method with volumetric ultrasound data. The proposed method enables an ultrasound robot to autonomously acquire high-quality 2D standard planes from patients, thereby reducing the need for human intervention. The core of the approach is a deep reinforcement learning algorithm that detects 2D standard planes within the 3D ultrasound volume and guides the ultrasound probe to extract high-quality standard planes. To assess the effectiveness of the 2D standard plane detection method, an evaluation was conducted using a realworld ultrasound dataset, and a comparison was made between its performance and that of other state-of-the-art autonomous approaches. The simulation experiment was also conducted to validate the autonomous robotic ultrasound acquisition workflow. Results demonstrate that the reinforcement learning-based volumetric data navigation method outperforms existing techniques in terms of standard plane accuracy and is very effective for volumetric data-based robotic navigation. © 2023 IEEE.

关键词： Diagnosis

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On Experimental Emulation of Printability and Fleet Aware Generic Mesh Decomposition for Enabling Aerial 3D Printing

On Experimental Emulation of Printability and Fleet Aware Ge...

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IEEE International Conference on robotics and Automation (ICRA)

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This article introduces an experimental emulation of a novel chunk-based flexible multi-DoF aerial 3D printing framework. The experimental demonstration of the overall autonomy focuses on precise motion planning and task allocation for a UAV, traversing through a series of planned space-filling paths involved in the aerial 3D printing process without physically depositing the overlaying material. The flexible multi-DoF aerial 3D printing is a newly developed framework and has the potential to strategically distribute the envisioned 3D model to be printed into small, manageable chunks suitable for distributed 3D printing. Moreover, by harnessing the dexterous flexibility due to the 6 DoF motion of UAV, the framework enables the provision of integrating the overall autonomy stack, potentially opening up an entirely new frontier in additive manufacturing. However, it’s essential to note that the feasibility of this pioneering concept is still in its very early stage of development, which yet needs to be experimentally verified. Towards this direction, experimental emulation serves as the crucial stepping stone, providing a pseudo mockup scenario by virtual material deposition, helping to identify technological gaps from simulation to reality. Experimental emulation results, supported by critical analysis and discussion, lay the foundation for addressing the technological and research challenges to significantly push the boundaries of the state-of-the-art 3D printing mechanism. - Full mission video available at https://***/gfZuYCA8jAw

关键词： Solid modeling Three-dimensional displays Coordinate measuring machines Emulation Three-dimensional printing Autonomous aerial vehicles Motion measurement

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Barriers on the EDGE: A scalable CBF architecture over EDGE for safe aerial-ground multi-agent coordination

arXiv

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arXiv 2024年

作者： Sankaranarayanan, Viswa Narayanan Seisa, Achilleas Santi Saradagi, Akshit Satpute, Sumeet Nikolakopoulos, George Robotics and Artificial Intelligence Group The Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

In this article, we address the problem of designing a scalable control architecture for a safe coordinated operation of a multi-agent system with aerial (UAVs) and ground robots (UGVs) in a confined task space. The proposed method uses Control Barrier Functions (CBFs) to impose constraints associated with (i) collision avoidance between agents, (ii) landing of UAVs on mobile UGVs, and (iii) task space restriction. Further, to account for the rapid increase in the number of constraints for a single agent with the increasing number of agents, the proposed architecture uses a centralized-decentralized Edge cluster, where a centralized node (Watcher) activates the relevant constraints reducing the need for high onboard processing and network complexity. The distributed nodes run the controller locally to overcome latency and network issues. The proposed Edge architecture is experimentally validated using multiple aerial and ground robots in a confined environment performing a coordinated operation. © 2024, CC BY.

关键词： Antenna grounds

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