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2nd IEEE International Conference on Artificial Intelligence in Everything, AIE 2022

作者： Fotso Kamsu, Brigitte Vubangsi, Mercel Tchoffo, M. Al-Turjman, Fadi University of Dschang Condensed Matter Electronics and Signal Processing Research Unit Department of Physics Faculty of Science Cameroon Near East University Ai and Robotics Institute Artificial Intelligence Engineering Dept. Nicosia Turkey University of Bamenda Nwr Computational Materials Science Lab Department of Computer Science Htttc Bambili Cameroon University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Cyprus

ISBN: (数字)9781665474009

ISBN: (纸本)9781665474009

We model a compositionally graded heterostructure using the effective mass approach with a screened Coulomb potential. Approximate analytical solutions to the model are compared with a Neural Network output and good agreement is achieved, paving the way for Artificial Intelligence modeling of systems with graded composition. © 2022 IEEE.

关键词： Grading

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Robots Taking Initiative in Collaborative Object Manipulation: Lessons from Physical Human-Human Interaction

arXiv

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arXiv 2023年

作者： Rysbek, Zhanibek Oh, Ki Hwan Shervedani, Afagh Mehri Klemenčič, Timotej Žefran, Miloš Di Eugenio, Barbara Robotics Lab Department of Electrical and Computer Engineering The University of Illinois at Chicago ChicagoIL60607 United States Laboratory of Robotics Faculty of Electrical Engineering University of Ljubljana Slovenia Natural Language Processing Lab Department of Computer Science The University of Illinois at Chicago ChicagoIL60607 United States

Physical Human-Human Interaction (pHHI) involves the use of multiple sensory modalities. Studies of communication through spoken utterances and gestures are well established, but communication through force signals is not well understood. In this paper, we focus on investigating the mechanisms employed by humans during the negotiation through force signals, and how the robot can communicate task goals, comprehend human intent, and take the lead as needed. To achieve this, we formulate a task that requires active force communication and propose a taxonomy that extends existing literature. Also, we conducted a study to observe how humans behave during collaborative manipulation tasks. An important contribution of this work is the novel features based on force-kinematic signals that demonstrate predictive power to recognize symbolic human intent. Further, we show the feasibility of developing a real-time intent classifier based on the novel features and speculate the role it plays in high-level robot controllers for physical Human-Robot Interaction (pHRI). This work provides important steps to achieve more human-like fluid interaction in physical co-manipulation tasks that are applicable and not limited to humanoid, assistive robots, and human-in-the-loop automation. Copyright © 2023, The Authors. All rights reserved.

关键词： Human robot interaction

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Extremely Weak Feedback Method for Controlling Chaotic Resonance

Extremely Weak Feedback Method for Controlling Chaotic Reson...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Takahiro Iinuma Yudai Ebato Sou Nobukawa Anh Tu Tran Nobuhiko Wagatsuma Keiichiro Inagaki Hirotaka Doho Teruya Yamanishi Haruhiko Nishimura Graduate School of Information and Computer Science’ Chiba Institute of Technology Chiba Japan Department of Computer Science Chiba Institute of Technology Chiha Japan Department of Information Science Faculty of Science Toho University Chiba Japan Department of Artificial Intelligence and Robotics Chubu University Aichi Japan Faculty of Education Teacher Training Division Kouchi University Kochi Japan Faculty of Data Science Osaka Seikei University Osaka Japan Faculty of Informatics Yamato University Osaka Japan

Chaotic resonance, resembling stochastic resonance, is induced by internal fluctuations (i.e., chaos). This phenomenon has been observed in numerous systems. The most representative form of chaotic resonance is the synchronization with respect to weak applied signals under chaos-chaos intermittency, where a chaotic orbit moves among multiple attractors. Chaotic resonance exhibits higher sensitivity than stochastic resonance, but its engineering applications are limited by concerns such as the requirement to adjust the state of chaos by internal system parameters to induce chaotic resonance rather than the external noise strength. However, achieving this adjustment is challenging, especially in biological systems. Therefore, to handle this limitation, we proposed a novel double-Gaussian-filtered reduced region of orbit (RRO) method (called the DG-RRO method) for obtaining perturbed feedback signals lower than the conventional RRO method. This DG-RRO feedback signal is determined by the inverse sign of the map function and double-Gaussian filters around the local maximum/minimum values of the map. Because of its fine local specification, the DG-RRO feedback signal induces a chaotic resonance by one-third the feedback strength of the conventional signal. This method may pave the way for using chaotic resonance in engineering applications.

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Natural Language and LLMs in Human-Robot Interaction: Performance and Challenges in a Simulated Setting

Natural Language and LLMs in Human-Robot Interaction: Perfor...

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Human-computer Interaction, Optimization and Robotic Applications (HORA), International Congress on

作者： Kelvin Olaiya Giovanni Delnevo Chiara Ceccarini Chan-Tong Lam Giovanni Pau Paola Salomoni Dept. of Computer Science and Engineering University of Bologna Italy Faculty of Applied Sciences Macao Polytechnic University Macao SAR China Autonomous Robotics Research Center Technology Innovation Institute Abu Dhabi United Arab Emirates

ISBN: (数字)9798331510886

ISBN: (纸本)9798331510893

Natural language provides an intuitive and accessible way for humans to communicate with robots, fostering more natural and flexible interaction across a range of tasks. This study investigates how effectively users can command a robot using natural language within a simulated environment. By employing Gemini Flash 2.0 as the underlying Large Language Model (LLM) to interpret and translate user prompts into executable plans, we explore both the strengths and limitations of this approach. The experiments evaluated user-generated prompts across multiple predefined tasks, revealing a spectrum of outcomes — from successful task completions to errors such as misinterpretations, spatial failures, and hallucinated behaviors where the robot acted on non-existent information. The results highlight how different communication strategies, combining directive and conversational phrasing, influenced task performance. This work contributes to advancing Human-Robot Interaction (HRI) design by emphasizing the potential of LLM-powered systems while addressing the challenges of ambiguity and error resilience in user-driven command structures.

关键词： Human computer interaction Translation Large language models Zero shot learning Human-robot interaction Robots Optimization Resilience

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Parallel Path Planning for Multi-Robot Coordination

Parallel Path Planning for Multi-Robot Coordination

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IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)

作者： Jorge Ribeiro Marina Brilhante Diogo Miguel Matos Carlos A. Silva Héber Sobreira Pedro Costa CRIIS - Centre of Robotics in Industry and Intelligent Systems INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal Faculty of Engineering University of Porto Porto Portugal Flowbotic Mobile Systems S.A Viseu Portugal

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Multi-robot coordination aims to synchronize robots for optimized, collision-free paths in shared environments, addressing task allocation, collision avoidance, and path planning challenges. The Time Enhanced A* (TEA*) algorithm addresses multi-robot pathfinding offering a centralized and sequential approach. However, its sequential nature can lead to order-dependent variability in solutions. This study enhances TEA* through multi-threading, using thread pooling and parallelization techniques via OpenMP, and a sensitivity analysis enabling parallel exploration of robot-solving orders to improve robustness and the likelihood of finding efficient, feasible paths in complex environments. The results show that this approach improved coordination efficiency, reducing replanning needs and simulation time. Additionally, the sensitivity analysis assesses TEA*'s scalability across various graph sizes and number of robots, providing insights into how these factors influence the efficiency and performance of the algorithm.

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Convolutional Neural Network and Adversarial Autoencoder in EEG images classification 5

Convolutional Neural Network and Adversarial Autoencoder in ...

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5th Scientific School on Dynamics of Complex Networks and their Applications, DCNA 2021

作者： Nasybullin, Albert Kurkin, Semen Faculty of Computer Science and Engineering Innopolis University Innopolis Russia Neuroscience and Cognitive Technology Laboratory Center for Technologies in Robotics and Mechatronics Components Innopolis University Innopolis Russia

ISBN: (纸本)9781665442824

In this paper, we consider applying computer vision algorithms for the classification problem one faces in neuroscience during EEG data analysis. Our approach is to apply a combination of computer vision and neural network methods to solve human brain activity classification problems during hand movement. We pre-processed raw EEG signals and generated 2D EEG topograms. Later, we developed supervised and semi-supervised neural networks to classify different motor cortex activities. © 2021 IEEE

关键词： computer vision

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Prototype of Turbine Propulsion System with Controllable Thrust Vector for Long-Range Unmanned VTOL Aircraft 21st

Prototype of Turbine Propulsion System with Controllable Thr...

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21st Polish Control Conference, PCC 2023

作者： Czyba, Roman Lemanowicz, Marcin Domin, Jaroslaw Grzybowski, Jacek Stępień, Izabela Ungier, Lukasz Rosiek, Radoslaw Bialas, Piotr Niemiec, Michal Department of Automatic Control and Robotics Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland Department of Chemical Engineering and Process Design Faculty of Chemistry Silesian University of Technology ul. Ks. M. Strzody 7 Gliwice44-100 Poland Department of Mechatronics Faculty of Electrical Engineering Silesian University of Technology ul. Akademicka 10a Gliwice44-100 Poland

ISBN: (纸本)9783031351723

The aim of the project was to build a prototype of a turbine drive with a controllable thrust vector for a long-range vertical take-off and landing (VTOL) aircraft. The construction of the prototype required the development of a vectoring system for the tail propulsion system and a dedicated VTOL aircraft stabilization and control system. The prototype of the turbine drive system was based on energy-efficient electric motors. Research also involved modeling the turbine propulsion aerodynamics with usage of computational fluid dynamics (CFD) analysis. As a result of the design stage, a prototype of the propulsion system was created, on which flight tests were carried out. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Computational fluid dynamics

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Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar for Precise Robot Localization without a Base Station

Factor Graph Fusion of Raw GNSS Sensing with IMU and Lidar f...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Jonas Beuchert Marco Camurri Maurice Fallon Dept. of Computer Science Univ. of Oxford UK Dept. of Eng. Science Oxford Robotics Inst. Univ. of Oxford UK Faculty of Science & Technology Free Univ. of Bozen-Bolzano Italy

Accurate localization is a core component of a robot's navigation system. To this end, global navigation satellite systems (GNSS) can provide absolute measurements outdoors and, therefore, eliminate long-term drift. However, fusing GNSS data with other sensor data is not trivial, especially when a robot moves between areas with and without sky view. We propose a robust approach that tightly fuses raw GNSS receiver data with inertial measurements and, optionally, lidar observations for precise and smooth mobile robot localization. A factor graph with two types of GNSS factors is proposed. First, factors based on pseudoranges, which allow for global localization on Earth. Second, factors based on carrier phases, which enable highly accurate relative localization, which is useful when other sensing modalities are challenged. Unlike traditional differential GNSS, this approach does not require a connection to a base station. On a public urban driving dataset, our approach achieves accuracy comparable to a state-of-the-art algorithm that fuses visual inertial odometry with GNSS data-despite our approach not using the camera, just inertial and GNSS data. We also demonstrate the robustness of our approach using data from a car and a quadruped robot moving in environments with little sky visibility, such as a forest. The accuracy in the global Earth frame is still 1–2 m, while the estimated trajectories are discontinuity-free and smooth. We also show how lidar measurements can be tightly integrated. We believe this is the first system that fuses raw GNSS observations (as opposed to fixes) with lidar in a factor graph.

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Development of Hydrophobic Ptfe Coatings by Electrophoretic Deposition and Heat Treatment for Low-Friction Applications

SSRN

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SSRN 2024年

作者： Fiolek, Aleksandra Zimowski, Slawomir Moskalewicz, Tomasz AGH University of Krakow Faculty of Metals Engineering and Industrial Computer Science Mickiewicza Av. 30 Kraków30 - 059 Poland AGH University of Krakow Faculty of Mechanical Engineering and Robotics Mickiewicza Av. 30 Kraków30-059 Poland

This study investigated the possibility of producing polytetrafluoroethylene (PTFE) coatings on Ti-6Al-4V titanium alloy substrates using electrophoretic deposition (EPD) and heat treatment. Three different size of PTFE particles and three different surfactants, namely chitosan (CS), sodium alginate (SA), and PAZO, were used to facilitate the deposition process. All polyelectrolytes were effective and enabled coating deposition regardless of the size of the PTFE particles. As a result of heat treatment, PTFE changed its morphology from particles to a continuous coating. All coatings showed high open porosity, and in some of them a network of microcracks was observed, which excluded them from further tests. PTFE coatings including a mixture of nanoparticles and submicroparticles deposited without mixing with CS or SA showed very high adhesion to the substrate and relatively high scratch resistance. The coating deposited with CS had a higher roughness compared to that of the coating deposited with SA. The coatings were characterized by a high contact angle, i.e. 145.4±1.1° and 133.4±4.0° for the coatings deposited with CS and SA, respectively, indicating their high hydrophobicity. The coatings showed very good sliding properties until the continuity of the coating was ensured in the friction contact. The friction coefficient with the Al2O3 ball was 0.09 and 0.11 for the coatings obtained with CS and SA, respectively. A thin tribofilm was formed on the surface of the ball at the point of contact, which ensured sliding. However, the wear resistance of the coatings was low and they were subject to significant deformation and intense abrasion. This work contributes to understanding of the PTFE coating production processes and their resulting properties, with implications for various applications. © 2024, The Authors. All rights reserved.

关键词： Titanium alloys

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Self Decoupling-Reconstruction Network for Facial Expression Recognition

Self Decoupling-Reconstruction Network for Facial Expression...

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International Joint Conference on Neural Networks (IJCNN)

作者： Linhuang Wang Xin Kang Fei Ding Hai-Tao Yu Yunong Wu Kazuyuki Matsumoto Satoshi Nakagawa Fuji Ren Graduate School of Advanced Technology and Science Tokushima University Tokushima Japan Faculty of Library Information and Media Science University of Tsukuba Tsukuba Japan Dataa Robotics Chengdu China Graduate School of Information Science & Technology The University of Tokyo Tokyo Japan School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Facial Expression Recognition (FER) poses significant challenges due to various imaging conditions, including diverse head poses, lighting conditions, resolutions, and occlusions. Additionally, different personal attributes such as age, gender, and racial background further contribute to the complexity of FER. To accurately extract meaningful expression features amidst these interfering factors to enhance recognition accuracy and the model’s generalization, we propose a Self Decoupling-Reconstruction Network (SDRNet). Specifically, our approach involves two learning processes. In the first phase, the network is trained to decouple facial images with expressions into expression and neutral components. This process involves reconstructing neutral facial images and the original input, ensuring the preservation of meaningful expression components devoid of interference in the decoupling process. In the second learning phase, we employ simple convolutional neural networks (CNNs) to recognize the extracted expression components. Our method has achieved state-of-the-art results across multiple widely used datasets, providing substantial evidence of its effectiveness. Additionally, we demonstrate the robust generalization performance of our approach through cross-database evaluations.

关键词： Image resolution Image recognition Head Face recognition Neural networks Lighting Imaging

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