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arXiv 2024年

作者： Yadav, Rishabh Dev Dantu, Swati Pan, Wei Sun, Sihao Roy, Spandan Baldi, Simone Department of Computer Science The Univerity of Manchester United Kingdom Multi-robot systems group Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Department of Cognitive Robotics Delft Univesrity of Technology Netherlands Robotics Research Center International Institute of Information Technology Hyderabad India School of Mathematics Southeast University Nanjing China

Successful aerial manipulation largely depends on how effectively a controller can tackle the coupling dynamic forces between the aerial vehicle and the manipulator. However, this control problem has remained largely unsolved as the existing control approaches either require precise knowledge of the aerial vehicle/manipulator inertial couplings, or neglect the state-dependent uncertainties especially arising during the interaction phase. This work proposes an adaptive control solution to overcome this long standing control challenge without any a priori knowledge of the coupling dynamic terms. Additionally, in contrast to the existing adaptive control solutions, the proposed control framework is modular, that is, it allows independent tuning of the adaptive gains for the vehicle position sub-dynamics, the vehicle attitude sub-dynamics, and the manipulator sub-dynamics. Stability of the closed loop under the proposed scheme is derived analytically, and real-time experiments validate the effectiveness of the proposed scheme over the state-of-the-art approaches. © 2024, CC BY.

关键词： Adaptive control systems

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Deep Generative Models for Constructing Synthetic Populations in Public Health Applications

Deep Generative Models for Constructing Synthetic Population...

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Nathan Farmer Arash Mahyari Ashok Sirinivasan Department of Computer Science University of West Florida Pensacola FL USA Florida Institute For Human and Machine Cognition (IHMC) Pensacola FL USA Department of Intelligent Systems and Robotics University of West Florida Pensacola FL USA

There is a growing acknowledgment of the significance of social determinants of health in shaping fair public health policies. These determinants, reflected in emerging data streams like mobility and social media data, are increasingly integral to public health models. However, privacy concerns impede broad access to sensitive data, because even non-identifiable data are susceptible to deductive disclosure. To address this, synthetic populations trained on such data emerge as a privacy-conscious solution, offering the added benefit of exploring various "what-if" scenarios. This paper delves into the application of Generative Adversarial Networks (GANs) to craft synthetic populations that tackle privacy issues linked to social determinants of health data. Utilizing the 2019 PUMS dataset for Florida, the study trains the GAN to produce diverse and adaptable synthetic populations. An autoencoder manages the mix of continuous and categorical variables, ensuring the generated data aligns with categorical ranges. The results, assessed by comparing marginal distributions and employing tSNE, underscore the GAN's efficacy in balancing privacy and data utility. The significance of this work lies in identifying the promise of GANs in generating synthetic populations to advance health research.

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Accessibility-Aware Reinforcement Learning for Inclusive Robotic Navigation

Accessibility-Aware Reinforcement Learning for Inclusive Rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Mengsha Hu Yunhuan Wu Yibei Guo Yi Wu Chao Shi Lei Xu Rui Liu Department of Computer Science Kent State University Kent OH USA Robotics Institute Carnegie Mellon University Pittsburgh PA USA Cognitive Robotics and AI Lab (CRAI) College of Aeronautics and Engineering Kent State University Kent OH USA Informatique et Sciences du Numérique (Computer Research Laboratory) CentraleSupélec Paris-Saclay University Gif-sur-Yvette France Department of Systems Science and Industrial Engineering Binghamton University Binghamton NY USA

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Robotic navigation aids humans in essential scenarios, including airport customer boarding, commercial meeting receptions, and disaster site navigation. While individuals benefit from robotic services, these services pose accessibility challenges for people with disabilities. Robots may face limitations in mobility, vision, hearing, and cognitive reasoning, hindering disabled individuals' access to these services. As robotic services become increasingly prevalent, ensuring they are inclusive and accessible is crucial, accommodating human disabilities with adaptations such as slower movements, standing support, and hazard detection. To address this issue, this study introduces a novel Accessibility-Aware Reinforcement Learning model (ARL). ARL extracts disability-related information from human behavioral observations using a contextembedding neural network. It then adjusts robot motions to provide inclusive assistance to individuals with disabilities. To assess the effectiveness of this approach, four assisting services in navigation scenarios ('lead', 'wait', 'assist', and 'protect') and three application scenarios involving navigating elderly individuals in a museum, through traffic, and across a building patio were designed. The results validate ARL's efficacy in enabling robots to make accessibility-aware decisions, thereby enhancing their support for individuals with disabilities.

关键词： Robot motion Navigation Neural networks Reinforcement learning Feature extraction People with disabilities Robot learning Data mining Older adults Robots

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QTER: QoS-Aware Three-Dimensional Efficient and Reliable Routing for LEO Satellite Networks

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IEEE Internet of Things Journal 2025年

作者： Zheng, Jinkai Luan, Tom H. Li, Guanjie Zhang, Yanfeng Yuan, Mingfeng Pan, Jianping Dongguan University of Technology School of Electrical Engineering and Intelligentization China Xi’an Jiaotong University China Xidian University School of Cyber Engineering China Singapore University of Technology and Design Information Systems Technology and Design Pillar Singapore University of Toronto Institute for Aerospace Studies University of Toronto Robotics Institute Canada University of Victoria Department of Computer Science Canada

Low Earth Orbit (LEO) satellite networks offer a promising approach for achieving global coverage and high-speed Internet access. However, the existing communication frameworks within these networks may result in low robustness and reliability. This paper introduces QTER, a novel and lightweight routing framework specifically designed for LEO networks, aimed at addressing these challenges. Our contributions are threefold. First, QTER establishes a cost-effective and reliable routing framework that accommodates a variety of applications with distinct Quality of Service (QoS) requirements. By enabling multi-path routing, the framework minimizes costs while ensuring end-to-end reliability, thus enhancing adaptability to diverse service demands. Second, we leverage the unique structural characteristics of LEO satellite networks by modeling the satellite constellation as a three-dimensional network, wherein higher-shell satellites serve as management satellite nodes (MSNs) to coordinate routing strategies among lower-shell satellites. This architecture significantly improves system efficiency and adaptability. Third, we propose a failure recovery mechanism that allows MSNs to relay packets when lower-orbit satellites are rendered unavailable due to environmental factors, thereby enhancing system robustness. Extensive simulations demonstrate that QTER exhibits resilience to node failures and dynamic network conditions, achieving reductions in average cost and delay by 59.4% and 38.9%, respectively, compared to baselines. © 2014 IEEE.

关键词： Resource allocation

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Direct Torque Control in Series-End Winding PMSM Drives 48

Direct Torque Control in Series-End Winding PMSM Drives

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Dong, Zhiping Zhao, Hang Wen, Hao Liu, Chunhua City University of Hong Kong School of Energy and Environment Hong Kong Hong Kong City University of Hong Kong Shenzhen Research Institute Shenzhen China Robotics and Autonomous Systems Thrust Guangzhou China The Hong Kong University of Science and Technology Department of Electronic & Computer Engineering Hong Kong Hong Kong

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

This paper provides a preliminary study of promoting the direct torque control (DTC) to series-end winding permanent magnet synchronous motor (SW-PMSM) drives. The DTC schemes of the conventional PMSM drives cannot be directly applied to the SW-PMSM drives since they have different drive topologies. With one more leg added to the inverter, the SW-PMSM drive has a different voltage vector distribution, which leads to a different switching table for the hysteresis controllers of the flux and the torque. In addition, the zero-sequence subspace also exists in the SW-PMSM drive, and it might generate the undesired zero-sequence current. Based on the above issues, the voltage vector distribution of the SW-PMSM drive is studied in this paper, and the voltage vectors without zero-sequence components are selected as the candidates to prevent generating zero-sequence current. Next, the switching table for the hysteresis controllers is re-derived according to the candidate voltage vectors, and the DTC for the SW-PMSM drive is obtained. Subsequently, the DTC of the multi-phase SW-PMSM drive is also investigated in the same manner. Finally, the effectiveness of the proposed DTC schemes for the SW-PMSM drives is verified. © 2022 IEEE.

关键词： Torque

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Deep Image Clustering by Spiking Neural Networks

Deep Image Clustering by Spiking Neural Networks

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Applied Imagery Pattern Recognition Workshop (AIPR)

作者： Arash Mahyari Hadi AliAkbarpour Florida Institute For Human and Machine Cognition (IHMC) Pensacola FL USA Department of Intelligent Systems and Robotics University of West Florida Pensacola FL USA Department of Computer Science Saint Louis University St. Louis MO USA

Neuromorphic cameras, or event cameras, are biologically-inspired sensors that detect changes in illumination at a pixel level, different from traditional cameras where each pixel independently and asynchronously outputs when it senses illumination changes. The neuromorphic cameras exhibit high-temporal resolution and dynamic range, useful in various applications. Spiking Neural Networks (SNNs), mimicking biological neurons, are efficient in processing neuromorphic images due to their event-driven, low-power nature. However, training SNNs is challenging because of the non-differentiability of neuron models which require different optimization techniques. Inspired by recent works on unsupervised deep clustering in conventional deep learning, we introduce a novel deep clustering algorithm that employs SNNs to extract and group visual features from neuromorphic images into clusters. It utilizes an SNN feature extractor for neuromorphic images, grouping the extracted features using K-means clustering. This algorithm, which doesn’t require pre-trained SNN weights, alternates between cluster updates and SNN weight adjustments. The extracted feature vectors in SNN are clustered and used as pseudo-labels for supervised training of the SNN extractor. The performance of the proposed algorithm is evaluated on two datasets of MNIST and NMNIST, and its effectiveness is assessed using the Normalized Mutual Information (NMI) metric. The performance of our SNN system with neuromorphic images, while marginally below its counterparts of ANNs with traditional images, demonstrates considerable potential.

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At school with a robot: Italian students’ perception of robotics during an educational program.

At school with a robot: Italian students’ perception of rob...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Francesca Cocchella Giulia Pusceddu Giulia Belgiovine Michela Bogliolo Linda Lastrico Maura Casadio Francesco Rea Alessandra Sciutti CONTACT unit Italian Institute of Technology Genoa Italy Department of Computer Science Bioengineering Robotics and Systems Engineering (DIBRIS) University of Genova Genova Italy RBCS unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

Social robots are expected to become more and more used in the education field. However, in the interaction between children and social robots, how robots are perceived in social contexts is still under investigation. In this exploratory study, we aimed to investigate how children’s expectations and demographical characteristics (N= 53, 9-14 years old) influence their perception of robot NAO during an education training program in schools. MANCOVA analysis conducted over questionnaire data indicates a positive correlation between the acceptance of the robot and the enjoyment of interacting with it. We found evidence that the more students accepted the robot, the more they perceived the group environment positively. Through a Correspondence Analysis, we investigate which are the preferred features of a robot according to the age of participants. The study suggests that a better opinion of robotics is a factor that can improve the learning environment in this specific context. Our exploratory study encourages conducting studies in-the-wild using self-reported measures to understand the implication of Child-Robot Interaction better.

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Development and Analysis of a Versatile Dataset of Speech, Real and Synthesized, of Arabic Learners

Development and Analysis of a Versatile Dataset of Speech, R...

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

作者： Mansour Alsulaiman Mohammed Algabri Hassan Mathkour Mohamed A. Bencherif Ghulam Muhammad Saad Al-Gahtani Mohammed Faisal Mohamed Amine Mekhtiche Taha Alfakih Computer Engineering Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Computer Science Department College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Centre of Smart Robotics Research (CS2R) King Saud University Riyadh Saudi Arabia Arabic Language Institute King Saud University Saudi Arabia Center of AI & Robotics Kuwait College of Science and Technology (KCST) Kuwait

computer-Aided Pronunciation Training (CAPT) systems are gaining popularity recently due to the advancements in deep neural networks (DNN) and machine learning and the availability of databases of speech of language learners. Unfortunately, research in Arabic CAPT systems suffer from the lack of CAPT datasets compared to other languages. In this paper, we present the details of and the ideas used in the development of a versatile dataset of speech, real and synthesized, of Arabic Learners. To develop the dataset, we utilized an existing Arabic speech corpus, King Saud University Speech Database (KSU-SD). KSU-SD's main application was a speaker recognition system, but it was designed to be useful in other applications such as CAPT systems. KSU-SD includes a large number of speakers from diverse nationalities (Saudis, Arabs, and Non-Arabs). KSU-SD contains the recording of about 60 non-native speakers from more than 20 nationalities, hence we selected it to build the non-native Arabic-CAPT corpus. The developed corpus consists of transcribed, segmented, and annotated speech, which makes it suitable for building Arabic CAPT systems. In addition to presenting the details of developing the dataset, we also present an analysis of the text and speech errors of the dataset. The dataset was verified by many CAPT systems.

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AR/VR Digital Twin for Simulation and Data Collection of Robotic Environments

AR/VR Digital Twin for Simulation and Data Collection of Rob...

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

作者： João G. Martins Karle Nutonen Paulo Costa Vladimir Kuts Tauno Otto Armando Sousa Marcelo R. Petry CRIIS - Centre for 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 (FEUP) Porto Portugal ViruTech - Virumaa innovation center of digitalization and green technologies Taltech Virumaa College Kohtla-Jarve Estonia Department of Mechanical and Indusrial Taltech Tallinn Estonia

ISBN: (数字)9798331538606

ISBN: (纸本)9798331538613

Digital twins enable real-time modeling, simulation, and monitoring of complex systems, driving advancements in automation, robotics, and industrial applications. This study presents a large-scale digital twin-testing facility for evaluating mobile robots and pilot robotic systems in a research laboratory environment. The platform integrates high-fidelity physical and environmental models, providing a controlled yet dynamic setting for analyzing robotic behavior. A key feature of the system is its comprehensive data collection framework, capturing critical parameters such as position, orientation, and velocity, which can be leveraged for machine learning, performance optimization, and decision-making. The facility also supports the simulation of discrete operational systems, using predictive modeling to bridge informational gaps when real-time data updates are unavailable. The digital twin was validated through a matrix manufacturing system simulation, with an Augmented Reality (AR) interface on the HoloLens 2 to overlay digital information onto mobile platform controllers, enhancing situational awareness. The main contributions include a digital twin framework for deploying data-driven robotic systems and three key AR/VR integration optimization methods. Demonstrated in a laboratory setting, the system is a versatile tool for research and industrial applications, fostering insights into robotic automation and digital twin scalability while reducing costs and risks associated with real-world testing.

关键词： Service robots Navigation Metaverse Databases Scalability Digital representation Real-time systems Digital twins Mobile robots Robots

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iMTSP: Solving Min-Max Multiple Traveling Salesman Problem with Imperative Learning

arXiv

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arXiv 2024年

作者： Guo, Yifan Ren, Zhongqiang Wang, Chen The Flight Dynamics & Control/Hybrid Systems Lab Purdue University West LafayetteIN47907 United States Robotics Institute Carnegie Mellon University PittsburghPA15213 United States Lab Department of Computer Science and Engineering University at Buffalo NY14260 United States

This paper considers a Min-Max Multiple Traveling Salesman Problem (MTSP), where the goal is to find a set of tours, one for each agent, to collectively visit all the cities while minimizing the length of the longest tour. Though MTSP has been widely studied, obtaining near-optimal solutions for large-scale problems is still challenging due to its NP-hardness. Recent efforts in data-driven methods face challenges of the need for hard-to-obtain supervision and issues with high variance in gradient estimations, leading to slow convergence and highly sub-optimal solutions. We address these issues by reformulating MTSP as a bilevel optimization problem, using the concept of imperative learning (IL). This involves introducing an allocation network that decomposes the MTSP into multiple single-agent traveling salesman problems (TSPs). The longest tour from these TSP solutions is then used to self-supervise the allocation network, resulting in a new self-supervised, bilevel, end-to-end learning framework, which we refer to as imperative MTSP (iMTSP). Additionally, to tackle the high-variance gradient issues during the optimization, we introduce a control variate-based gradient estimation algorithm. Our experiments showed that these innovative designs enable our gradient estimator to converge 20× faster than the advanced reinforcement learning baseline, and find up to 80% shorter tour length compared with Google OR-Tools MTSP solver, especially in large-scale problems (e.g. 1000 cities and 15 agents). © 2024, CC BY.

关键词： Traveling salesman problem

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