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Artificial intelligence for long-term robot autonomy: A survey

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arXiv 2018年

作者： Kunze, Lars Hawes, Nick Duckett, Tom Hanheide, Marc Krajnik, Tomas Oxford Robotics Institute Dept. of Engineering Science University of Oxford School of Computer Science University of Lincoln Dept. of Computer Science Faculty of Electrical Engineering Czech Technical University in Prague

Autonomous systems will play an essential role in many applications across diverse domains including space, marine, air, field, road, and service robotics. They will assist us in our daily routines and perform dangerous, dirty and dull tasks. However, enabling robotic systems to perform autonomously in complex, real-world scenarios over extended time periods (i.e. weeks, months, or years) poses many challenges. Some of these have been investigated by sub-disciplines of Artificial Intelligence (AI) including navigation and mapping, perception, knowledge representation and reasoning, planning, interaction, and learning. The different sub-disciplines have developed techniques that, when re-integrated within an autonomous system, can enable robots to operate effectively in complex, long-term scenarios. In this paper, we survey and discuss AI techniques as 'enablers' for long-term robot autonomy, current progress in integrating these techniques within long-running robotic systems, and the future challenges and opportunities for AI in long-term autonomy. Copyright © 2018, The Authors. All rights reserved.

关键词： Surveys

Non-invasive sensor methods used in monitoring newborn babies after birth, a clinical perspective

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Maternal health, neonatology and perinatology 2022年第1期8卷 9页

作者： Oana Anton Henry Dore Elizabeth Rendon-Morales Rodrigo Aviles-Espinosa Paul Seddon David Wertheim Ramon Fernandez Heike Rabe East Sussex NHS Healthcare Trust Eastbourne UK. oana.anton@***. Brighton and Sussex Medical School Academic Department of Paediatrics Royal Alexandra Children's Hospital Brighton UK. oana.anton@***. Robotics and Mechatronics Systems Research Group School of Engineering and Informatics University of Sussex Brighton UK. Brighton and Sussex Medical School Academic Department of Paediatrics Royal Alexandra Children's Hospital Brighton UK. School of Computer Science and Mathematics Faculty of Science Engineering and Computing Kingston University Kingston upon Thames UK. Department of Neonatology University Hospitals Sussex Brighton UK.

BACKGROUND:Reducing the global new-born mortality is a paramount challenge for humanity. There are approximately 786,323 live births in the UK each year according to the office for National Statistics; around 10% of these newborn infants require assistance during this transition after birth. Each year around, globally around 2.5 million newborns die within their first month. The main causes are complications due to prematurity and during delivery. To act in a timely manner and prevent further damage, health professionals should rely on accurate monitoring of the main vital signs heart rate and respiratory rate. AIMS:To present a clinical perspective on innovative, non-invasive methods to monitor heart rate and respiratory rate in babies highlighting their advantages and limitations in comparison with well-established methods. METHODS:Using the data collected in our recently published systematic review we highlight the barriers and facilitators for the novel sensor devices in obtaining reliable heart rate measurements. Details about difficulties related to the application of sensors and interfaces, time to display, and user feedback are explored. We also provide a unique overview of using a non-invasive respiratory rate monitoring method by extracting RR from the pulse oximetry trace of newborn babies. RESULTS:Novel sensors to monitor heart rate offer the advantages of minimally obtrusive technologies but have limitations due to movement artefact, bad sensor coupling, intermittent measurement, and poor-quality recordings compared to gold standard well established methods. Respiratory rate can be derived accurately from pleth recordings in infants. CONCLUSION:Some limitations have been identified in current methods to monitor heart rate and respiratory rate in newborn babies. Novel minimally invasive sensors have advantages that may help clinical practice. Further research studies are needed to assess whether they are sufficiently accurate, practical, and reliable to

关键词： Heart rate monitoring Newborn Novel sensors

A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

GRU controller-based UPQC compensator design for improving power quality in grid-integrated non-linear load system

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Scientific Reports 2025年第1期15卷 1-28页

作者： B. Srikanth Goud Harish Pulluri Ch. Naga Sai Kalyan Gundala Srinivasa Rao Bhabasis Mohapatra Narsimha Reddy Kuppireddy Ch. Rami Reddy Mohammad Shorfuzzaman Basem Abu Zneid Mukesh Pushkarna Department of Electrical and Electronics Engineering School of Engineering Anurag University Hyderabad 500088 India Department of Electrical and Electronics Engineering Vasireddy Venkatadri International Technological University (VVITU) Guntur 522 508 India Department of Electrical and Electronics Engineering CMR College of Engineering & Technology Hyderabad India Department of Electrical Engineering ITER Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar 751030 Odisha India Department of Electronics and Communication Engineering Vardhaman College of Engineering Hyderabad Telangana India Department of Electrical and Electronics Engineering Joginpally B R Engineering College Hyderabad 500075 India Department of Computer Science College of Computers and Information Technology Taif University Taif 21944 Saudi Arabia Robotics and Artificial Intelligence Engineering Department Faculty of Engineering Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman Jordan Department of Electrical Engineering GLA University Mathura Mathura Uttar Pradesh India

Power quality has prominently gained its importance in power systems with the advancement of technology. Voltage sags/swells, harmonics, and other disturbances are the major issues causing most of the technical and financial damages which are reducing the quality of energy supplied. To overcome these challenges design of a unified power quality conditioner (UPQC) plays a vital role in mitigating the PQ issues. In this paper, an advanced neural network base approach is developed to manage UPQC to maintain a constant power supply for the end users. DC link of UPQC is taken from PV, fuel, and battery at a specific range. The compensator DC link is linked in a smart grid with nonlinear load. On the other hand, the switching pulse was performed with the use of the Gated Recurrent Unit (GRU) controller technique. Various fault conditions are created to make a dataset that is utilized to design the GRU that analyses the load voltage and current at each second to generate a pulse for UPQC. The performance is evaluated utilizing an advanced controller under various conditions, including swell, sag, harmonics, and combined three-phase faults. The low harmonic content of voltage is 0.04%, 0.25%, and 0.98%. The suggested controller is accessible with 99.5% specificity, 99% sensitivity, and 98% accuracy. The proposed controller provides low harmonic content while operating in a highly secure, dependable, and efficient manner.

关键词： Battery Fuel GRU Harmonics reduction PV Power quality UPQC

Composition loss for counting, density map estimation and localization in dense crowds

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arXiv 2018年

作者： Idrees, Haroon Tayyab, Muhmmad Athrey, Kishan Zhang, Dong Al-Maadeed, Somaya Rajpoot, Nasir Shah, Mubarak Robotics Institute Carnegie Mellon University NVIDIA Inc Computer Science Department Faculty of Engineering Qatar University Department of Computer Science University of Warwick United Kingdom Center for Research in Computer Vision University of Central Florida

With multiple crowd gatherings of millions of people every year in events ranging from pilgrimages to protests, concerts to marathons, and festivals to funerals;visual crowd analysis is emerging as a new frontier in computer vision. In particular, counting in highly dense crowds is a challenging problem with far-reaching applicability in crowd safety and management, as well as gauging political significance of protests and demonstrations. In this paper, we propose a novel approach that simultaneously solves the problems of counting, density map estimation and localization of people in a given dense crowd image. Our formulation is based on an important observation that the three problems are inherently related to each other making the loss function for optimizing a deep CNN decomposable. Since localization requires high-quality images and annotations, we introduce UCF-QNRF dataset that overcomes the shortcomings of previous datasets, and contains 1.25 million humans manually marked with dot annotations. Finally, we present evaluation measures and comparison with recent deep CNN networks, including those developed specifically for crowd counting. Our approach significantly outperforms state-of-the-art on the new dataset, which is the most challenging dataset with the largest number of crowd annotations in the most diverse set of scenes. Copyright © 2018, The Authors. All rights reserved.

关键词： Machine learning

Parameter estimation of nonlinearly parameterized regressions without overparameterization nor persistent excitation: Application to system identification and adaptive control

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arXiv 2019年

作者： Ortega, Romeo Gromov, Vladislav Nuño, Emmanuel Pyrkin, Anton Romero, Jose Guadalupe Laboratoire des Signaux et Systèmes CNRS-SUPELEC Gif-sur-Yvette France Faculty of Control Systems and Robotics ITMO University Saint Petersburg Russia Department of Computer Science CUCEI University of Guadalajara Guadalajara Mexico Departamento Académico de Sistemas Digitales ITAM Ciudad de México Mexico

In this paper we propose a solution to the problem of parameter estimation of nonlinearly parameterized regressions-continuous or discrete time-and apply it for system identification and adaptive control. We restrict our attention to parameterizations that can be factorized as the product of two functions, a measurable one and a nonlinear function of the parameters to be estimated. Although in this case it is possible to define an extended vector of unknown parameters to get a linear regression, it is well-known that overparameterization suffers from some severe shortcomings. Another feature of the proposed estimator is that parameter convergence is ensured without a persistency of excitation assumption. It is assumed that, after a coordinate change, some of the elements of the transformed function satisfy a monotonicity condition. The proposed estimators are applied to design identifiers and adaptive controllers for nonlinearly parameterized systems. In continuous-time we consider a general class of nonlinear systems and those described by Euler-Lagrange models, while in discrete-time we apply the method to the challenging problems of direct and indirect adaptive pole-placement. The effectiveness of our approach is illustrated with several classical examples, which are traditionally tackled using overparameterization and assuming persistency of excitation. Copyright © 2019, The Authors. All rights reserved.

关键词： Parameterization

Spoken dialogue-based remote control of educational mobile robot with mecanum wheels 17

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Spoken dialogue-based remote control of educational mobile r...

17th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2019

作者： Chivarov, Nayden Chikurtev, Denis Pleva, Matus Ondas, Stanislav Juhar, Jozef Liao, Yuan-Fu Institute of Information and Communication Technologies Bulgarian Academy of Sciences BAS Acad. G. Bonchev St. Sofia1113 Bulgaria Robotics Lab European Polytechnical University 23 Sv. Sv. Kiril i Metodiy Str. Pernik2300 Bulgaria Department of Electronics and Multimedia Communications Faculty of Electrical Engineering and Informatics Technical University of Kosice Letna 9 Kosice04200 Slovakia Department of Electronic Engineering College of Electrical Engineering &computer Science National Taipei University of Technology Zhongxiao E. Rd. 406 Taipei10608 Taiwan

ISBN: (纸本)9781728149677

This paper describes a new platform of educational mobile robot with Mecanum wheels and voice enabled dialogue-based human robot interaction model was established. Moreover, new trends in using deep neural networks for cloud-and edge-based speech recognition was described in detail. Finally, the progress of both the cloudand edge-based speech recognition was reported. Especially, promising usability test result on resisting room reverberation and background noise had been achieved by the proposed convolutional neural network (CNN)-based wake-up word (WUW) spotting system. © 2019 IEEE.

关键词： Mobile robots

On the precision of neural computation with interaural time differences in the medial superior olive

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arXiv 2020年

作者： Marsalek, Petr Sanda, Pavel Bures, Zbynek Institute of Pathological Physiology First Medical Faculty Charles University in Prague U Nemocnice 5/478 Praha 2128 53 Czech Republic Department of Radioelectronics Faculty of Electrical Engineering Czech Technical University in Prague Technická 2/1902 Praha 6166 27 Czech Republic Institute of Computer Science Czech Academy of Sciences Pod Vodárenskou věží 2/271 Praha 8182 07 Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Jugoslávských partyzánů 4/1903 Praha 6166 36 Czech Republic College of Polytechnics Tolstého 16/1556 Jihlava586 01 Czech Republic

Incoming sound is in cochlea and auditory nerve encoded into spike trains. At the third neuron of the auditory pathway, spike trains of the left and right sides are processed in brainstem nuclei to yield sound localization information. Two different localization encoding mechanisms are employed in two "centers" for low and high sound frequencies in the brainstem. The centers are superior olivary nuclei, medial and lateral. This paper contains analytical estimates of parameters needed in description of auditory coding in sound localization neural circuit. Our model spike trains are based on electro-physiological recordings. We arrive to best estimates for neuronal signaling with the use of just noticeable difference of the ideal observer. We describe spike timing jitter and its role in the spike train processing. We study frequency dependence of spike trains on the sound frequency. All parameters are accompanied with detailed estimates of their values and variability. Intervals bounding all the parameter from lower and higher values are discussed. Copyright © 2020, The Authors. All rights reserved.

关键词： Neurons

Correction: Gut microbiome in endometriosis: a cohort study on 1000 individuals

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BMC medicine 2024年第1期22卷 448页

作者： Inmaculada Pérez-Prieto Eva Vargas Eduardo Salas-Espejo Kreete Lüll Analuce Canha-Gouveia Laura Antequera Pérez Juan Fontes Andres Salumets Reidar Andreson Oliver Aasmets Katrine Whiteson Elin Org Signe Altmäe Department of Biochemistry and Molecular Biology I Faculty of Sciences University of Granada Granada Spain. inmapprieto@ugr.es. Instituto de Investigación Biosanitaria Ibs.GRANADA Granada Spain. inmapprieto@ugr.es. Department of Biochemistry and Molecular Biology I Faculty of Sciences University of Granada Granada Spain. Instituto de Investigación Biosanitaria Ibs.GRANADA Granada Spain. Systems Biology Unit Department of Experimental Biology Faculty of Experimental Sciences University of Jaen Jaen Spain. Institute of Genomics Estonian Genome Centre University of Tartu Tartu Estonia. Department of Physiology Faculty of Veterinary University of Murcia Campus Mare Nostrum Murcia Spain. Department of Computer Engineering Automation and Robotics University of Granada Granada Spain. U. Reproducción UGC Laboratorio Clínico y UGC Obstetricia y Ginecología. HU Virgen de Las Nieves Granada Spain. Competence Centre On Health Technologies Tartu Estonia. Division of Obstetrics and Gynaecology Department of Clinical Science Intervention and Technology Karolinska Institutet Huddinge Stockholm Sweden. Department of Gynaecology and Reproductive Medicine Karolinska University Hospital Huddinge Stockholm Sweden. Department of Obstetrics and Gynaecology Institute of Clinical Medicine University of Tartu Tartu Estonia. Institute of Molecular and Cell Biology University of Tartu Tartu Estonia. School of Biological Sciences University of California Irvine CA USA.