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Titre : An introduction to ethics in robotics and AI Type de document : Monographie Auteurs : Christoph Bartneck, Auteur ; Christoph Lütge, Auteur ; Alan Wagner, Auteur ; Sean Welsch, Auteur Editeur : Springer International Publishing Année de publication : 2021 Importance : 117 p Présentation : . Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-030-51110-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] données massives
[Termes IGN] édition en libre accès
[Termes IGN] éthique
[Termes IGN] navigation autonome
[Termes IGN] protection de la vie privée
[Termes IGN] robotique
[Termes IGN] utilisateur militaire
[Termes IGN] véhicule sans piloteRésumé : (éditeur) This open access book introduces the reader to the foundations of AI and ethics. It discusses issues of trust, responsibility, liability, privacy and risk. It focuses on the interaction between people and the AI systems and Robotics they use. Designed to be accessible for a broad audience, reading this book does not require prerequisite technical, legal or philosophical expertise. Throughout, the authors use examples to illustrate the issues at hand and conclude the book with a discussion on the application areas of AI and Robotics, in particular autonomous vehicles, automatic weapon systems and biased algorithms. A list of questions and further readings is also included for students willing to explore the topic further. Note de contenu : 1- What is AI?
2- What is ethics?
3- Trust and fairness in AI systems
4- Responsibility and liability in the case of AI systems
5- Risks in the business of AI
6- Psychological aspects of AI
7- Privacy issues of AI
8- Application areas of AI
9- Autonomous vehicles
10- Military uses of AI
11- Ethics in AI and Robotics: A Strategic ChallengeNuméro de notice : 28570 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie DOI : 10.1007/978-3-030-51110-4 En ligne : https://doi.org/10.1007/978-3-030-51110-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97726
Titre : AI based robot safe learning and control Type de document : Monographie Auteurs : Xuefeng Zhou ·, Auteur ; Shuai Li, Auteur ; et al., Auteur Editeur : Springer Nature Année de publication : 2020 Importance : 127 p. Format : 21 x 30 cm ISBN/ISSN/EAN : 978-981-1555039-- Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] contrainte d'objet
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau neuronal récurrent
[Termes IGN] robotique
[Termes IGN] sécurité
[Termes IGN] système de contrôle
[Termes IGN] vitesse angulaireRésumé : (éditeur) This open access book mainly focuses on the safe control of robot manipulators. The control schemes are mainly developed based on dynamic neural network, which is an important theoretical branch of deep reinforcement learning. In order to enhance the safety performance of robot systems, the control strategies include adaptive tracking control for robots with model uncertainties, compliance control in uncertain environments, obstacle avoidance in dynamic workspace. The idea for this book on solving safe control of robot arms was conceived during the industrial applications and the research discussion in the laboratory. Most of the materials in this book are derived from the authors’ papers published in journals, such as IEEE Transactions on Industrial Electronics, neurocomputing, etc. This book can be used as a reference book for researcher and designer of the robotic systems and AI based controllers, and can also be used as a reference book for senior undergraduate and graduate students in colleges and universities. Note de contenu : 1- Adaptive Jacobian based trajectory tracking for redundant manipulators with model uncertainties in repetitive tasks
2- RNN based trajectory control for manipulators with uncertain kinematic parameters
3- RNN based adaptive compliance control for robots with model uncertainties
4- Deep RNN based obstacle avoidance control for redundant manipulators
5- Optimization-based compliant control for manipulators under dynamic obstacle constraints
6- RNN for motion-force control of redundant manipulators with optimal joint torqueNuméro de notice : 28518 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : sans En ligne : https://directory.doabooks.org/handle/20.500.12854/32049 Format de la ressource électronique : url Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97304
Titre : Collaborative visual-inertial state and scene estimation Type de document : Thèse/HDR Auteurs : Marco Karrer, Auteur ; Margarita Chli, Directeur de thèse Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2020 Importance : 151 p. Format : 21 x 30 cm Note générale : bibliographie
A thesis submitted to attain the degree of Doctor of Sciences of ETH Zurich in Mechanical EngineeringLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] Cartographie et localisation simultanées
[Termes IGN] centrale inertielle
[Termes IGN] compensation par faisceaux
[Termes IGN] estimation de pose
[Termes IGN] image captée par drone
[Termes IGN] reconstruction d'objet
[Termes IGN] robotique
[Termes IGN] système multi-agents
[Termes IGN] vision par ordinateurIndex. décimale : THESE Thèses et HDR Résumé : (auteur) The capability of a robot to create a map of its workspace on the fly, while constantly updating it and continuously estimating its motion in it, constitutes one of the central research problems in mobile robotics and is referred to as Simultaneous Localization And Mapping (SLAM) in the literature. Relying solely on the sensor-suite onboard the robot, SLAM is a core building block in enabling the navigational autonomy necessary to facilitate the general use of mobile robots and has been the subject of booming research interest spanning over three decades. With the largest body of related literature addressing the challenge of single-agent SLAM, it is only very recently, with the relative maturity of this field that approaches tackling collaborative SLAM with multiple agents have started appearing. The potential of collaborative multi-agent SLAM is great; not only promising to boost the efficiency of robotic missions by splitting the task at hand to more agents but also to improve the overall robustness and accuracy by boosting the amount of data that each agent’s estimation process has access to. While SLAM can be performed using a variety of different sensors, this thesis is focused on the fusion of visual and inertial cues, as one of the most common combinations of sensing modalities in robotics today. The information richness captured by cameras, along with the high-frequency and metric information provided by Inertial Measurement Units (IMUs) in combination with the low weight and power consumption offered by a visual-inertial sensor suite render this setup ideal for a wide variety of applications and robotic platforms, in particular to resource-constrained platforms such as Unmanned Aerial Vehicles (UAVs). The majority of the state-of-the-art visual-inertial estimators are designed as odometry algorithms, providing only estimates consistent within a limited time-horizon. This lack in global consistency of estimates, however, poses a major hurdle in an effective fusion of data from multiple agents and the practi- cal definition of a common reference frame, which is imperative before collaborative effort can be coordinated. In this spirit, this thesis investigates the potential of global optimization, based on a central access point (server) as a first approach, demonstrating global consistency using only monocular-inertial data. Fusing data from multiple agents, not only consistency can be maintained, but also the accuracy is shown to improve at times, revealing the great potential of collaborative SLAM. Aiming at improving the computational efficiency, in a second approach a more efficient system architecture is employed, allowing a more suitable distribution of the computational load amongst the agents and the server. Furthermore, the architecture implements a two-way communication enabling a tighter collaboration between the agents as they become capable of re-using information captured by other agents through communication with the server, enabling improvements of their onboard pose tracking online, during the mission. In addition to general collaborative SLAM without specific assumptions on the agents’ relative pose configuration, we investigate the potential of a configuration with two agents, carrying one camera each with overlapping fields of view, essentially forming a virtual stereo camera. With the ability of each robotic agent to move independently, the potential to control the stereo baseline according to the scene depth is very promising, for example at high altitudes where all scene points are far away and, therefore, only provide weak constraints on the metric scale in a standard single-agent system. To this end, an approach to estimate the time-varying stereo transformation formed between two agents is proposed, by fusing the egomotion estimates of the individual agents along with the image measurements extracted from the view-overlap in a tightly coupled fashion. Taking this virtual stereo camera idea a step further, a novel collaboration framework is presented, utilizing the view-overlap along with relative distance measurements across the two agents (e.g. obtained via Ultra-Wide Band (UWB) modules), in order to successfully perform state estimation at high altitudes where state-of-the-art single-agent methods fail. In the interest of low-latency pose estimation, each agent holds its own estimate of the map, while consistency between the agents is achieved using a novel consensus-based sliding window bundle adjustment. Despite that in this work, experiments are shown in a two-agent setup, the proposed distributed bundle adjustment scheme holds great potential for scaling up to larger problems with multiple agents, due to the asynchronicity of the proposed estimation process and the high level of parallelism it permits. The majority of the developed approaches in this thesis rely on sparse feature maps in order to allow for efficient and timely pose estimation, however, this translates to reduced awareness of the spatial structure of a robot’s workspace, which can be insufficient for tasks requiring careful scene interaction and manipulation of objects. Equipping a typical visual-inertial sensor suite with an RGB-D camera, an add-on framework is presented that enables the efficient fusion of naturally noisy depth information into an accurate, local, dense map of the scene, providing sufficient information for an agent to plan contact with a surface. With the focus on collaborative SLAM using visual-inertial data, the approaches and systems presented in this thesis contribute towards achieving collaborative Visual-Inertial SLAM (VI-SLAM) deployable in challenging real-world scenarios, where the participating agents’ experiences get fused and processed at a central access point. On the other side, it is shown that taking advantage of specific configurations can push the collaboration amongst the agents towards achieving greater general robustness and accuracy of scene and egomotion estimates in scenarios, where state-of-the-art single-agent systems are otherwise unsuccessful, paving the way towards intelligent robot collaboration. Note de contenu : Introduction
1- Real-time dense surface reconstruction for aerial manipulation
2- Towards globally consistent visual-inertial collaborative SLAM
3- CVI-SLAM – collaborative visual-inertial SLAM
4- Collaborative 6DoF relative pose estimation for two UAVs with overlapping fields of view
5- Distributed variable-baseline stereo SLAM from two UAVsNuméro de notice : 28318 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Thèse étrangère Note de thèse : PhD Thesis : Mechanical Engineering : ETH Zurich : 2020 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/465334 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98251 Nonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou · (2020)
Titre : Nonparametric Bayesian learning for collaborative robot multimodal introspection Type de document : Monographie Auteurs : Xuefeng Zhou ·, Auteur ; Hongmin Wu, Auteur ; Juan Rojas, Auteur ; et al., Auteur Editeur : Springer Nature Année de publication : 2020 Importance : 137 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-981-1562631-- Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] classification bayesienne
[Termes IGN] inférence
[Termes IGN] interface homme-machine
[Termes IGN] modèle de Markov caché
[Termes IGN] modèle mathématique
[Termes IGN] problème de Dirichlet
[Termes IGN] robotiqueRésumé : (éditeur) This open access book focuses on robot introspection, which has a direct impact on physical human–robot interaction and long-term autonomy, and which can benefit from autonomous anomaly monitoring and diagnosis, as well as anomaly recovery strategies. In robotics, the ability to reason, solve their own anomalies and proactively enrich owned knowledge is a direct way to improve autonomous behaviors. To this end, the authors start by considering the underlying pattern of multimodal observation during robot manipulation, which can effectively be modeled as a parametric hidden Markov model (HMM). They then adopt a nonparametric Bayesian approach in defining a prior using the hierarchical Dirichlet process (HDP) on the standard HMM parameters, known as the Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM). The HDP-HMM can examine an HMM with an unbounded number of possible states and allows flexibility in the complexity of the learned model and the development of reliable and scalable variational inference methods. This book is a valuable reference resource for researchers and designers in the field of robot learning and multimodal perception, as well as for senior undergraduate and graduate university students. Note de contenu : 1- Introduction to robot introspection
2- Nonparametric Bayesian modeling of multimodal time series
3- Incremental learning robot task representation and identification
4- Nonparametric Bayesian method for robot anomaly monitoring
5- Nonparametric Bayesian method for robot anomaly diagnose
6- Learning policy for robot anomaly recovery based on robot introspectionNuméro de notice : 25965 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE/MATHEMATIQUE Nature : Monographie DOI : 10.1007%2F978-981-15-6263-1 En ligne : https://link.springer.com/book/10.1007%2F978-981-15-6263-1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96557 Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors / Paul Chauchat (2020)
Titre : Smoothing algorithms for navigation, localisation and mapping based on high-grade inertial sensors Type de document : Thèse/HDR Auteurs : Paul Chauchat, Auteur ; Silvère Bonnabel, Directeur de thèse Editeur : Paris : Université Paris Sciences et Lettres Année de publication : 2020 Importance : 135 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse de Doctorat de l'Université Paris Sciences et Lettres, Informatique temps réel, robotique, automatiqueLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] algorithme de filtrage
[Termes IGN] algorithme de lissage
[Termes IGN] Cartographie et localisation simultanées
[Termes IGN] filtre de Kalman
[Termes IGN] itération
[Termes IGN] navigation inertielle
[Termes IGN] robotiqueRésumé : (auteur) Mobile systems need to locate themselves ever more accurately, and in ever more complex situations. This is in particular true for autonomous systems, for which controlling the position error is a critical safety issue. To this end, they are endowed with various sensors, the data of which are fused to obtain an estimate of the vehicle’s location, either globally (with the GPS for instance), or locally, with respect to its surroundings (with cameras for instance). This thesis investigates algorithms for localisation by sensor fusion, namely filtering and especially smoothing, when the mobile is equipped with high-grade inertial sensors. The first part deals with the nonlinear consequences of the use of high-grade inertial sensors, and demonstrates how the nonlinear structure of both filtering and smoothing algorithms may be improved by leveraging the invariant filtering framework. The second part deals with the problems incurred by the linear solvers that are used at each step of nonlinear smoothing algorithms as a result of having highly precise sensors. It introduces a novel least-squares linear solver that solves the issues. Note de contenu : Introduction
I- From Invariant filtering to invariant smoothing
II- Navigation with highly precise sensors
ConclusionNuméro de notice : 28576 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Thèse française Note de thèse : thèse de Doctorat : Informatique temps réel, robotique, automatique : Paris Sciences et Lettres Organisme de stage : Centre de robotique (Paris) En ligne : https://pastel.archives-ouvertes.fr/tel-02887295/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97843 Machine learning and pose estimation for autonomous robot grasping with collaborative robots / Victor Talbot (2018)
PermalinkPermalinkPermalinkPermalinkPermalinkPanorama de l'intelligence artificielle, ses bases méthodologiques, ses développements, 3. L'intelligence artificielle : frontières et applications / Pierre Marquis (2014)
PermalinkPermalinkRapport annuel 2006 INRIA, Institut National de Recherche en Informatique et en Automatique / I. Bellin (2007)
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