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AI based robot safe learning and control / Xuefeng Zhou (2020)  

Public 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] optimisation (mathématiques) [Termes IGN] réseau neuronal récurrent [Termes IGN] robotique [Termes IGN] sécurité [Termes IGN] système de contrôle [Termes IGN] vitesse angulaire Résumé : (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 torque Numéro 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

Nonparametric Bayesian learning for collaborative robot multimodal introspection / Xuefeng Zhou (2020)  

Public 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] robotique Résumé : (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 introspection Numéro 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