Letteratura scientifica selezionata sul tema "Continuous physical human-Robot interaction"
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Articoli di riviste sul tema "Continuous physical human-Robot interaction"
Feth, Daniela. "Haptic Human–Robot Collaboration: Comparison of Robot Partner Implementations in Terms of Human-Likeness and Task Performance". Presence: Teleoperators and Virtual Environments 20, n. 2 (1 aprile 2011): 173–89. http://dx.doi.org/10.1162/pres_a_00042.
Testo completoUmbrico, Alessandro, Andrea Orlandini, Amedeo Cesta, Marco Faroni, Manuel Beschi, Nicola Pedrocchi, Andrea Scala et al. "Design of Advanced Human–Robot Collaborative Cells for Personalized Human–Robot Collaborations". Applied Sciences 12, n. 14 (6 luglio 2022): 6839. http://dx.doi.org/10.3390/app12146839.
Testo completoBallesteros, Joaquin, Francisco Pastor, Jesús M. Gómez-de-Gabriel, Juan M. Gandarias, Alfonso J. García-Cerezo e Cristina Urdiales. "Proprioceptive Estimation of Forces Using Underactuated Fingers for Robot-Initiated pHRI". Sensors 20, n. 10 (18 maggio 2020): 2863. http://dx.doi.org/10.3390/s20102863.
Testo completoFan, Jinlong, Yang Yue, Yu Wang, Bei Wan, Xudong Li e Gengpai Hua. "A Continuous Gesture Segmentation and Recognition Method for Human-Robot Interaction". Journal of Physics: Conference Series 2213, n. 1 (1 marzo 2022): 012039. http://dx.doi.org/10.1088/1742-6596/2213/1/012039.
Testo completoAyoubi, Younsse, Med Laribi, Said Zeghloul e Marc Arsicault. "V2SOM: A Novel Safety Mechanism Dedicated to a Cobot’s Rotary Joints". Robotics 8, n. 1 (6 marzo 2019): 18. http://dx.doi.org/10.3390/robotics8010018.
Testo completoTry, Pieter, Steffen Schöllmann, Lukas Wöhle e Marion Gebhard. "Visual Sensor Fusion Based Autonomous Robotic System for Assistive Drinking". Sensors 21, n. 16 (11 agosto 2021): 5419. http://dx.doi.org/10.3390/s21165419.
Testo completoYamazaki, Yoichi, Masayuki Ishii, Takahiro Ito e Takuya Hashimoto. "Frailty Care Robot for Elderly and its Application for Physical and Psychological Support". Journal of Advanced Computational Intelligence and Intelligent Informatics 25, n. 6 (20 novembre 2021): 944–52. http://dx.doi.org/10.20965/jaciii.2021.p0944.
Testo completoLeib, Raz, Marta Russo, Andrea d’Avella e Ilana Nisky. "A bang-bang control model predicts the triphasic muscles activity during hand reaching". Journal of Neurophysiology 124, n. 1 (1 luglio 2020): 295–304. http://dx.doi.org/10.1152/jn.00132.2020.
Testo completoSanthanaraj, Karthik Kumar, Ramya M.M. e Dinakaran D. "A survey of assistive robots and systems for elderly care". Journal of Enabling Technologies 15, n. 1 (25 marzo 2021): 66–72. http://dx.doi.org/10.1108/jet-10-2020-0043.
Testo completoDella Santina, Cosimo, Robert K. Katzschmann, Antonio Bicchi e Daniela Rus. "Model-based dynamic feedback control of a planar soft robot: trajectory tracking and interaction with the environment". International Journal of Robotics Research 39, n. 4 (11 gennaio 2020): 490–513. http://dx.doi.org/10.1177/0278364919897292.
Testo completoTesi sul tema "Continuous physical human-Robot interaction"
Tout, Bilal. "Identification of human-robot systems in physical interaction : application to muscle activity detection". Electronic Thesis or Diss., Valenciennes, Université Polytechnique Hauts-de-France, 2024. https://ged.uphf.fr/nuxeo/site/esupversions/36d9eab3-c170-4e40-abb6-e6b4e27aeee2.
Testo completoOver the last years, physical human-robot interaction has become an important research subject, for example for rehabilitation applications. This PhD aims at improving these interactions, as part of model-based controllers development, using parametric identification approaches to identify models of the systems in interaction. The goal is to develop identification methods taking into account the variability and complexity of the human body, and only using the sensor of the robotic system to avoid adding external sensors. The different approaches presented in this thesis are tested experimentally on a one degree of freedom (1-DOF) system allowing the interaction with a person’s hand.After a 1st chapter presenting the state-of-the-art, the 2nd chapter tackles the identification methods developed in robotics as well as the issue of data filtering, analyzed both in simulation and experimentally. The question of the low-pass filter tuning is addressed, and in particular the choice of the cut-off frequency which remains delicate for a nonlinear system. To overcome these difficulties, a filtering technique using an extended Kalman filter (EKF) is developed from the robot dynamic model. The proposed EKF formulation allows a filter tuning depending on the known properties of the sensor and on the confidence on the initial parameters estimations. This method is compared in simulation and experimentally to different existing methods by analyzing its sensitivity to initialization and filter tuning. Results show that the proposed method is promising if the EKF is correctly tuned.The 3rd chapter concerns the continuous identification of the parameters of the model of a passive system interacting with a robotic system, by combining payload identification methods with online identification algorithms, without external sensors. These methods are validated in simulation and experimentally with the 1-DOF system whose handle is attached to elastic rubber bands to emulate a passive human joint. The analysis of the effects of the online methods tuning highlights a necessary trade-off between the convergence speed and the accuracy of the parameters estimates. Finally, the comparison of the payload identification methods shows that methods identifying separately the robotic system and the passive human parameters give better accuracy and a lower computation complexity.The 4th chapter deals with the identification during the human-robot interaction. A quadratic stiffness model is proposed to better fit the passive human joint behavior than a linear stiffness model. Then, this model is used with an iterative identification method based on outlier rejection technique, to detect the human user muscle activity without external sensors. This method is compared experimentally to a non-iterative method that uses electromyography (EMG), by adapting the 1-DOF system to interact with the wrist and to allow the detection of the flexor and extensor muscle activity of two human users. The proposed iterative identification method not using EMG signals achieves results close to those obtained with the non-iterative method using EMG signals when a model that correctly represents the passive human joint behavior is selected. The muscle activity detection results obtained with both methods show a satisfactory level of similarity compared to those obtained directly from EMG signals
Vogt, David. "Learning Continuous Human-Robot Interactions from Human-Human Demonstrations". Doctoral thesis, Technische Universitaet Bergakademie Freiberg Universitaetsbibliothek "Georgius Agricola", 2018. http://nbn-resolving.de/urn:nbn:de:bsz:105-qucosa-233262.
Testo completoAhmed, Muhammad Rehan. "Compliance Control of Robot Manipulator for Safe Physical Human Robot Interaction". Doctoral thesis, Örebro universitet, Akademin för naturvetenskap och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-13986.
Testo completoGopinathan, Sugeeth [Verfasser]. "Personalization and Adaptation in Physical Human-Robot Interaction / Sugeeth Gopinathan". Bielefeld : Universitätsbibliothek Bielefeld, 2019. http://d-nb.info/1181946336/34.
Testo completoShe, Yu. "Compliant robotic arms for inherently safe physical human-robot interaction". The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1541335591178684.
Testo completoTownsend, Eric Christopher. "Estimating Short-Term Human Intent for Physical Human-Robot Co-Manipulation". BYU ScholarsArchive, 2017. https://scholarsarchive.byu.edu/etd/6358.
Testo completoGuled, Pavan. "Analysis of the physical interaction between Human and Robot via OpenSim software". Master's thesis, Alma Mater Studiorum - Università di Bologna, 2018.
Cerca il testo completoBriquet-Kerestedjian, Nolwenn. "Impact detection and classification for safe physical Human-Robot Interaction under uncertainties". Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLC038/document.
Testo completoThe present thesis aims to develop an efficient strategy for impact detection and classification in the presence of modeling uncertainties of the robot and its environment and using a minimum number of sensors, in particular in the absence of force/torque sensor.The first part of the thesis deals with the detection of an impact that can occur at any location along the robot arm and at any moment during the robot trajectory. Impact detection methods are commonly based on a dynamic model of the system, making them subject to the trade-off between sensitivity of detection and robustness to modeling uncertainties. In this respect, a quantitative methodology has first been developed to make explicit the contribution of the errors induced by model uncertainties. This methodology has been applied to various detection strategies, based either on a direct estimate of the external torque or using disturbance observers, in the perfectly rigid case or in the elastic-joint case. A comparison of the type and structure of the errors involved and their consequences on the impact detection has been deduced. In a second step, novel impact detection strategies have been designed: the dynamic effects of the impacts are isolated by determining the maximal error range due to modeling uncertainties using a stochastic approach.Once the impact has been detected and in order to trigger the most appropriate post-impact robot reaction, the second part of the thesis focuses on the classification step. In particular, the distinction between an intentional contact (the human operator intentionally interacts with the robot, for example to reconfigure the task) and an undesired contact (a human subject accidentally runs into the robot), as well as the localization of the contact on the robot, is investigated using supervised learning techniques and more specifically feedforward neural networks. The challenge of generalizing to several human subjects and robot trajectories has been investigated
Gomes, Junior Waldez Azevedo. "Improving Ergonomics Through Physical Human-Robot Collaboration". Electronic Thesis or Diss., Université de Lorraine, 2021. http://www.theses.fr/2021LORR0208.
Testo completoThis thesis aims to provide tools for improving ergonomics at work environments. Some work activities in industry are commonly executed by workers in a non-ergonomic fashion, which may lead to musculoskeletal disorders in the short or in the long term.Work-related Musculoskeletal Disorders (WMSDs) are a major health issue worldwide, that also represents important costs both for society and companies. WMSDs are known to be caused by multiple factors, such as repetitive motion, excessive force, and awkward, non-ergonomic body postures. Not surprisingly, work environments with such factors may present an incidence of WMSDs of up to 3 or 4 times higher than in the overall population.Here, our approach is to evaluate the human motion with respect to ergonomics indexes, optimize the motion, and intervene on the task based on the optimized motion.To evaluate the body posture ergonomics, we developed a Digital Human Model (DHM) simulation capable of replaying whole-body motions.In simulation, the initial movement can be iteratively improved, until an optimal ergonomic whole-body motion is obtained.We make the case that a robot in physical interaction with a human could drive the human towards more ergonomic whole-body motions, possibly to an ergonomically optimal motion. To design a robot controller that influences the body posture, we first investigate the human motor behavior in a human-human co-manipulation study. In this human dyad study, we observed motor behavior patterns that were used to design a collaboration controller for physical human-robot interaction (pHRI). In a new study, the same co-manipulation task was then executed by humans collaborating with a Franka Emika Panda robot
Agravante, Don Joven. "Human-humanoid collaborative object transportation". Thesis, Montpellier, 2015. http://www.theses.fr/2015MONTS224/document.
Testo completoHumanoid robots provide many advantages when working together with humans to perform various tasks. Since humans in general have alot of experience in physically collaborating with each other, a humanoid with a similar range of motion and sensing has the potential to do the same.This thesis is focused on enabling humanoids that can do such tasks together withhumans: collaborative humanoids. In particular, we use the example where a humanoid and a human collaboratively carry and transport objectstogether. However, there is much to be done in order to achieve this. Here, we first focus on utilizing vision and haptic information together forenabling better collaboration. More specifically the use of vision-based control together with admittance control is tested as a framework forenabling the humanoid to better collaborate by having its own notion of the task. Next, we detail how walking pattern generators can be designedtaking into account physical collaboration. For this, we create leader and follower type walking pattern generators. Finally,the task of collaboratively carrying an object together with a human is broken down and implemented within an optimization-based whole-bodycontrol framework
Libri sul tema "Continuous physical human-Robot interaction"
Prats, Mario. Robot Physical Interaction through the combination of Vision, Tactile and Force Feedback: Applications to Assistive Robotics. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.
Cerca il testo completoRobot Physical Interaction Through The Combination Of Vision Tactile And Force Feedback Applications To Assistive Robotics. Springer, 2012.
Cerca il testo completoPrats, Mario, Ángel P. del Pobil e Pedro J. Sanz. Robot Physical Interaction through the combination of Vision, Tactile and Force Feedback: Applications to Assistive Robotics. Springer, 2014.
Cerca il testo completoReid-Merritt, Patricia, a cura di. Race in America. Praeger, 2017. http://dx.doi.org/10.5040/9798216983729.
Testo completoReid-Merritt, Patricia, a cura di. Race in America. Praeger, 2017. http://dx.doi.org/10.5040/9798216983712.
Testo completoKarpyn, Allison. Behavioral Design as an Emerging Theory for Dietary Behavior Change. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190626686.003.0003.
Testo completoHolden, Vanessa M. Surviving Southampton. University of Illinois Press, 2021. http://dx.doi.org/10.5622/illinois/9780252043864.001.0001.
Testo completoWikle, Christopher K. Spatial Statistics. Oxford University Press, 2018. http://dx.doi.org/10.1093/acrefore/9780190228620.013.710.
Testo completoMetta, Giorgio. Humans and humanoids. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0047.
Testo completoVerschure, Paul F. M. J. A chronology of Distributed Adaptive Control. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0036.
Testo completoCapitoli di libri sul tema "Continuous physical human-Robot interaction"
Na, Risu, e Haocheng Dai. "A Framework for Cypher-Physical Human-robot Collaborative Immersive MR Interaction – Beaux Arts Ball 4.0". In Proceedings of the 2021 DigitalFUTURES, 263–72. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-5983-6_25.
Testo completoYoung, Zoe, Michael P. Craven, Maddie Groom e John Crowe. "Snappy App: A Mobile Continuous Performance Test with Physical Activity Measurement for Assessing Attention Deficit Hyperactivity Disorder". In Human-Computer Interaction. Applications and Services, 363–73. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-07227-2_35.
Testo completoHaddadin, Sami. "Physical Human-Robot Interaction". In Encyclopedia of Robotics, 1–8. Berlin, Heidelberg: Springer Berlin Heidelberg, 2020. http://dx.doi.org/10.1007/978-3-642-41610-1_26-1.
Testo completoNatale, Ciro. "Physical Human-Robot Interaction". In Encyclopedia of Systems and Control, 1–9. London: Springer London, 2019. http://dx.doi.org/10.1007/978-1-4471-5102-9_100033-1.
Testo completoNatale, Ciro. "Physical Human-Robot Interaction". In Encyclopedia of Systems and Control, 1716–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-44184-5_100033.
Testo completoHaddadin, Sami, e Elizabeth Croft. "Physical Human–Robot Interaction". In Springer Handbook of Robotics, 1835–74. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32552-1_69.
Testo completoBicchi, Antonio, Michael A. Peshkin e J. Edward Colgate. "Safety for Physical Human–Robot Interaction". In Springer Handbook of Robotics, 1335–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-30301-5_58.
Testo completoHaddadin, Sami, e Elizabeth Croft. "Erratum to: Physical Human–Robot Interaction". In Springer Handbook of Robotics, E1. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-32552-1_81.
Testo completoReed, Kyle B. "Cooperative Physical Human-Human and Human-Robot Interaction". In Springer Series on Touch and Haptic Systems, 105–27. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2754-3_7.
Testo completoPrassler, Prof Dr Erwin, Dr Andreas Stopp, Martin Hägele, Ioannis Iossifidis, Dr Gisbert Lawitzky, Dr Gerhard Grunwald e Prof Dr Ing Rüdiger Dillmann. "4 Co-existence: Physical Interaction and Coordinated Motion". In Advances in Human-Robot Interaction, 161–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-31509-4_14.
Testo completoAtti di convegni sul tema "Continuous physical human-Robot interaction"
Hou, Zhimin, e Dongyu Li. "Physical Human-Robot Interaction Control with Human Behavior Comprehension". In 2024 WRC Symposium on Advanced Robotics and Automation (WRC SARA), 69–75. IEEE, 2024. http://dx.doi.org/10.1109/wrcsara64167.2024.10685734.
Testo completoFausti, Roberto, Stefano Ghidini, Manuel Beschi e Nicola Pedrocchi. "Elasto-plastic Control for Physical Human-Robot Interaction". In 2024 IEEE 29th International Conference on Emerging Technologies and Factory Automation (ETFA), 01–07. IEEE, 2024. http://dx.doi.org/10.1109/etfa61755.2024.10710976.
Testo completoLong, Juncai, Jituo Li, Xiaojie Diao, Chengdi Zhou, Guodong Lu e Yixiong Feng. "Multistable Soft Actuator for Physical Human-robot Interaction". In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 4090–97. IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10801795.
Testo completoKille, Sean, Paul Leibold, Philipp Karg, Balint Varga e Sören Hohmann. "Human-Variability-Respecting Optimal Control for Physical Human-Machine Interaction". In 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN), 1595–602. IEEE, 2024. http://dx.doi.org/10.1109/ro-man60168.2024.10731297.
Testo completoSubramanian, Karthik, Sarthak Arora, Odysseus Adamides e Ferat Sahin. "Using Mixed Reality for Safe Physical Human-Robot Interaction". In 2024 IEEE Conference on Telepresence, 225–29. IEEE, 2024. https://doi.org/10.1109/telepresence63209.2024.10841696.
Testo completoWang, Chen, Yanan Li, Shuzhi Sam Ge, Keng Peng Tee e Tong Heng Lee. "Continuous critic learning for robot control in physical human-robot interaction". In 2013 13th International Conference on Control, Automaton and Systems (ICCAS). IEEE, 2013. http://dx.doi.org/10.1109/iccas.2013.6704029.
Testo completoTout, Bilal, Jason Chevrie, Antoine Dequidt e Laurent Vermeiren. "Towards Continuous Identification of Passive Human Joint Impedance Using Physical Human-Robot Interaction System". In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2023. http://dx.doi.org/10.1109/iros55552.2023.10341372.
Testo completoShe, Yu, Zhaoyuan Gu, Siyang Song, Hai-Jun Su e Junmin Wang. "A Continuously Tunable Stiffness Arm With Cable-Driven Mechanisms for Safe Physical Human-Robot Interaction". In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22035.
Testo completoSchale, Daniel, Martin F. Stoelen e Erik Kyrkjebo. "Continuous and Incremental Learning in physical Human-Robot Cooperation using Probabilistic Movement Primitives". In 2022 31st IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). IEEE, 2022. http://dx.doi.org/10.1109/ro-man53752.2022.9900547.
Testo completoColim, Ana, André Cardoso, Estela Bicho, Luís Louro, Carla Alves, Pedro Ribeiro, Débora Pereira et al. "On the development of an ergonomic approach for the design of an industrial robotic coworker". In 10th International Conference on Human Interaction and Emerging Technologies (IHIET 2023). AHFE International, 2023. http://dx.doi.org/10.54941/ahfe1004004.
Testo completoRapporti di organizzazioni sul tema "Continuous physical human-Robot interaction"
Vantassel, Stephen M., e Brenda K. Osthus. Safety. U.S. Department of Agriculture, Animal and Plant Health Inspection Service, novembre 2018. http://dx.doi.org/10.32747/2018.7208746.ws.
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