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Статті в журналах з теми "Modeling, control and learning of soft robots"
Kim, Daekyum, Sang-Hun Kim, Taekyoung Kim, Brian Byunghyun Kang, Minhyuk Lee, Wookeun Park, Subyeong Ku, et al. "Review of machine learning methods in soft robotics." PLOS ONE 16, no. 2 (February 18, 2021): e0246102. http://dx.doi.org/10.1371/journal.pone.0246102.
Повний текст джерелаCaremel, Cedric, Matthew Ishige, Tung D. Ta, and Yoshihiro Kawahara. "Echo State Network for Soft Actuator Control." Journal of Robotics and Mechatronics 34, no. 2 (April 20, 2022): 413–21. http://dx.doi.org/10.20965/jrm.2022.p0413.
Повний текст джерелаSun, Boai, Weikun Li, Zhangyuan Wang, Yunpeng Zhu, Qu He, Xinyan Guan, Guangmin Dai, et al. "Recent Progress in Modeling and Control of Bio-Inspired Fish Robots." Journal of Marine Science and Engineering 10, no. 6 (June 2, 2022): 773. http://dx.doi.org/10.3390/jmse10060773.
Повний текст джерелаDai, Yicheng, Zhihao Deng, Xin Wang, and Han Yuan. "A Hybrid Controller for a Soft Pneumatic Manipulator Based on Model Predictive Control and Iterative Learning Control." Sensors 23, no. 3 (January 22, 2023): 1272. http://dx.doi.org/10.3390/s23031272.
Повний текст джерелаWu, Qiuxuan, Yueqin Gu, Yancheng Li, Botao Zhang, Sergey A. Chepinskiy, Jian Wang, Anton A. Zhilenkov, Aleksandr Y. Krasnov, and Sergei Chernyi. "Position Control of Cable-Driven Robotic Soft Arm Based on Deep Reinforcement Learning." Information 11, no. 6 (June 8, 2020): 310. http://dx.doi.org/10.3390/info11060310.
Повний текст джерелаJiang, Hao, Zhanchi Wang, Yusong Jin, Xiaotong Chen, Peijin Li, Yinghao Gan, Sen Lin, and Xiaoping Chen. "Hierarchical control of soft manipulators towards unstructured interactions." International Journal of Robotics Research 40, no. 1 (January 2021): 411–34. http://dx.doi.org/10.1177/0278364920979367.
Повний текст джерелаYoussef, Samuel M., MennaAllah Soliman, Mahmood A. Saleh, Mostafa A. Mousa, Mahmoud Elsamanty, and Ahmed G. Radwan. "Modeling of Soft Pneumatic Actuators with Different Orientation Angles Using Echo State Networks for Irregular Time Series Data." Micromachines 13, no. 2 (January 29, 2022): 216. http://dx.doi.org/10.3390/mi13020216.
Повний текст джерелаShi, Yunde, Mingqiu Guo, Chang Hui, Shilin Li, Xiaoqiang Ji, Yuan Yang, Xiang Luo, and Dan Xia. "Learning-Based Repetitive Control of a Bowden-Cable-Actuated Exoskeleton with Frictional Hysteresis." Micromachines 13, no. 10 (October 4, 2022): 1674. http://dx.doi.org/10.3390/mi13101674.
Повний текст джерелаBaysal, Cabbar Veysel. "An Inverse Dynamics-Based Control Approach for Compliant Control of Pneumatic Artificial Muscles." Actuators 11, no. 4 (April 16, 2022): 111. http://dx.doi.org/10.3390/act11040111.
Повний текст джерелаCursi, Francesco, George P. Mylonas, and Petar Kormushev. "Adaptive Kinematic Modelling for Multiobjective Control of a Redundant Surgical Robotic Tool." Robotics 9, no. 3 (August 31, 2020): 68. http://dx.doi.org/10.3390/robotics9030068.
Повний текст джерелаДисертації з теми "Modeling, control and learning of soft robots"
Morales, Bieze Thor. "Contribution to the kinematic modeling and control of soft manipulators using computational mechanics." Thesis, Lille 1, 2017. http://www.theses.fr/2017LIL10112/document.
Повний текст джерелаThis work provides new methods for the kinematic modeling and control of soft, continuum manipulators based on the Finite Element Method. Contrary to the case of rigid manipulators, soft and continuum manipulators generate their motion by deformation, therefore, the proposed methodology accounts for the deformation mechanics to better describe the kinematics of these type of robots. This methodology does not produce analytic solutions, instead, a numerical approximation is provided by methods derived from Computational Mechanics. The methodology is applied to a continuum manipulator, namely, the Compact Bionic Handling Assistant (CBHA). A closed-loop control scheme based on control allocation is also presented. The models and controller are validated experimentally
Lakhal, Othman. "Contribution to the modeling and control of hyper-redundant robots : application to additive manufacturing in the construction." Thesis, Lille 1, 2018. http://www.theses.fr/2018LIL1I061/document.
Повний текст джерелаAdditive manufacturing technology has been identified as one of the major digital innovations that has revolutionized not only industry, but also building. From a research point of view, additive manufacturing remains a very relevant topic. It is an automated process for depositing materials layer by layer to print houses or small structures for on-site assembly. In additive manufacturing processes, the deposition of materials is generally followed by a printing quality control step. However, the geometry of structures printed with funicular surfaces is sometimes complex, as robots with rigid structures cannot reach certain areas of the structure to be inspected. In this thesis, a flexible and highly redundant manipulator equipped with a camera is attached to the end-effector of a mobile manipulator robot for the quality inspection process of the printed structures. Indeed, soft manipulators can bend along their surounded 3D objects; and this inherent flexibility makes them suitable for navigation in crowded environments. As the number of controlled actuators is greater than the dimension of the workspace, this thesis can be summarized as a trajectory tracking of hyper-redundant robots. In this thesis, a hybrid approach that combines the advantages of model-based approaches and learning-based approaches is developed to model and solve the kinematics of soft and hyper-redundant manipulators. The principle is to develop mathematical models with reasonable assumptions, and to improve their accuracy through learning processes. The performance of the proposed approach is validated by performing a series of simulations and experiments applied to the compact bionic handling arm (cbha) robot
Oliveira, Artur João Anjos. "Ultrasound Tracking and Closed-Loop Control of Magnetically-Actuated Biomimetic Soft Robot." Master's thesis, 2022. http://hdl.handle.net/10316/99395.
Повний текст джерелаSoft robots atuados por magnetismo podem fornecer potenciais aplicações médicas e revolucionar a área de intervenções minimamente invasivas. A sua natureza mole e sem fios permite a navegação para alvos de difícil alcance do corpo humano sem danificar os tecidos circundantes. Além disso, a atuação magnética é livre de radiação, não é prejudicial para os seres humanos e elimina a necessidade de ter uma fonte de energia a bordo do robô. Apesar dos recentes desenvolvimentos no projeto e atuação deste tipo de robôs, existem alguns desafios, como localização, perceção e planeamento de caminhos, a serem superados para poderem realizar tarefas em ambientes desafiadores.O objetivo principal do projeto é alcançar o controlo de movimento em malha fechada e o planeamento de um soft robot, o Milípede, usando imagens de ultrassom. Neste estudo, integramos estratégias de localização e controlo num sistema de atuação magnética para direcionar com segurança o soft robot para um alvo. Em relação ao controlo, um controlador Proporcional Integrativo (PI) é usado para calcular as velocidades lineares e angulares para conduzir o robô pelo espaço de trabalho evitando obstáculos. Consoante as velocidades, o campo magnético correspondente é aplicado, utilizando um conjunto com seis bobinas eletromagnéticas. A localização é obtida primeiro de uma câmara a olhar para o espaço de trabalho como prova de conceito dos métodos de controlo e planeamento de movimento. Em seguida, comparamos o desempenho entre dois algoritmos de ultrassom, um geométrico e uma abordagem de aprendizagem profunda, para estimar a pose do Milípede. Por fim, o controlo de circuito fechado do soft robot é obtido, utilizando imagens de ultrassom. Os resultados mostram a possibilidade de usar os soft robots para realizar tarefas de forma autónoma em cenários clinicamente relevantes.
Untethered magnetically actuated soft robots can provide potential medical applications and revolutionize the field of minimally invasive interventions. Its soft, untethered nature allows the navigation to difficult-to-reach targets of the human body without damaging the surrounding tissues. Moreover, magnetic actuation is radiation-free, not harmful for humans and removes the need to have an on-board source of energy in the robot. Despite the recent developments in the design and actuation of soft robots, there are some challenges, such as localization, perception, and path planning, to overcome so that they can perform tasks in challenging environments.The main goal of the current project is to achieve closed-loop motion control and planning of a soft robot, the Millipede, using ultrasound imaging technique. In this study, we integrate localization and control strategies into a magnetic actuation system to safely steer the untethered soft robot to a target. Regarding the control, a Proportional Integrative (PI) controller is used to calculate the linear and angular velocities to steer the robot through the workspace while avoiding obstacles. According to the velocities, the corresponding magnetic field is applied, using a setup with six electromagnetic coils. The localization is first obtained from a top-view camera as a proof-of-concept of the motion control and planning methods. Then, we compare the performance between two ultrasound algorithms, geometric and a deep learning approach, to estimate the pose of the Millipede. Finally, the closed-loop control of the untethered soft robot is achieved using ultrasound imaging. The results show the possibility of using the soft robots to autonomously perform tasks in clinically relevant scenarios.
Lu, Bo active 21st century. "Improving process monitoring and modeling of batch-type plasma etching tools." Thesis, 2015. http://hdl.handle.net/2152/30486.
Повний текст джерелаКниги з теми "Modeling, control and learning of soft robots"
Stefan, Wermter, Palm Günther, and Elshaw Mark, eds. Biomimetic neural learning for intelligent robots: Intelligent systems, cognitive robotics, intelligent robots. Berlin: Springer, 2005.
Знайти повний текст джерелаBiomimetic Neural Learning for Intelligent Robots: Intelligent Systems, Cognitive Robotics, and Neuroscience (Lecture Notes in Computer Science). Springer, 2005.
Знайти повний текст джерелаMetta, Giorgio. Humans and humanoids. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0047.
Повний текст джерелаЧастини книг з теми "Modeling, control and learning of soft robots"
Huang, Weicheng, Zachary Patterson, Carmel Majidi, and M. Khalid Jawed. "Modeling Soft Swimming Robots using Discrete Elastic Rod Method." In Bioinspired Sensing, Actuation, and Control in Underwater Soft Robotic Systems, 247–59. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50476-2_13.
Повний текст джерелаWang, Jing, Jinglin Zhou, and Xiaolu Chen. "Multivariate Statistics Between Two-Observation Spaces." In Intelligent Control and Learning Systems, 31–44. Singapore: Springer Singapore, 2022. http://dx.doi.org/10.1007/978-981-16-8044-1_3.
Повний текст джерелаİlman, Mehmet Mert, and Pelin Yildirim Taser. "Machine Learning and Optimization Applications for Soft Robotics." In Design and Control Advances in Robotics, 13–29. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5381-0.ch002.
Повний текст джерелаZolfagharian, Ali, Mahdi Bodaghi, Pejman Heidarian, Abbas Z. Kouzani, and Akif Kaynak. "Closed-loop control of 4D-printed hydrogel soft robots." In Smart Materials in Additive Manufacturing, Volume 2 : 4D Printing Mechanics, Modeling, and Advanced Engineering Applications, 251–78. Elsevier, 2022. http://dx.doi.org/10.1016/b978-0-323-95430-3.00009-9.
Повний текст джерелаRodriguez, Ricardo, Ivo Bukovsky, and Noriyasu Homma. "Potentials of Quadratic Neural Unit for Applications." In Advances in Abstract Intelligence and Soft Computing, 343–54. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2651-5.ch023.
Повний текст джерелаMainzer, Klaus. "Challenges of Complex Systems in Cognitive and Complex Systems." In Thinking Machines and the Philosophy of Computer Science, 367–84. IGI Global, 2010. http://dx.doi.org/10.4018/978-1-61692-014-2.ch022.
Повний текст джерелаТези доповідей конференцій з теми "Modeling, control and learning of soft robots"
Rajendran, Sunil Kumar, and Feitian Zhang. "Learning Based Speed Control of Soft Robotic Fish." In ASME 2018 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/dscc2018-8977.
Повний текст джерелаPawlowski, Ben, Charles W. Anderson, and Jianguo Zhao. "Dynamic Control of Soft Robots Using Reinforcement Learning." In ASME 2019 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dscc2019-9181.
Повний текст джерелаLuo, Ming, Mahdi Agheli, and Cagdas D. Onal. "Theoretical Modeling of a Pressure-Operated Soft Snake Robot." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-35340.
Повний текст джерелаWiese, Mats, Gundula Runge-Borchert, Benjamin-Hieu Cao, and Annika Raatz. "Transfer learning for accurate modeling and control of soft actuators." In 2021 IEEE 4th International Conference on Soft Robotics (RoboSoft). IEEE, 2021. http://dx.doi.org/10.1109/robosoft51838.2021.9479300.
Повний текст джерелаChen, Xiaotian, Paolo Stegagno, Wei Zeng, and Chengzhi Yuan. "Localized Motion Dynamics Modeling of A Soft Robot: A Data-Driven Adaptive Learning Approach." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867191.
Повний текст джерелаLuo, Shuzhen, Merrill Edmonds, Jingang Yi, Xianlian Zhou, and Yantao Shen. "Spline-Based Modeling and Control of Soft Robots." In 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2020. http://dx.doi.org/10.1109/aim43001.2020.9158917.
Повний текст джерелаModugno, Valerio, Gerard Neumann, Elmar Rueckert, Giuseppe Oriolo, Jan Peters, and Serena Ivaldi. "Learning soft task priorities for control of redundant robots." In 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016. http://dx.doi.org/10.1109/icra.2016.7487137.
Повний текст джерелаHofer, Matthias, and Raffaello D'Andrea. "Design, Modeling and Control of a Soft Robotic Arm." In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018. http://dx.doi.org/10.1109/iros.2018.8594221.
Повний текст джерелаLargilliere, Frederick, Valerian Verona, Eulalie Coevoet, Mario Sanz-Lopez, Jeremie Dequidt, and Christian Duriez. "Real-time control of soft-robots using asynchronous finite element modeling." In 2015 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2015. http://dx.doi.org/10.1109/icra.2015.7139541.
Повний текст джерелаYujun Lin and Weiwu Yan. "Study of soft sensor modeling based on deep learning." In 2015 American Control Conference (ACC). IEEE, 2015. http://dx.doi.org/10.1109/acc.2015.7172253.
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