Добірка наукової літератури з теми "Control and learning of soft robots"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся зі списками актуальних статей, книг, дисертацій, тез та інших наукових джерел на тему "Control and learning of soft robots".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Статті в журналах з теми "Control and learning of soft robots"
Iscen, Atil, Ken Caluwaerts, Jonathan Bruce, Adrian Agogino, Vytas SunSpiral, and Kagan Tumer. "Learning Tensegrity Locomotion Using Open-Loop Control Signals and Coevolutionary Algorithms." Artificial Life 21, no. 2 (May 2015): 119–40. http://dx.doi.org/10.1162/artl_a_00163.
Повний текст джерелаKim, 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 джерелаHamaya, Masashi, Kazutoshi Tanaka, Felix von Drigalski, and Yoshihisa Ijiri. "Learning Control with Soft Robots: Application for Industrial Assembly." Journal of the Robotics Society of Japan 39, no. 7 (2021): 609–12. http://dx.doi.org/10.7210/jrsj.39.609.
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 джерелаVan Meerbeek, I. M., C. M. De Sa, and R. F. Shepherd. "Soft optoelectronic sensory foams with proprioception." Science Robotics 3, no. 24 (November 28, 2018): eaau2489. http://dx.doi.org/10.1126/scirobotics.aau2489.
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 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 джерелаPletl, Szilveszter, and Bela Lantos. "Advanced Robot Control Algorithms Based on Fuzzy, Neural and Genetic Methods." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 2 (March 20, 2001): 81–89. http://dx.doi.org/10.20965/jaciii.2001.p0081.
Повний текст джерелаKAWAMURA, KAZUHIKO, R. ALAN PETERS II, ROBERT E. BODENHEIMER, NILANJAN SARKAR, JUYI PARK, CHARLES A. CLIFTON, ALBERT W. SPRATLEY, and KIMBERLY A. HAMBUCHEN. "A PARALLEL DISTRIBUTED COGNITIVE CONTROL SYSTEM FOR A HUMANOID ROBOT." International Journal of Humanoid Robotics 01, no. 01 (March 2004): 65–93. http://dx.doi.org/10.1142/s021984360400006x.
Повний текст джерелаДисертації з теми "Control and learning of soft robots"
Pajon, Adrien. "Humanoid robots walking with soft soles." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTS060/document.
Повний текст джерелаWhen unexpected changes of the ground surface occur while walking, the human central nervous system needs to apply appropriate control actions to assure dynamic stability. Many studies in the motor control field have investigated the mechanisms of such a postural control and have widely described how center of mass (COM) trajectories, step patterns and muscle activity adapt to avoid loss of balance. Measurements we conducted show that when stepping over a soft ground, participants actively modulated the ground reaction forces (GRF) under the supporting foot in order to exploit the elastic and compliant properties of the surface to dampen the impact and to likely dissipate the mechanical energy accumulated during the ‘fall’ onto the new compliant surface.In order to control more efficiently the feet-ground interaction of humanoid robots during walking, we propose adding outer soft (i.e. compliant) soles to the feet. They absorb impacts and cast ground unevenness during locomotion on rough terrains. However, they introduce passive degrees of freedom (deformations under the feet) that complexify the tasks of state estimation and overall robot stabilization. To address this problem, we devised a new walking pattern generator (WPG) based on a minimization of the energy consumption that offers the necessary parameters to be used jointly with a sole deformation estimator based on finite element model (FEM) of the soft sole to take into account the sole deformation during the motion. Such FEM computation is time costly and inhibit online reactivity. Hence, we developed a control loop that stabilizes humanoid robots when walking with soft soles on flat and uneven terrain. Our closed-loop controller minimizes the errors on the center of mass (COM) and the zero-moment point (ZMP) with an admittance control of the feet based on a simple deformation estimator. We demonstrate its effectiveness in real experiments on the HRP-4 humanoid walking on gravels
Kraus, Dustan Paul. "Coordinated, Multi-Arm Manipulation with Soft Robots." BYU ScholarsArchive, 2018. https://scholarsarchive.byu.edu/etd/7066.
Повний текст джерелаKandhari, Akhil. "Control and Analysis of Soft Body Locomotion on a Robotic Platform." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1579793861351961.
Повний текст джерелаMarchese, Andrew D. (Andrew Dominic). "Design, fabrication, and control of soft robots with fluidic elastomer actuators." Thesis, Massachusetts Institute of Technology, 2015. http://hdl.handle.net/1721.1/97807.
Повний текст джерелаCataloged from PDF version of thesis.
Includes bibliographical references (pages 223-236).
The goal of this thesis is to explore how autonomous robotic systems can be created with soft elastomer bodies powered by fluids. In this thesis we innovate in the design, fabrication, control, and experimental validation of both single and multi-segment soft fluidic elastomer robots. First, this thesis describes an autonomous fluidic elastomer robot that is both self-contained and capable of rapid, continuum body motion. Specifically, the design, modeling, fabrication, and control of a soft fish is detailed, focusing on enabling the robot to perform rapid escape responses. The robot employs a compliant body with embedded actuators emulating the slender anatomical form of a fish. In addition, the robot has a novel fluidic actuation system that drives body motion and has all the subsystems of a traditional robot on-board: power, actuation, processing, and control. At the core of the fish's soft body is an array of Fluidic Elastomer Actuators (FEAs). The fish is designed to emulate escape responses in addition to forward swimming because such maneuvers require rapid body accelerations and continuum body motion. These maneuvers showcase the performance capabilities of this self-contained robot. The kinematics and controllability of the robot during simulated escape response maneuvers are analyzed and compared to studies on biological fish. During escape responses, the soft-bodied robot is shown to have similar input-output relationships to those observed in biological fish. The major implication of this portion of the thesis is that a soft fluidic elastomer robot is shown to be both self-contained and capable of rapid body motion. Next, this thesis provides an approach to planar manipulation using soft fluidic elastomer robots. That is, novel approaches to design, fabrication, kinematic modeling, power, control, and planning as well as extensive experimental evaluations with multiple manipulator prototypes are presented. More specifically, three viable manipulator morphologies composed entirely from soft silicone rubber are explored, and these morphologies are differentiated by their actuator structures, namely: ribbed, cylindrical, and pleated. Additionally, three distinct casting-based fabrication processes are explored: lamination-based casting, retractable-pin-based casting, and lost-wax- based casting. Furthermore, two ways of fabricating a multiple DOF manipulator are explored: casting the complete manipulator as a whole, and casting single DOF segments with subsequent concatenation. An approach to closed-loop configuration control is presented using a piecewise constant curvature kinematic model, real-time localization data, and novel fluidic drive cylinders which power actuation. Multi-segment forward and inverse kinematic algorithms are developed and combined with the configuration controller to provide reliable task-space position control. Building on these developments, a suite of task-space planners are presented to demonstrate new autonomous capabilities from these soft robots such as: (i) tracking a path in free-space, (ii) maneuvering in confined environments, and (iii) grasping and placing objects. Extensive evaluations of these capabilities with physical prototypes demonstrate that manipulation with soft fluidic elastomer robots is viable. Lastly, this thesis presents a robotic manipulation system capable of autonomously positioning a multi-segment soft fluidic elastomer robot in three dimensions while subject to the self-loading effects of gravity. Specifically, an extremely soft robotic manipulator morphology that is composed entirely from low durometer elastomer, powered by pressurized air, and designed to be both modular and durable is presented. To understand the deformation of a single arm segment, a static physics-based model is developed and experimentally validated. Then, to kinematically model the multi-segment manipulator, a piece-wise constant curvature assumption consistent with more traditional continuum manipulators is used. Additionally, a complete fabrication process for this new manipulator is defined and used to make multiple functional prototypes. In order to power the robot's spatial actuation, a high capacity fluidic drive cylinder array is implemented, providing continuously variable, closed-circuit gas delivery. Next, using real-time localization data, a processing and control algorithm is developed that generates realizable kinematic curvature trajectories and controls the manipulator's configuration along these trajectories. A dynamic model for this multi-body fluidic elastomer manipulator is also developed along with a strategy for independently identifying all unknown components of the system: the soft manipulator, its distributed fluidic elastomer actuators, as well as its drive cylinders. Next, using this model and trajectory optimization techniques locally-optimal, open-loop control policies are found. Lastly, new capabilities offered by this soft fluidic elastomer manipulation system are validated with extensive physical experiments. These are: (i) entering and advancing through confined three-dimensional environments, (ii) conforming to goal shape-configurations within a sagittal plane under closed-loop control, and (iii) performing dynamic maneuvers we call grabs.
by Andrew D. Marchese.
Ph. D.
Pan, Min, Zhe Hao, Chenggang Yuan, and Andrew Plummer. "Development and control of smart pneumatic mckibben muscles for soft robots." Technische Universität Dresden, 2020. https://tud.qucosa.de/id/qucosa%3A71262.
Повний текст джерелаZhang, Zhongkai. "Vision-based calibration, position control and force sensing for soft robots." Thesis, Lille 1, 2019. http://www.theses.fr/2019LIL1I001/document.
Повний текст джерелаThe modeling of soft robots which have, theoretically, infinite degrees of freedom, are extremely difficult especially when the robots have complex configurations. This difficulty of modeling leads to new challenges for the calibration and the control design of the robots, but also new opportunities with possible new force sensing strategies. This dissertation aims to provide new and general solutions using modeling and vision. The thesis at first presents a discrete-time kinematic model for soft robots based on the real-time Finite Element (FE) method. Then, a vision-based simultaneous calibration of sensor-robot system and actuators is investigated. Two closed-loop position controllers are designed. Besides, to deal with the problem of image feature loss, a switched control strategy is proposed by combining both the open-loop controller and the closed-loop controller. Using soft robot itself as a force sensor is available due to the deformable feature of soft structures. Two methods (marker-based and marker-free) of external force sensing for soft robots are proposed based on the fusion of vision-based measurements and FE model. Using both methods, not only the intensities but also the locations of the external forces can be estimated.As a specific application, a cable-driven continuum catheter robot through contacts is modeled based on FE method. Then, the robot is controlled by a decoupled control strategy which allows to control insertion and bending independently. Both the control inputs and the contact forces along the entire catheter can be computed by solving a quadratic programming (QP) problem with a linear complementarity constraint (QPCC)
Gaskett, Chris. "Q-Learning for robot control." View thesis entry in Australian Digital Theses Program, 2002. http://eprints.jcu.edu.au/623/1/gaskettthesis.pdf.
Повний текст джерелаHyatt, Phillip Edmond. "Robust Real-Time Model Predictive Control for High Degree of Freedom Soft Robots." BYU ScholarsArchive, 2020. https://scholarsarchive.byu.edu/etd/8453.
Повний текст джерелаMirano, Geronimo (Geronimo J. ). "Jacobian-based control of soft robots for manipulation using implicit surface models." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/113126.
Повний текст джерелаThis electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (page 47).
Soft robot hands offer numerous advantages over rigid ones for manipulation, including robustness and safety. Yet, compared to rigid robots, soft robots are characterized by continuous mechanics, and finite-element approximations with many degrees of freedom present a significant obstacle for modern control approaches. The central question my thesis explores is whether we can capture the benefits of soft robot hands with relatively simple dynamical models. Specifically, we demonstrate a very simple model of a 2D soft manipulator that uses pulleys and cables to model deformable surfaces. This model captures much of the qualitative behavior of soft membranes, while also proving amenable to modern control techniques. We validate this model physically using a hardware set-up. We then demonstrate a simple quasi-static Jacobian controller which solves a second-order cone program to achieve the task of in-hand object repositioning.
by Geronimo Mirano.
M. Eng.
Yang, Hee Doo. "Design, Manufacturing, and Control of Soft and Soft/Rigid Hybrid Pneumatic Robotic Systems." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/100635.
Повний текст джерелаDoctor of Philosophy
Книги з теми "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 джерелаDesign and control of intelligent robotic systems. Berlin: Springer, 2009.
Знайти повний текст джерелаTowards real learning robots. Frankfurt am Main: Peter Lang, 2000.
Знайти повний текст джерелаTowards real learning robots. Frankfurt am Main: Peter Lang, 1999.
Знайти повний текст джерелаde, Velde Walter Van, ed. Toward learning robots. Cambridge, Mass: MIT Press, 1993.
Знайти повний текст джерелаCrangle, Colleen. Language and learning for robots. Stanford, Calif: Center for the Study of Language and Information, 1994.
Знайти повний текст джерелаInoue, Takahiro. Mechanics and control of soft-fingered manipulation. London: Springer, 2009.
Знайти повний текст джерелаInoue, Takahiro. Mechanics and control of soft-fingered manipulation. London: Springer, 2009.
Знайти повний текст джерелаInoue, Takahiro. Mechanics and control of soft-fingered manipulation. London: Springer, 2009.
Знайти повний текст джерела1963-, Hirai Shinʼichi, ed. Mechanics and control of soft-fingered manipulation. London: Springer, 2009.
Знайти повний текст джерелаЧастини книг з теми "Control and learning of soft robots"
Zhang, Haochong, Rongyun Cao, Shlomo Zilberstein, Feng Wu, and Xiaoping Chen. "Toward Effective Soft Robot Control via Reinforcement Learning." In Intelligent Robotics and Applications, 173–84. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65289-4_17.
Повний текст джерелаLee, Wei-Po, and Tsung-Hsien Yang. "Learning RNN-Based Gene Regulatory Networks for Robot Control." In Advances in Intelligent and Soft Computing, 93–102. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03156-4_10.
Повний текст джерелаRafajłowicz, Ewaryst, and Wojciech Rafajłowicz. "Iterative Learning of Optimal Control – Case Study of the Gantry Robot." In Artificial Intelligence and Soft Computing, 337–46. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59060-8_30.
Повний текст джерелаFujita, Hamido, and Yu-Chien Ko. "Subjective Weights Based Meta-Learning in Multi-criteria Decision Making." In Advances in Soft Computing, Intelligent Robotics and Control, 109–25. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-05945-7_7.
Повний текст джерелаTushkanov, Nikolay, Vladimir Nazarov, Alla Kuznetsova, and Olga Tushkanova. "Multi-sensor System of Intellectual Handling Robot Control on the Basis of Collective Learning Paradigm." In Advances in Intelligent and Soft Computing, 195–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25661-5_26.
Повний текст джерелаZhang, Yunce, Tao Wang, Ning Tan, and Shiqiang Zhu. "Open-Loop Motion Control of a Hydraulic Soft Robotic Arm Using Deep Reinforcement Learning." In Intelligent Robotics and Applications, 302–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89095-7_30.
Повний текст джерелаHaddadin, Sami. "Soft-Robotics Control." In Towards Safe Robots, 25–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40308-8_3.
Повний текст джерелаAccame, M. "Learning to Control a Visual Sensing System." In Making Robots Smarter, 109–25. Boston, MA: Springer US, 1999. http://dx.doi.org/10.1007/978-1-4615-5239-0_7.
Повний текст джерелаGrube, Malte, and Robert Seifried. "An Optical Curvature Sensor for Soft Robots." In ROMANSY 24 - Robot Design, Dynamics and Control, 125–32. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-06409-8_13.
Повний текст джерелаLi, Mengdan, Yu Huo, Gong Wang, Yifei Liu, and Bingshan Liu. "Soft Variable Structure Control in Flexible-Joint Robots." In Advances in Intelligent Systems and Computing, 801–7. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-0238-5_84.
Повний текст джерелаТези доповідей конференцій з теми "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 джерелаZhao, Leidi, Raheem Lawhorn, Siddharth Patil, Steve Susanibar, Lu Lu, Cong Wang, and Bo Ouyang. "Multiform Adaptive Robot Skill Learning From Humans." In ASME 2017 Dynamic Systems and Control Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/dscc2017-5114.
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 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 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 джерелаYou, Xuanke, Yixiao Zhang, Xiaotong Chen, Xinghua Liu, Zhanchi Wang, Hao Jiang, and Xiaoping Chen. "Model-free control for soft manipulators based on reinforcement learning." In 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2017. http://dx.doi.org/10.1109/iros.2017.8206123.
Повний текст джерелаGillespie, Morgan T., Charles M. Best, Eric C. Townsend, David Wingate, and Marc D. Killpack. "Learning nonlinear dynamic models of soft robots for model predictive control with neural networks." In 2018 IEEE International Conference on Soft Robotics (RoboSoft). IEEE, 2018. http://dx.doi.org/10.1109/robosoft.2018.8404894.
Повний текст джерелаLi, Yingqi, Xiaomei Wang, and Ka-Wai Kwok. "Towards Adaptive Continuous Control of Soft Robotic Manipulator using Reinforcement Learning." In 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2022. http://dx.doi.org/10.1109/iros47612.2022.9981335.
Повний текст джерелаWang, Xinran, and Nicolas Rojas. "A Data-Efficient Model-Based Learning Framework for the Closed-Loop Control of Continuum Robots." In 2022 IEEE 5th International Conference on Soft Robotics (RoboSoft). IEEE, 2022. http://dx.doi.org/10.1109/robosoft54090.2022.9762115.
Повний текст джерелаYan, Changzhi, Qiyuan Zhang, Zhaoyang Liu, Xueqian Wang, and Bin Liang. "Control of Free-Floating Space Robots to Capture Targets Using Soft Q-Learning." In 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2018. http://dx.doi.org/10.1109/robio.2018.8665049.
Повний текст джерелаЗвіти організацій з теми "Control and learning of soft robots"
Abdula, Andrii I., Halyna A. Baluta, Nadiia P. Kozachenko, and Darja A. Kassim. Peculiarities of using of the Moodle test tools in philosophy teaching. [б. в.], July 2020. http://dx.doi.org/10.31812/123456789/3867.
Повний текст джерела