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Добірка наукової літератури з теми "Robotics hand"

Автор: Grafiati

Опубліковано: 4 квітня 2023

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Статті в журналах з теми "Robotics hand"

Parida, P. K., Bibhuti Bhusan Biswal, and M. R. Khan. "Kinematic Modeling and Analysis of a Multifingered Robotic Hand." Advanced Materials Research 383-390 (November 2011): 6684–88. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.6684.

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Precise and secure handling of flexible or irregularly shaped objects by robotic hands has become a challenge. Robot hands used in medical robotics and rehabilitation robotics need to be anthropomorphic to do the desired tasks. Although it is possible to develop robotic hands which can be very closely mapped to human hands, it is sometimes poses several problems due to control, manufacturing and economic reasons. The present work aims at designing and developing a robotic hand with five fingers for manipulation of objects. The kinematic modeling and its analysis, as a part of the development process is presented in this paper. The simulation results of the hand shows that the conceptualized design is yielding the desired result and works very efficiently.

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Leiper, K. J. "Robotics - a helping hand?" TrAC Trends in Analytical Chemistry 4, no. 2 (February 1985): 40–43. http://dx.doi.org/10.1016/0165-9936(85)85022-6.

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Bahrin, Syed Zainal Abidin Syed Kamarul, and Khairul Salleh Mohamed Sahari. "Initial Development of a Master-Slave Controller for a Five-Fingered Robotic Hand Design by Using Pressure Sensors Comparator Technique." International Journal of Engineering & Technology 7, no. 4.35 (November 30, 2018): 765. http://dx.doi.org/10.14419/ijet.v7i4.35.23104.

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There are numerous robotic hand designs but the five-fingered robotic hand design is the most dexterous robotic hand design due to its similar appearance and motions with the human hands. The five-fingered robotic hands are commonly controlled or governed through a master-slave system that can be accomplished by using simple preset motions or other complicated and advanced technologies. However, a five-fingered robotics hand can also be controlled by a novel approach known as pressure sensors comparator technique. This technique compares the values from the pressure sensors that are strategically located at the glove (master) and robotic hand (slave). If the values differ, the actuators will generate motions accordingly. The initial finding based on the master and slave prototypes showed that applying this technique is very challenging due to the humans' physiological diversity. Nevertheless, a solution was proposed for further studies and future developments by introducing an offset.

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Shaji, Ashwin K., and Rinku Dhiman. "Gesture Controlled Robotic Hand Using RF Unit and Accelerometer." International Journal of Research in Engineering, Science and Management 3, no. 11 (November 30, 2020): 125–27. http://dx.doi.org/10.47607/ijresm.2020.387.

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In the race of man v/s machine, automation comes as a companion of man and machine. Taking the technology to the next level from the mobile driven world to an automation driven world, will increase manufacturers their production rates, productivity and efficiency with materials, product quality, and worker safety. From ancient times the ingenuity and the brain power human beings have astonished researchers with engineering and mechanical marvels like the wheel, bow and arrow, cross bows, etc. What started from the wheel did not end there but evolved into the complex mechatronics systems that we see around us today. The robotics is one such human marvel that will be one-day equal human beings themselves. The robots thus have far more use in the daily life than any other systems. The robotics and automation is a rising piece of technology which could lessen the loads of work and solve the problems exponentially. As robotics is finding its place on every sector in this globe, the aim this project is to introduce robotics in the field of industry. The title of the system is ‘Gesture controlled robotic arm’. The aim of the system is to provide safety and to increase productivity in our industries. The research project should be designed in such a way that it should occupy minimum space, should possess high maneuverability and high agility. The project in discussion is types of robots which needs minimum space and are proved to be highly maneuverable and highly agile. The robot contains two main units, one is the robotic arm and second is the data glove with accelerometer using a RF controller. The robotic arm unit is responsible for the hand functions of the whole structure of the robot. The data glove is responsible for the input feedback to the robotic arm. The robotic unit will be controlled by an Arduino platform to improve its stability. The angle tilt will be measured using ADXL335 sensor. The ADXL335 uses angle, tilt and yaw values with Arduino for data transfer. Through advanced primary and secondary research techniques, system implementation hurdles and potential risks involved in developing such a system are identified. The project is fully planned using advanced project management techniques like PERT chart and Gantt chart in order to identify the critical activities and the timeline related with it.

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Ono, Eiichi. "KANSEI and Robotics. Robotic Kansei Measurement of Hand Value." Journal of the Robotics Society of Japan 17, no. 7 (1999): 928–32. http://dx.doi.org/10.7210/jrsj.17.928.

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Shahid, Talha, Darwin Gouwanda, Surya G. Nurzaman, and Alpha A. Gopalai. "Moving toward Soft Robotics: A Decade Review of the Design of Hand Exoskeletons." Biomimetics 3, no. 3 (July 18, 2018): 17. http://dx.doi.org/10.3390/biomimetics3030017.

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Soft robotics is a branch of robotics that deals with mechatronics and electromechanical systems primarily made of soft materials. This paper presents a summary of a chronicle study of various soft robotic hand exoskeletons, with different electroencephalography (EEG)- and electromyography (EMG)-based instrumentations and controls, for rehabilitation and assistance in activities of daily living. A total of 45 soft robotic hand exoskeletons are reviewed. The study follows two methodological frameworks: a systematic review and a chronological review of the exoskeletons. The first approach summarizes the designs of different soft robotic hand exoskeletons based on their mechanical, electrical and functional attributes, including the degree of freedom, number of fingers, force transmission, actuation mode and control strategy. The second approach discusses the technological trend of soft robotic hand exoskeletons in the past decade. The timeline analysis demonstrates the transformation of the exoskeletons from rigid ferrous materials to soft elastomeric materials. It uncovers recent research, development and integration of their mechanical and electrical components. It also approximates the future of the soft robotic hand exoskeletons and some of their crucial design attributes.

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Pozzi, Maria, Sara Marullo, Gionata Salvietti, Joao Bimbo, Monica Malvezzi, and Domenico Prattichizzo. "Hand closure model for planning top grasps with soft robotic hands." International Journal of Robotics Research 39, no. 14 (August 10, 2020): 1706–23. http://dx.doi.org/10.1177/0278364920947469.

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Automating the act of grasping is one of the most compelling challenges in robotics. In recent times, a major trend has gained the attention of the robotic grasping community: soft manipulation. Along with the design of intrinsically soft robotic hands, it is important to devise grasp planning strategies that can take into account the hand characteristics, but are general enough to be applied to different robotic systems. In this article, we investigate how to perform top grasps with soft hands according to a model-based approach, using both power and precision grasps. The so-called closure signature (CS) is used to model closure motions of soft hands by associating to them a preferred grasping direction. This direction can be aligned to a suitable direction over the object to achieve successful top grasps. The CS-alignment is here combined with a recently developed AI-driven grasp planner for rigid grippers that is adjusted and used to retrieve an estimate of the optimal grasp to be performed on the object. The resulting grasp planner is tested with multiple experimental trials with two different robotic hands. A wide set of objects with different shapes was grasped successfully.

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Biswal, Deepak Ranjan, and Pramod Kumar Parida. "Modelling and Finite Element Based Analysis of a Five Fingered Underactuated Robotic Hand." International Journal for Research in Applied Science and Engineering Technology 10, no. 9 (September 30, 2022): 100–108. http://dx.doi.org/10.22214/ijraset.2022.46579.

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Abstract: Imparting the dexterity and autonomous competence to a robotic system is a significant burden in humanoid robotics, especially in the fields of industrial manufacturing, prosthetics, orthopedic rehabilitation, etc. Operating a humanoid hand requires a very innovative actuator and transmission system. The under-actuated concepts are proving to be a possible means of achieving extremely dexterous robotic hands without the need for diverse mechanical design. The main characteristics of an under-actuated robotic hand are that fewer actuators are required to operate it than the degrees of freedom. The under-actuated equivalent hand is significantly less expensive than the fully-actuated equivalent hand and remarkably reduces the complexity of the control system. The existing work dealt with the modeling and finite element-based analysis of an anthropomorphic underactuated robotic hand using five fingers including the thumb and palm with dexterity and with a total of twenty-one degrees of freedom.

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Vargas, Oscar, Omar Flor, and Carlos Toapanta. "Robotic hand design with linear actuators based on Toronto development." Athenea 1, no. 1 (September 26, 2020): 22–28. http://dx.doi.org/10.47460/athenea.v1i1.3.

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In this work, the design of a robotic hand with 7 degrees of freedom is presented that allows greater flexibility, achieving the usual actions performed by a normal hand. The work consists of a prototype designed with linear actuators and myoelectric sensor, following the mechanism of the University of Toronto for the management of functional phalanges. The design, construction description, components and recommendations for the elaboration of a flexible and useful robotic hand for amputee patients with a residual limb for the socket are presented. Keywords: Robotic hand, Degree of freedom, Toronto´s Mechanism, lineal actuator. References [1]W. Diane, J. Braza and M. Yacub, Essentials of Physical Medicine and Rehabilitation, 4th ed. Philadelphia: Walter R. Frontera and Julie K. Silver and Thomas D. Rizzo, 2020, pp. 651 - 657. [2]A. Heerschop, C. Van Der Sluis, E. Otten, & R.M. Bongers, Looking beyond proportional control: The relevance of mode switching in learning to operate multi-articulating myoelectric upper-limb prostheses, . Biomedical Signal Processing and Control, 2020, doi:10.1016/j.bspc.2019.101647. [3]L. Heisnam, B. Suthar, 20 DOF robotic hand for tele-operation: — Design, simulation, control and accuracy test with leap motion. 2016 International Conference on Robotics and Automation for Humanitarian Applications (RAHA), 2016, doi:10.1109/raha.2016.7931886. [4]Y. Mishima, R. Ozawa, Design of a robotic finger using series gear chain mechanisms. 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2014, doi:10.1109/iros.2014.6942961. [5]N. Dechev, W. Cleghorn, S. Naumann, Multi-segmented finger design of an experimental prosthetic hand,Proceedings of the Sixth National Applied Mechanisms & Robotics Conference, december 1999. [6]O. Flor, “Building a mobile robot,” Education for the future. Accessed on: December 29, 2019. [Online] Available: https://omarflor2014.wixsite.com/misitio. [7]Vargas, O., Flor,O., Suarez, F., Design of a robotic prototype of the hand and right forearm for prostheses, Universidad, Ciencia y Tecnología, 2019. [8]O. Vargas, O. Flor, F. Suarez, C. Chimbo, Construction and functional tests of a robotic prototype for human prostheses, Revista espirales, 2020. [9]P. PonPriya, E. Priya, Design and control of prosthetic hand using myoelectric signal. International Conference on Computing and Communications Technologies (ICCCT), 2017, doi:10.1109/iccct2.2017.7972314. [10]N. Bajaj, A. Spiers, A. Dollar, State of the Art in Artificial Wrists: A Review of Prosthetic and Robotic Wrist Design. IEEE Transactions on Robotics, 2019, doi:10.1109/tro.2018.2865890.

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Castiblanco, Paola Andrea, José Luis Ramirez, and Astrid Rubiano. "Smart Materials and Their Application in Robotic Hand Systems: A State of the Art." Indonesian Journal of Science and Technology 6, no. 2 (May 15, 2021): 401–26. http://dx.doi.org/10.17509/ijost.v6i2.35630.

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The use of soft robotics and smart materials for the design of devices that help the population in different tasks has gained a rising interest. Medicine is one of the fields where its implementation has shown significant advances. However, there are works related to applications, directed to the human body especially in replacement of devices for the upper limb. This document aims to explore the state of the art relating to the study of soft robotics, the implementation of smart materials, and the artificial muscles in the design or construction of hand prostheses or robotic devices analogous to the human hand.

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Дисертації з теми "Robotics hand"

Bullock, Ian Merrill. "Understanding Human Hand Functionality| Classification, Whole-Hand Usage, and Precision Manipulation." Thesis, Yale University, 2017. http://pqdtopen.proquest.com/#viewpdf?dispub=10584937.

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A better understanding of human hand functionality can help improve robotic and prosthetic hand capability, as well as having benefits for rehabilitation or device design. While the human hand has been studied extensively in various fields, fewer existing works study the human hand within frameworks which can be easily applied to robotic applications, or attempt to quantify complex human hand functionality in real-world environments or with tasks approaching real-world complexity. This dissertation presents a study of human hand functionality from the multiple angles of high level classification methods, whole-hand grasp usage, and precision manipulation, where a small object is repositioned in the fingertips.

Our manipulation classification work presents a motion-centric scheme which can be applied to any human or hand-based robotic manipulation task. Most previous classifications are domain specific and cannot easily be applied to both robotic and human tasks, or can only be applied to a certain subset of manipulation tasks. We present a number of criteria which can be used to describe manipulation tasks and understand differences in the hand functionality used. These criteria are then applied to a number of real world example tasks, including a description of how the classification state can change over time during a dynamic manipulation task.

Next, our study of real-world grasping contributes to an understanding of whole-hand usage. Using head mounted camera video from two housekeepers and two machinists, we analyze the grasps used in their natural work environments. By tagging both grasp state and objects involved, we can measure the prevalence of each grasp and also understand how the grasp is typically used. We then use the grasp-object relationships to select small sets of versatile grasps which can still handle a wide variety of objects, which are promising candidates for implementation in robotic or prosthetic manipulators.

Following the discussion of overall hand shapes, we then present a study of precision manipulation, or how people reposition small objects in the fingertips. Little prior work was found which experimentally measures human capabilities with a full multi-finger precision manipulation task. Our work reports the size and shape for the precision manipulation workspace, and finds that the overall workspace is small, but also has a certain axis along which more object movement is possible. We then show the effect of object size and the number of fingers used on the resulting workspace volume – an ideal object size range is determined, and it is shown that adding additional fingers will reduce workspace volume, likely due to the additional kinematic constraints. Using similar methods to our main precision manipulation investigation, but with a spherical object rolled in the fingertips, we also report the overall fingertip surface usage for two- and three-fingered manipulation, and show a shift in typical fingertip area used between the two and three finger cases.

The experimental precision manipulation data is then used to refine the design of an anthropomorphic precision manipulator. The human precision manipulation workspace is used to select suitable spring ratios for the robotic fingers, and the resulting hand is shown to achieve about half of the average human workspace, despite using only three actuators.

Overall, we investigate multiple aspects of human hand function, as well as constructing a new framework for analyzing human and robotic manipulation. This work contributes to an improved understanding of human grasp usage in real-world environments, as well as human precision manipulation workspace. We provide a demonstration of how some of the studied aspects of human hand function can be applied to anthropomorphic manipulator design, but we anticipate that the results will also be of interest in other fields, such as by helping to design devices matched to hand capabilities and typical usage, or providing inspiration for future methods to rehabilitate hand function.

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Ziesmer, Jacob Ames. "Reconfigurable End Effector Allowing For In-Hand Manipulation Without Finger Gaiting Or Regrasping." [Milwaukee, Wis.] : e-Publications@Marquette, 2009. http://epublications.marquette.edu/theses_open/2.

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Alshahid, Kuteiba. "Computer modelling of the human hand." Thesis, University of Sussex, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.316650.

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Olson, Stephanie T. "Human-Inspired Robotic Hand-Eye Coordination." Thesis, Florida Atlantic University, 2018. http://pqdtopen.proquest.com/#viewpdf?dispub=10928904.

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My thesis covers the design and fabrication of novel humanoid robotic eyes and the process of interfacing them with the industry robot, Baxter. The mechanism can reach a maximum saccade velocity comparable to that of human eyes. Unlike current robotic eye designs, these eyes have independent left-right and up-down gaze movements achieved using a servo and DC motor, respectively. A potentiometer and rotary encoder enable closed-loop control. An Arduino board and motor driver control the assembly. The motor requires a 12V power source, and all other components are powered through the Arduino from a PC.

Hand-eye coordination research influenced how the eyes were programmed to move relative to Baxter’s grippers. Different modes were coded to adjust eye movement based on the durability of what Baxter is handling. Tests were performed on a component level as well as on the full assembly to prove functionality.

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Vin, Jerry. "ROBOTIC FINGERSPELLING HAND FOR THE DEAF-BLIND." DigitalCommons@CalPoly, 2013. https://digitalcommons.calpoly.edu/theses/1100.

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Because communication has always been difficult for people who are deaf-blind, The Smith-Kettlewell Eye Research Institute (SKERI), in conjunction with the California Polytechnic State University Mechanical Engineering department, has commissioned the design, construction, testing, and programming of a robotic hand capable of performing basic fingerspelling to help bridge the communication gap. The hand parts were modeled using SolidWorks and fabricated using an Objet rapid prototyper. Its fingers are actuated by 11 Maxon motors, and its wrist is actuated by 2 Hitec servo motors. The motors are controlled by Texas Instruments L293D motor driver chips, ATtiny2313 slave microcontroller chips programmed to act as motor controllers, and a master ATmega644p microcontroller. The master controller communicates with a computer over a USB cable to receive sentences typed by a sighted user. The master controller then translates each letter into its corresponding hand gesture in the American Manual Alphabet and instructs each motor controller to move each finger joint into the proper position.

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Goettsch, Ulix. "Basis functions for use in direct calibration techniques to determine part-in-hand location /." Thesis, Connect to this title online; UW restricted, 2001. http://hdl.handle.net/1773/7147.

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Al-Gallaf, Ebrahim Abdulla. "Task space robot hand manipulation and optimal distribution of fingertip force functions." Thesis, University of Reading, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.387046.

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Christian, Matthew. "Improving Motor Skills of a Smart Prosthetic Hand by Deep Learning." Thesis, Tennessee State University, 2019. http://pqdtopen.proquest.com/#viewpdf?dispub=10979821.

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Medical science has made it possible to use prosthetic devices to restore the basic abilities needed to function in everyday life. Although robotic prosthetic hands can improve mobility over a simple hook prosthetic, the current state-of-the-art devices are still limited in their ability to grasp and hold objects as quickly and as accurately as the natural human hand. This project trains a deep learning neural network to control a robotic prosthetic hand in performing a grasping task.

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Pretlove, John. "Stereoscopic eye-in-hand active machine vision for real-time adaptive robot arm guidance." Thesis, University of Surrey, 1993. http://epubs.surrey.ac.uk/843230/.

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This thesis describes the design, development and implementation of a robot mounted active stereo vision system for adaptive robot arm guidance. This provides a very flexible and intelligent system that is able to react to uncertainty in a manufacturing environment. It is capable of tracking and determining the 3D position of an object so that the robot can move towards, and intercept, it. Such a system has particular applications in remotely controlled robot arms, typically working in hostile environments. The stereo vision system is designed on mechatronic principles and is modular, light-weight and uses state-of-the-art dc servo-motor technology. Based on visual information, it controls camera vergence and focus independently while making use of the flexibility of the robot for positioning. Calibration and modelling techniques have been developed to determine the geometry of the stereo vision system so that the 3D position of objects can be estimated from the 2D camera information. 3D position estimates are obtained by stereo triangulation. A method for obtaining a quantitative measure of the confidence of the 3D position estimate is presented which is a useful built-in error checking mechanism to reject false or poor 3D matches. A predictive gaze controller has been incorporated into the stereo head control system. This anticipates the relative 3D motion of the object to alleviate the effect of computational delays and ensures a smooth trajectory. Validation experiments have been undertaken with a Puma 562 industrial robot to show the functional integration of the camera system with the robot controller. The vision system is capable of tracking moving objects and the information this provides is used to update command information to the controller. The vision system has been shown to be in full control of the robot during a tracking and intercept duty cycle.

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Ray, Zachary J. "Hand Orientation Feedback for Grasped Object Slip Prevention with a Prosthetic Hand." University of Akron / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=akron1461181998.

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Книги з теми "Robotics hand"

1945-, Howell Anthony, ed. Hand-made machines. Cardiff, Wales?]: Z Productions, 2007.

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Corporation, Meridian, and United States. National Aeronautics and Space Administration., eds. Force reflecting hand controller for manipulator teleoperation. Alexandria, VA: Meridian Corporation, 1991.

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We, robot: Skywalker's hand, blade runners, Iron Man, slutbots, and how fiction became fact. Guilford, CT: Lyons Press, 2011.

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Driels, Morris. Adaptive control of direct drive dexterous robotic hand with bilateral tactile sensing. Monterey, Calif: Naval Postgraduate School, 1990.

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A, Goodale Melvyn, ed. Vision and action: The control of grasping. Norwood, N.J: Ablex Pub. Corp., 1990.

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Chaudhary, Ankit. Robust Hand Gesture Recognition for Robotic Hand Control. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-4798-5.

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Control theory of multi-fingered hands: A modelling and analytical-mechanics approach for dexterity and intelligence. London: Springer, 2008.

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Birglen, Lionel, Thierry Laliberté, and Clément Gosselin. Underactuated Robotic Hands. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77459-4.

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Birglen, Lionel. Underactuated robotic hands. Berlin: Springer, 2008.

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Birglen, Lionel. Underactuated robotic hands. Berlin: Springer, 2008.

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Частини книг з теми "Robotics hand"

Narayanan, Gokul, Joshua Amrith Raj, Abhinav Gandhi, Aditya A. Gupte, Adam J. Spiers, and Berk Calli. "Within-Hand Manipulation Planning and Control for Variable Friction Hands." In Experimental Robotics, 600–610. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-71151-1_53.

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Kessens, Chad C., and Jaydev P. Desai. "Compact Hand with Passive Grasping." In Experimental Robotics, 117–32. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23778-7_9.

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Liu, Bingchen, Li Jiang, and Shaowei Fan. "Hand Posture Reconstruction Through Task-Dependent Hand Synergies." In Intelligent Robotics and Applications, 14–24. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-89095-7_2.

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Hirzinger, G., J. Butterfaß, S. Knoch, and H. Liu. "DLR’s multisensory articulated hand." In Experimental Robotics V, 47–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 1998. http://dx.doi.org/10.1007/bfb0112949.

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Deshpande, Ashish D. "Humanlike Hand Mechanism." In Humanoid Robotics: A Reference, 1–18. Dordrecht: Springer Netherlands, 2017. http://dx.doi.org/10.1007/978-94-007-7194-9_88-1.

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Che, Demeng, and Wenzeng Zhang. "A Humanoid Robot Upper Limb System with Anthropomorphic Robot Hand: GCUA Hand II." In Social Robotics, 182–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-17248-9_19.

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Zhang, Chi, Wenzeng Zhang, Zhenguo Sun, and Qiang Chen. "HAG-SR Hand: Highly-Anthropomorphic-Grasping Under-Actuated Hand with Naturally Coupled States." In Social Robotics, 475–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34103-8_48.

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Odhner, Lael U., Raymond R. Ma, and Aaron M. Dollar. "Experiments in Underactuated In-Hand Manipulation." In Experimental Robotics, 27–40. Heidelberg: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-00065-7_3.

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Grebenstein, Markus. "The Awiwi Hand: An Artificial Hand for the DLR Hand Arm System." In Springer Tracts in Advanced Robotics, 65–130. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-03593-2_4.

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Song, Shuang, and Wenzeng Zhang. "PCSS Hand: An Underactuated Robotic Hand with a Novel Parallel-Coupled Switchable Self-adaptive Grasp." In Social Robotics, 481–91. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-47437-3_47.

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Тези доповідей конференцій з теми "Robotics hand"

Paturca, Sanda Victorinne, Miruna Petraru, Valeriu Bostan, Cosmin Karl Banica, and Vasile Plesca. "Robotics Laboratory - Developing a Robotic Hand Prosthesis." In 2020 International Symposium on Fundamentals of Electrical Engineering (ISFEE). IEEE, 2020. http://dx.doi.org/10.1109/isfee51261.2020.9756143.

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Reymundo, Alberto A., Elvin M. Munoz, Marcelo Navarro, Emir Vela, and Hermano Igo Krebs. "Hand rehabilitation using Soft-Robotics." In 2016 6th IEEE International Conference on Biomedical Robotics and Biomechatronics (BioRob). IEEE, 2016. http://dx.doi.org/10.1109/biorob.2016.7523708.

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Shimabukuro, Yuto, Shotaro Gushi, and Hiroki Higa. "Trial Development of a Robotic Hand Based on Soft Robotics." In 2022 IEEE 4th Global Conference on Life Sciences and Technologies (LifeTech). IEEE, 2022. http://dx.doi.org/10.1109/lifetech53646.2022.9754918.

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Liu, Jie, and Yuru Zhang. "Mapping human hand motion to dexterous robotic hand." In 2007 IEEE International Conference on Robotics and biomimetics (ROBIO). IEEE, 2007. http://dx.doi.org/10.1109/robio.2007.4522270.

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Mattar, Ebrahim. "Biomimetic Dexterous Hands: Human Like Multi-fingered Robotics Hand Control." In 2012 UKSim 14th International Conference on Computer Modelling and Simulation (UKSim). IEEE, 2012. http://dx.doi.org/10.1109/uksim.2012.35.

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Fiorini, Paolo. "Smart Hand For Manipulators." In Robotics and IECON '87 Conferences, edited by Abe Abramovich. SPIE, 1987. http://dx.doi.org/10.1117/12.943011.

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Xu, Jijie, Michael Y. Wang, Hong Wang, and Zexiang Li. "Force Analysis of Whole Hand Grasp by Multifingered Robotic Hand." In 2007 IEEE International Conference on Robotics and Automation. IEEE, 2007. http://dx.doi.org/10.1109/robot.2007.363789.

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Ma, Raymond R., Walter G. Bircher, and Aaron M. Dollar. "Toward robust, whole-hand caging manipulation with underactuated hands." In 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017. http://dx.doi.org/10.1109/icra.2017.7989158.

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Scarcia, Umberto, Roberto Meattini, and Claudio Melchiorri. "Mapping human hand fingertips motion to an anthropomorphic robotic hand." In 2017 IEEE International Conference on Robotics and Biomimetics (ROBIO). IEEE, 2017. http://dx.doi.org/10.1109/robio.2017.8324511.

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Ono, E., H. Ichijou, and N. Aisaka. "Robot hand for handling cloth." In Fifth International Conference on Advanced Robotics 'Robots in Unstructured Environments. IEEE, 1991. http://dx.doi.org/10.1109/icar.1991.240582.

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Звіти організацій з теми "Robotics hand"

Melchiorri, Claudio, and J. K. Salisbury. Exploiting the Redundancy of a Hand-Arm Robotic System. Fort Belvoir, VA: Defense Technical Information Center, October 1990. http://dx.doi.org/10.21236/ada241161.

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Allen, Peter. Intelligent Sensor-Based Manipulation with Robotic Hands. Fort Belvoir, VA: Defense Technical Information Center, December 1998. http://dx.doi.org/10.21236/ada357655.

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Driels, Morris R. Adaptive Control of Direct Drive Dexterous Robotic Hand with Bilateral Tactile Sensing. Fort Belvoir, VA: Defense Technical Information Center, December 1990. http://dx.doi.org/10.21236/ada233980.

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Iberall, Thea, and S. T. Venkataraman. Workshop on Dextrous Robot Hands: IEEE International Conference on Robotics and Automation. Held in Philadelphia, PA April 25-29, 1988. Fort Belvoir, VA: Defense Technical Information Center, April 1988. http://dx.doi.org/10.21236/ada203788.

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Adebayo, Oliver, Joanna Aldoori, William Allum, Noel Aruparayil, Abdul Badran, Jasmine Winter Beatty, Sanchita Bhatia, et al. Future of Surgery: Technology Enhanced Surgical Training: Report of the FOS:TEST Commission. The Royal College of Surgeons of England, August 2022. http://dx.doi.org/10.1308/fos2.2022.

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Анотація:

Over the past 50 years the capability of technology to improve surgical care has been realised and while surgical trainees and trainers strive to deliver care and train; the technological ‘solutions’ market continues to expand. However, there remains no coordinated process to assess these technologies. The FOS:TEST Report aimed to (1) define the current, unmet needs in surgical training, (2) assess the current evidence-base of technologies that may be beneficial to training and map these onto both the patient and trainee pathway and (3) make recommendations on the development, assessment, and adoption of novel surgical technologies. The FOS:TEST Commission was formed by the Association of Surgeons in Training (ASiT), The Royal College of Surgeons of England (RCS England) Robotics and Digital Surgery Group and representatives from all trainee specialty associations. Two national datasets provided by Health Education England were used to identify unmet surgical training needs through qualitative analysis against pre-defined coding frameworks. These unmet needs were prioritised at two virtual consensus hackathons and mapped to the patient and trainee pathway and the capabilities in practice (CiPs) framework. The commission received more than 120 evidence submissions from surgeons in training, consultant surgeons and training leaders. Following peer review, 32 were selected that covered a range of innovations. Contributors also highlighted several important key considerations, including the changing pedagogy of surgical training, the ethics and challenges of big data and machine learning, sustainability, and health economics. This summates to 7 Key Recommendations and 51 concluding statements. The FOS:TEST Commission was borne out of what is a pivotal point in the digital transformation of surgical training. Academic expertise and collaboration will be required to evaluate efficacy of any novel training solution. However, this must be coupled with pragmatic assessments of feasibility and cost to ensure that any intervention is scalable for national implementation. Currently, there is no replacement for hands-on operating. However, for future UK and ROI surgeons to stay relevant in a global market, our training methods must adapt. The Future of Surgery: Technology Enhanced Surgical Training Report provides a blueprint for how this can be achieved.

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Hand-assist, laparoscopic and robotic live donor nephrectomy – advantages and drawbacks of each technique. BJUI Knowledge, May 2017. http://dx.doi.org/10.18591/bjuik.0382.

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