000 04624nam a22005895i 4500
001 978-3-319-26706-7
003 DE-He213
005 20200421111703.0
007 cr nn 008mamaa
008 160224s2016 gw | s |||| 0|eng d
020 _a9783319267067
_9978-3-319-26706-7
024 7 _a10.1007/978-3-319-26706-7
_2doi
050 4 _aQA76.9.U83
050 4 _aQA76.9.H85
072 7 _aUYZG
_2bicssc
072 7 _aCOM070000
_2bisacsh
082 0 4 _a005.437
_223
082 0 4 _a4.019
_223
245 1 0 _aHuman and Robot Hands
_h[electronic resource] :
_bSensorimotor Synergies to Bridge the Gap Between Neuroscience and Robotics /
_cedited by Matteo Bianchi, Alessandro Moscatelli.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXI, 283 p. 97 illus., 66 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Series on Touch and Haptic Systems,
_x2192-2977
505 0 _aIntroduction -- Part I: Neuroscience -- Dexterous Manipulation: From High-level Representation to Low-level Co-ordination of Digit Force and Position -- Digit Position and Force Synergies During Unconstrained Grasping -- The Motor Control of Hand Movements in the Human Brain: Toward the Definition of a Cortical Representation of Postural Synergies -- Synergy Control in Subcortical Circuitry: Insights from Neurophysiology -- Neuronal "Op-Amps" Implement Adaptive control in Biology and Robotics -- Sensorimotor Synergies:Fusion of Cutaneous Touch and Proprioception in the Perceived Hand Kinematics -- Part II: Robotics, Models and Sensing Tools -- From Soft to Adaptive Synergies:The PISA/IIT Softhand -- A Learn by Demonstration Approach for Closed-loop Robust Anthromorphic Grasp Planning -- Teleimpudance Control: Overview and Application -- Incremental Learning of Muscle Synergies:From Calibrating a Prothesis to Interacting with it -- How to Map Human Hand Synergies onto Robotic Hands Using the Syngrasp Matlab Toolbox -- Quasi-static Analysis of Synergistically Underactuated Robotic Hands in Grasp and Manipulation Tasks -- A simple Model of the Hand for the Analysis of Object Exploration -- Synergy-based Optimal Sensing Techniques for Hand Pose Reconstruction.
520 _aThis book looks at the common problems both human and robotic hands encounter when controlling the large number of joints, actuators and sensors required to efficiently perform motor tasks such as object exploration, manipulation and grasping. The authors adopt an integrated approach to explore the control of the hand based on sensorimotor synergies that can be applied in both neuroscience and robotics. Hand synergies are based on goal-directed, combined muscle and kinematic activation leading to a reduction of the dimensionality of the motor and sensory space, presenting a highly effective solution for the fast and simplified design of artificial systems. Presented in two parts, the first part, Neuroscience, provides the theoretical and experimental foundations to describe the synergistic organization of the human hand. The second part, Robotics, Models and Sensing Tools, exploits the framework of hand synergies to better control and design robotic hands and haptic/sensing systems/tools, using a reduced number of control inputs/sensors, with the goal of pushing their effectiveness close to the natural one. Human and Robot Hands provides a valuable reference for students, researchers and designers who are interested in the study and design of the artificial hand.
650 0 _aComputer science.
650 0 _aNeurosciences.
650 0 _aUser interfaces (Computer systems).
650 0 _aArtificial intelligence.
650 0 _aControl engineering.
650 0 _aRobotics.
650 0 _aMechatronics.
650 1 4 _aComputer Science.
650 2 4 _aUser Interfaces and Human Computer Interaction.
650 2 4 _aControl, Robotics, Mechatronics.
650 2 4 _aNeurosciences.
650 2 4 _aArtificial Intelligence (incl. Robotics).
700 1 _aBianchi, Matteo.
_eeditor.
700 1 _aMoscatelli, Alessandro.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319267050
830 0 _aSpringer Series on Touch and Haptic Systems,
_x2192-2977
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-26706-7
912 _aZDB-2-SCS
942 _cEBK
999 _c55126
_d55126