Identification and hybrid impedance control of human skin muscle by multi-fingered robot hand

Mouri, Keisuke; Terashima, Kazuhiko; Minyong, Panya; Kitagawa, Hideo; Miyoshi, Takanori

doi:10.1109/iros.2007.4399308

Cited by 33 publications

(22 citation statements)

References 7 publications

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“…Existing massage robot's researches mainly focus on the trajectory reproduction of massagist technique and the changing force curve [1][2][3][4][5][6][7]. Robotic massage is implemented by a redundant manipulator equipped with a tactile sensor, in which the hybrid impedance control is adopted [8][9][10].…”

Section: ⃝Icros Kiee and Springer 2016mentioning

confidence: 99%

Strategies for feet massage robot to position the pelma acupoints with model predictive and real-time optimization

Wei

Gu²,

Xu-jing

et al. 2016

Int. J. Control Autom. Syst.

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It is one of critical factors for a massage robot to find and position the acupuncture point precisely in order to cure the diseases. Based on large amount of sample data offline, Chinese medical empirical knowledge is also introduced to build the prediction model. The massagist prescription and the robot mechanism devise are both considered for robot positioning. Least squares method is of simplicity, easy to use and high efficiency. Its real-time calculation is very effective, too. A modeling method for robot positioning is proposed based on least squares. Knowledge consultation is set for the calculation of acupoint position. The robot needs to get the feature points of a foot to be massaged. The foot contour sampling data are divided into piecewise curve fitting. Qlearning is adopted to optimize the robot positioning for they are model free. CMAC (Cerebellar Model Articulation Controller) cerebellum model is incorporated into the function approximation of Q learning. The learning system is rewarded by referring to the strengths of instrumental signal. By the direct representation, the model of human pelma acupoint is expressed with the vector variables and formal computer language. Through prediction model's calculation, the robot will work out the rough position of acupuncture point. Meanwhile, Q learning does the online adjustment for accurate location. These strategies provide for the robot to automatically search and position the pelma acupoint with little real-time computation and storage. The idea of this paper also prompts a research cue for the development of Chinese medical standardization.

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Section: ⃝Icros Kiee and Springer 2016mentioning

confidence: 99%

Strategies for feet massage robot to position the pelma acupoints with model predictive and real-time optimization

Wei

Gu²,

Xu-jing

et al. 2016

Int. J. Control Autom. Syst.

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“…Research efforts were recently presented to control a dexterous multifingered hand during deformable object manipulation using force/tactile feedback. In [7,8], Minyong et al collected and used force/tactile data to imitate the movements of the human hand. Other research directions address the resolution of the interaction control problem using multisensory feedback.…”

Section: Handling Deformable Objectsmentioning

confidence: 99%

Integrated Multisensory Robotic Hand System for Deformable Object Manipulation

Khalil¹,

Payeur²,

Creţu

2010

IASTED Technology Conferences / 705: ARP / 706: RA / 707: NANA / 728: CompBIO

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Designing a dexterous robotic hand able to interact intelligently with deformable objects constitutes a challenging area of research where many issues are yet to be solved. The complexity of such interactions requires the assistance of intelligent multisensory robotic systems that combine measurements collected from different sensors in order to accurately plan for the forces to be applied on the deformable object. This paper presents the development of a real-time multisensory robotic hand platform that incorporates live measurements of its internal position, velocity and force parameters along with data from external tactile sensors and a stereoscopic vision device. The resulting prototype of the integrated multisensory system is validated experimentally by the computation of deformable object models in which the measurements are merged. A formal dynamic model is discussed and a neural network representation model is presented. The results demonstrate the performance and suitability of the multisensory platform for the development of enhanced robotic hand capabilities when manipulating deformable objects.

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“…Hybrid forceposition control is widely used to handle gripping or grasping of objects 27 ; the control approach introduces two states 28,29 . The first state is controlling the positioning error which is also known as controlling an unconstrained mode while the second state is providing force control in a particular direction.…”

Section: Introductionmentioning

confidence: 99%

“…Switching actions may be uncertain and cause instability 30 . More recent solutions have resolved this in a geometric approach, where the directionality expressed by the kinematics Jacobian defines the directions for position and force control 14,27,30 . Directional force control approaches are ideal in industrial applications 29 , but may be generally problematic in scenarios with humanoid robot hands, where the environment is uncertain and multidirectional (although specific exceptions of directional compliance control in robot hands exist 27 ).…”

Section: Introductionmentioning

confidence: 99%

Robotic hand posture and compliant grasping control using operational space and integral sliding mode control

Herrmann¹,

Jalani

Mahyuddin

et al. 2014

Robotica

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SUMMARYThis paper establishes a novel approach of robotic hand posture and grasping control. For this purpose, the control uses the operational space approach. This permits the consideration of the shape of the object to be grasped. Thus, the control is split into a task control and a particular optimizing posture control. The task controller employs Cylindrical and Spherical coordinate systems due to their simplicity and geometric suitability. This is achieved by using an integral sliding mode controller (ISMC) as task controller. The ISMC allows us to introduce a model reference approach where a virtual mass-spring-damper system can be used to design a compliant trajectory tracking controller. The optimizing posture controller together with the task controller creates a simple approach to obtain pre-grasping/object approach hand postures. The experimental results show that target trajectories can be easily followed by the task control despite the presence of friction and stiction. When the object is grasped, the compliant control will automatically adjust to a specific compliance level due to an augmented compliance parameter adjustment algorithm. Once a specific compliance model has been achieved, the fixed compliance controller can be tested for a specific object grasp scenario. The experimental results prove that the Bristol Elumotion robot hand (BERUL) can automatically and successfully attain different compliance levels for a particular object via the ISMC.

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Identification and hybrid impedance control of human skin muscle by multi-fingered robot hand

Cited by 33 publications

References 7 publications

Strategies for feet massage robot to position the pelma acupoints with model predictive and real-time optimization

Strategies for feet massage robot to position the pelma acupoints with model predictive and real-time optimization

Integrated Multisensory Robotic Hand System for Deformable Object Manipulation

Robotic hand posture and compliant grasping control using operational space and integral sliding mode control

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