• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.517-522
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• 1991

In this paper, robot's grasping poses are classified into three cases, and force/torque information in each grasping pose is analyzed. In the grasping process, error between the desired and the actual grasping poses can be generated because of uncertainty in the environment. A systematic algorithm is presented, that uses the force/torque information generated by grasping pose error to estimate robot's actual grasping pose.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.455-458
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• 1988

In this paper, a grasping force control of robot hand is presented with consideration of workpiece dynamics. It is difficult to control a manipulator without the knowledge of Its dynamics, because its handling with unknown workpiece may bring about overshoots and vibrations. Then it is necessary to adjust control gains according to the handling workpieces in order to achieve good control performances. The authors propose a new control strategy which uses adaptive observer and VSS (${\underline{V}}ariable$ ${\underline{S}}tructure$ ${\underline{S}}ystem$) controller to subdue these overshoots and vibrations. Some simulations of the proposed method are carried out for a grasping system to control the grasping force to various workpieces, whose dynamics cannot be known in advance.

• Journal of Advanced Marine Engineering and Technology
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• v.30 no.3
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• pp.428-437
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• 2006

This paper attempts to derive and analyze the dynamic system of grasping a rigid object by means of two multi-degrees-of-freedom robot flngers with soft and deformable tips. It is shown firstly that a set of differential equation describing dynamics system of the manipulators and object together with geometric constraint of tight area-contacts is formulated by Lagrange's equation. It is shown secondly that the problems of controlling both the forces of pressing object and the rotation angle of the object under the geometric constraints are discussed. In this paper. the control method for dynamic stable grasping and enhancing dexterity in manipulating things is proposed. It is illustrated by computer simulation that the control system gives the performance improvement in the dynamic stable grasping of the dual fingers robot with soft tips.

• International Journal of Control, Automation, and Systems
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• v.4 no.5
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• pp.592-600
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• 2006

The pattern recognition of the complicated grasping operation based on electroencephalography (simply named as EEG) is very helpful on realtime control of the robotic hand. In the paper, a new spectral feature analysis method based on Band Pass Filter (simply named as BPF) and Power Spectral Analysis (simply named as PSA) is presented for discriminating the complicated grasping operations. By analyzing the spectral features of grasping operations with the use of the two-channel EEG measurement system and the pattern recognition of the BP neural network, the degree of recognition by the traditional spectral feature method based on FFT and the new spectral features method based on BPF and PSA could be compared. The results show that the proposed method provides highly improved performance than the traditional one because the new method has two obvious advantages such as high recognition capability and the fast learning speed.

• Journal of the Korean Society of Industry Convergence
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• v.18 no.3
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• pp.167-172
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• 2015

In this paper, we propose a new approach to design and control a smart gripper of robot system. A control method for flexible grasping a object in partially unknown environment was proposed, where a proximate sensor detecting the distance between the fingertip and object was used. Based on the proximate sensor signal the finger motion controller could plan the grasping process divided in three phases. The first step is scanning process which two first joints were moved to mid-position of the detected range by a state-variable feedback position controller, after the scanning was finished. The contact force of fingertip was then controlled using the detection sensor of the servo controller for finger joint control. The proposed grasping planning was tested on rectangular bar.

• Journal of the Korean Society for Precision Engineering
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• v.30 no.3
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• pp.300-307
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• 2013

This paper describes the development of a cylindrical-object grasping force measuring system applied haptic technology to measure the grasping force of strokes patients' fingers and other patients' paralyzed fingers. Because the cylindrical-object and the force measuring device of the developed cylindrical-object grasping force measuring system are connected with the electrical wires, patients and their families have difficulty not only measuring the patients' grasping force using the system but also knowing their rehabilitation extent when using it. In this paper, the cylindrical-object grasping force measuring system applied haptic technology was developed, and the cylindrical-object grasping force measuring device sends data to the rehabilitation evaluating system applied haptic technology by wireless communication. The grasping force measurement characteristic test using the system was carried out, and it was confirmed that the rehabilitation extent of the patients' paralyzed fingers and normal people fingers can be evaluated.

• Journal of Institute of Control, Robotics and Systems
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• v.4 no.2
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• pp.187-193
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• 1998

This paper proposes an advanced visual tracking algorithm for the stable grasping of a moving target(2D). This algorithm is programmed to find grasping points of an unknown polygonal object and execute visual tracking. The Kalman Filter(KF) algorithm based on the SVD(Singular Value Decomposition) is applied to the visual tracking system for the tracking of a moving object. The KF based on the SVD improves the accuracy of the tracking and the robustness in the estimation of state variables and noise statistics. In addition, it does not have the numerical unstability problem that can occur in the visual tracking system based on Kalman filter. In the grasping system, a parameterized family is constructcd, and through the family, the grasping system finds the stable grasping points of an unknown object through the geometric properties of the parameterized family. In the previous studies, many researchers have been studied on only 'How to track a moving target'. This paper concern not only on 'how to track' but also 'how to grasp' and apply the grasping theory to a visual tracking system.

• Journal of Institute of Control, Robotics and Systems
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• v.19 no.12
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• pp.1160-1166
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• 2013

This paper presents feedforward controllers to improve the control performance of the motion and grasping force of a surgical instrument used in an MIS (Minimally Invasive Surgery) robot. The surgical instrument has a long distance between the drive motors and its active joints. Therefore, the gripper on the instrument is controlled by a cable drive mechanism, which generates a coupled motion between the wrist joint and the grip direction. In order to solve the problem, this paper analyzes the pulley composition of the surgical instrument and proposes feedforward controllers to eliminate the coupled motion. Furthermore, feedforward controllers to regulate the grasping force are proposed to deal with another coupling problem between the grasping force of the instrument and the motion of the instrument joints. The experimental results demonstrate the improved control performance of the motion and grasping force of the instrument.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.2
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• pp.127-134
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• 2004

In this paper, human motor behaving model based sensory motor coordination(SMC) algorithm is implemented on robotic grasping task. Compare to conventional SMC models which connect sensor to motor directly, the proposed method used biologically inspired human behaving system in conjunction with SMC algorithm for fast grasping force control of robot arm. To characterize various grasping objects, pressure sensors on hand gripper were used. Measured sensory data are simultaneously transferred to perceptual mechanism(PM) and long term memory(LTM), and then the sensory information is forwarded to the fastest channel among several information-processing flows in human motor system. In this model, two motor learning routes are proposed. One of the route uses PM and the other uses short term memory(STM) and LTM structure. Through motor learning procedure, successful information is transferred from STM to LTM. Also, LTM data are used for next moor plan as reference information. STM is designed to single layered perception neural network to generate fast motor plan and receive required data which comes from LTM. Experimental results showed that proposed method can control of the grasping force adaptable to various shapes and types of greasing objects, and also it showed quicker grasping-behavior lumining time compare to simple feedback system.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.1 s.94
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• pp.42-48
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• 1999

A control method for a robot hand grasping a object in a partially unknown environment will be proposed, where a proximate sensor detecting the distance between the fingertip and object was used. Particularly, the finger joints were driven servo-pneumatically in this study. Based on the proximate sensor signal the finger motion controller could plan the grasping process divided in three phases ; fast aproach, slow transitional contact and contact force control. That is, the fingertip approached to the object with full speed, until the output signal of the proximate sensor began to change. Within the perating range of the proximate sensor, the finger joint was moved by a state-variable feedback position controller in order to obtain a smooth contact with the object. The contact force of fingertip was then controlled using the blocked-line pressure sensitivity of the flow control servovalve for finger joint control. In this way, the grasping impact could be reduced without reducing the object approaching speed. The performance of the proposed grasping method was experimentally compared with that of a open loop-controlled one.