• The Journal of Korea Robotics Society
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• v.10 no.4
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• pp.193-199
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• 2015

This paper dealt with a pincers-type gripper being able to grip a heavy-weighted cylindrical object having various size with itself. This gripper should be designed to seize the objects without any change of jaw shape. Grasping achieved equilibrium after the object slipped on the jaw while grasping it. To cope with this situation, we suggested the slip considered gripper design procedure based on grasping equilibrium. The obtained slip condition can provide a limit friction coefficient depending on the contact angle when initiating contact between jaw and object. Consequently, the gripping force and the required actuating force can be calculated. In order to verify the proposed slip condition, the simulations were performed using a dynamic software.

• The Journal of Korea Robotics Society
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• v.14 no.2
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• pp.94-103
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• 2019

The optimal grasping point of the object varies depending on the shape of the object, such as the weight, the material, the grasping contact with the robot hand, and the grasping force. In order to derive the optimal grasping points for each object by a three fingered robot hand, optimal point and posture have been derived based on the geometry of the object and the hand using the artificial neural network. The optimal grasping cost function has been derived by constructing the cost function based on the probability density function of the normal distribution. Considering the characteristics of the object and the robot hand, the optimum height and width have been set to grasp the object by the robot hand. The resultant force between the contact area of the robot finger and the object has been estimated from the grasping force of the robot finger and the gravitational force of the object. In addition to these, the geometrical and gravitational center points of the object have been considered in obtaining the optimum grasping position of the robot finger and the object using the artificial neural network. To show the effectiveness of the proposed algorithm, the friction cone for the stable grasping operation has been modeled through the grasping experiments.

• The Journal of Korea Robotics Society
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• v.16 no.2
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• pp.130-136
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• 2021

In recent years, much study has been conducted in robotic grasping. The grasping algorithms based on deep learning have shown better grasping performance than the traditional ones. However, deep learning-based algorithms require a lot of data and time for training. In this study, a grasping algorithm using an artificial neural network-based graspability estimator is proposed. This graspability estimator can be trained with a small number of data by using a neural network based on the residual blocks and point clouds containing the shapes of objects, not RGB images containing various features. The trained graspability estimator can measures graspability of objects and choose the best one to grasp. It was experimentally shown that the proposed algorithm has a success rate of 90% and a cycle time of 12 sec for one grasp, which indicates that it is an efficient grasping algorithm.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.16-22
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• 2024

Robotic grasping in unstructured environments poses a significant challenge, demanding precise estimation of gripping positions for diverse and unknown objects. Generative Grasping Convolution Neural Network (GG-CNN) can estimate the position and direction that can be gripped by a robot gripper for an unknown object based on a three-dimensional depth map. Since GG-CNN uses only a depth map as an input, the precision of the depth map is the most critical factor affecting the result. To address the challenge of depth map precision, we integrate the Segment Anything Model renowned for its robust zero-shot performance across various segmentation tasks. We adjust the components corresponding to the segmented areas in the depth map aligned through external calibration. The proposed method was validated on the Cornell dataset and SurgicalKit dataset. Quantitative analysis compared to existing methods showed a 49.8% improvement with the dataset including surgical instruments. The results highlight the practical importance of our approach, especially in scenarios involving thin and metallic objects.

• 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.

• 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.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.712-715
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• 1997

In image jacobian based visual servoing, generally, inverse jacobian should be calculated by complicated coordinate transformations. These are required excessive computation and the singularity of the image jacobian should be considered. This paper presents a visual servoing to control the pose of the robotic manipulator for tracking and grasping 3-D moving object whose pose and motion parameters are unknown. Because the object is in motion tracking and grasping must be done on-line and the controller must have continuous learning ability. In order to estimate parameters of a moving object we use the kalman filter. And for tracking and grasping a moving object we use a fuzzy inference based reinforcement learning algorithm of dynamic recurrent neural networks. Computer simulation results are presented to demonstrate the performance of this visual servoing

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.58-64
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• 2024

The ability of the robot gripper to handle a wide range of objects significantly impacts its operational effectiveness. Among the robot grippers commonly used, the economically feasible choice is the relatively simple structure of a parallel gripper. To perform more densely packed tasks with a parallel gripper, it should be capable of handling small objects. Therefore, this study designs a parallel gripper mechanism equipped with assisting grippers to ensure smooth grasping of small objects. The parallel gripper is designed using a rack and pinion gear system, with two additional grippers on both side, and these assisting grippers are designed to be detachable. The two assisting grippers have different type of tip to grasp thin fabric shapes and thin stick shapes. The gripper prototype is used to verify the grasping capabilities for shapes achievable with a conventional parallel gripper and those intended for grasping with the assisting grippers through grasping experiments. Consequently, by equipping a conventional parallel gripper with assisting grippers as in this study, it becomes capable of handling a broader range of objects, in addition to its existing functionality.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.62-69
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• 2008

A specialized anthropomorphic robot hand which can be attached to the biped humanoid robot MAHRU-R in KIST, has been developed. This built-in type hand consists of three fingers and a thumb with total four DOF(Degrees of Freedom) where the finger mechanism is well designed for grasping typical objects stably in human's daily activities such as sphere and cylinder shaped objects. The restriction of possible motions and the limitation of grasping objects arising from the reduction of DOF can be overcome by reflecting a typical human finger's motion profile to the design procedure. As a result, the developed hand can imitate not only human hand's shape but also its motion in a compact and efficient manner. Also this novel robot hand can perform various human hand gestures naturally and grasp normal objects with both power and precision grasping capability.

• Journal of Institute of Control, Robotics and Systems
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• v.16 no.9
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• pp.876-882
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• 2010

Stroke patients can't use their hands because of the paralysis their fingers. Their fingers are recovered by rehabilitating training, and the rehabilitating extent can be judged by measuring the pressing force to be contacted with two fingers (thumb and first finger, thumb and middle finger, thumb and ring finger, thumb and little finger). But, at present, the grasping finger force of two-finger can't be accurately measured, because there is not a proper finger-force measuring system. Therefore, doctors can't correctly judge the rehabilitating extent. So, the finger-force measuring system which can measure the grasping force of two-finger must be developed. In this paper, the finger-force measuring system with a three-axis force sensor which can measure the pressing force was developed. The three-axis force sensor was designed and fabricated, and the force measuring device was designed and manufactured using DSP (Digital Signal Processing). Also, the grasping force test of men was performed using the developed finger-force measuring system, it was confirmed that the grasping forces of men were different according to grasping methods.