• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.124-126
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• 1989

Inverse kinematic problem is a crucial point for robot manipulator control. In this paper, to implement the Jacobian control technique we used the Hopfield(Tank)'s neural network. The states of neurons represent joint veocities, and the connection weights are determined from the current value of the Jacobian matrix. The network energy function is constructed so that its minimum corresponds to the minimum least square error. At each sampling time, connection weights and neuron states are updated according to current joint position. Inverse kinematic solution to the planar redundant manipulator is solved by computer simulation.

• Journal of the Korea Society of Computer and Information
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• v.14 no.8
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• pp.11-18
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• 2009

This paper proposes the way of improving learning speed in Levenberg-Marquardt algorithm using the principal submatrix of Jacobian matrix. The Levenberg-Marquardt learning uses Jacobian matrix for Hessian matrix to get the second derivative of an error function. To make the Jacobian matrix an invertible matrix. the Levenberg-Marquardt learning must increase or decrease ${\mu}$ and recalculate the inverse matrix of the Jacobian matrix due to these changes of ${\mu}$. Therefore, to have the proper ${\mu}$, we create the principal submatrix of Jacobian matrix and set the ${\mu}$ as the eigenvalues sum of the principal submatrix. which can make learning speed improve without calculating an additional inverse matrix. We also showed that our method was able to improve learning speed in both a generalized XOR problem and a handwritten digit recognition problem.

• The Journal of Korea Robotics Society
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• v.10 no.4
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• pp.230-244
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• 2015

Continuous-path motion control such as resolved motion rate control requires online solving of the inverse differential kinematics for a robot. However, the solution space of the inverse differential kinematics related to Jacobian J is not well-established. In this paper, the solution space of inverse differential kinematics is analyzed through categorization of mapping conditions between joint velocities and end-effector velocity of a robot. If end-effector velocity is within the column space of J, the solution or the minimum norm solution is obtained. If it is not within the column space of J, an approximate solution by least-squares is obtained. Moreover, this paper introduces an improved mapping diagram showing orthogonality and mapping clearly between subspaces, and concrete examples numerically showing the concept of several subspaces. Finally, a solver and graphics user interface (GUI) for inverse differential kinematics are developed using MATLAB, and the solution of inverse differential kinematics using the GUI is demonstrated for a vertically articulated robot.

• Bulletin of the Korean Mathematical Society
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• v.47 no.1
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• pp.211-219
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• 2010

Let P be a Jacobian polynomial such as deg P = $deg_y$ P. Suppose the Jacobian polynomial P satisfies the intersection condition satisfying $dim_C$ C[x, y]/$P_y$> = deg P - 1, we can prove that the Keller map which has P as one of coordinate polynomial always has its inverse.

• Proceedings of the KIEE Conference
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• 2001.11b
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• pp.327-329
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• 2001

This paper presents a fast and accurate contingency analysis in EHV systems for line outages, loss of generation of redispatching and loss-of-load or load management. Unlike other contingencies, line outage requires the modification of the Jacobian of the base case power flow and the calculation of its new inverse, which is substantially different from the original inverse. In this paper, we obtain the inverse of the new Jacobian from the original inverse without repeating the time consuming matrix inversion process. Numerical test results show the significant improvement in the accuracies compared with those obtained using the original inverse.

• Journal of the Korean Society of Industry Convergence
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• v.24 no.6_2
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• pp.953-959
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• 2021

In this paper, the numerical inverse kinematics analysis is presented for a collaborative robot with an offset wrist. Robot manipulators with offset wrist are widely used in industrial applications, due to many advantages over those with wrist center and those with three parallel axes such as simple mechanical design, light weight, and so on. There may not exist a closed-form solution for a robot manipulator with offset wrist. A simple numerical method is applied to solve the inverse kinematics with offset wrist. Singularity is analyzed using Jacobian matrix and the numerical inverse kinematics algorithm is implemented on the real-time controller.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.1820-1823
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• 1997

For the Cartesian space position controlled robot, it is required to have the accurate mapping from the Cartesian space to the joint space in order to command the desired joint trajectories correctly. since the actual mapping from Cartesian space to joint space is obtained at the joint coordinate not at the actuator coordinate, uncertainty in Jacobian can be present. In this paper, two feasible neural network schemes are proposed to compensate for the kinematic Jacobian uncertainties. Uncertainties in Jacobian can be compensated by identifying either actuator Jacobian off-line or the inverse of that in on-line fashion. the case study of the stenciling robot is examined.

• Journal of the Korean Data and Information Science Society
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• v.23 no.5
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• pp.1027-1035
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• 2012

This paper proposes a new Levenberg-Marquardt algorithm that is accelerated by adjusting a Jacobian matrix and a quasi-Hessian matrix. The proposed method partitions the Jacobian matrix into block matrices and employs the inverse of a partitioned matrix to find the inverse of the quasi-Hessian matrix. Our method can avoid expensive operations and save memory in calculating the inverse of the quasi-Hessian matrix. It can shorten the training time for fast convergence. In our results tested in a large application, we were able to save about 20% of the training time than other algorithms.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.4
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• pp.295-300
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• 1990

Inverse kinematic problem is a crucial point for robot manipulator control. In this paper, to implement the Jacobian control technique we used the Hopfield, Tank's neural network. The states of neurons represent joint velocities, and the connection weights are determined from the current value of the Jacobian matirx. The network energy function is constructed so that its minimum corresponds to the minimum least square error. At each sampling time, connection weights and neuron states are updated according to current joint positon. Inverse kinematic solution to the planar redundant manipulator is solved by computer simulation.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.3
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• pp.97-104
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• 2003

This paper proposes a new parallel manipulator fur plane motion, and then discusses on the workspace and kinematical characteristics of the manipulator. The conventional planar parallel manipulators have some disadvantages which are complex non-closed type direct kinematics, workspaces containing useless voids, and concave type border tines of workspaces. The proposed planar parallel manipulator overcomes the above disadvantages, that is, the manipulator has simple closed type direct kinematics, a void-free workspace, and a convex type borderline of a workspace. This paper shows the simulation result of the workspace as well as performances indices using a homogeneous inverse Jacobian.