• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.1
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• pp.331-339
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• 2016

This paper developed a home wellness robot platform to perform the roles in basic health care and life care in an aging society. A robotic platform and a sensory platform were implemented for an indoor wellness service. In the robotic platform, the precise mobility and the dexterous manipulation are not only developed in a symbiotic service-robot, but they also ensure the robot architecture of human friendliness. The mobile robot was made in the agile system, which consists of Omni-wheels. The manipulator was made in the anthropomorphic system to carry out dexterous handwork. In the sensing platform, RF tags and stereo camera were used for self and target localization. They were processed independently and cooperatively for accurate position and posture. The wellness robot platform was integrated in a real-time system. Finally, its good performance was confirmed through live indoor tests for health and life care.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2529-2551
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• 2022

Visual servoing (VS) based on the Kalman filter (KF) algorithm, as in the case of KF-based image-based visual servoing (IBVS) systems, suffers from three problems in uncalibrated environments: the perturbation noises of the robot system, error of noise statistics, and slow convergence. To solve these three problems, we use an IBVS based on KF, African vultures optimization algorithm enhanced extreme learning machine (AVOA-ELM), and fuzzy logic (FL) in this paper. Firstly, KF online estimation of the Jacobian matrix. We propose an AVOA-ELM error compensation model to compensate for the sub-optimal estimation of the KF to solve the problems of disturbance noises and noise statistics error. Next, an FL controller is designed for gain adaptation. This approach addresses the problem of the slow convergence of the IBVS system with the KF. Then, we propose a visual servoing scheme combining FL and KF-AVOA-ELM (FL-KF-AVOA-ELM). Finally, we verify the algorithm on the 6-DOF robotic manipulator PUMA 560. Compared with the existing methods, our algorithm can solve the three problems mentioned above without camera parameters, robot kinematics model, and target depth information. We also compared the proposed method with other KF-based IBVS methods under different disturbance noise environments. And the proposed method achieves the best results under the three evaluation metrics.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.186-192
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• 1999

This paper introduces a study of a method for the forward kinematic analysis, which finds the 6 DOF motions and velocities from the given six cylinder lengths in the Stewart platform. From the viewpoints of kinematics, the solution for the inverse kinematic is easily found by using the vectors of the links which are composed of the joint coordinates in base and plate frames, to act contrary to the serial manipulator, but forward kinematic is difficult because of the nonlinearity and complexity of the Stewart platform dynamic equation with the multi-solutions. Hence we, first in this study, introduce the linear estimator using the Luenberger's observer, and the estimator using the nonlinear measured model for the forward kinematic solutions. But it is difficult to find the parameter of the design for the estimation gain or to select the estimation gain and the constant steady state error exists. So this study suggests the estimator with the estimation gain to be learned by the neural network with the structure of multi-perceptron and the learning method using back propagation and shows the estimation performance using the simulation.