• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.1093-1098
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• 2003

In this paper, a practical methodology of hand-manipulator motion coordination for indoor service robot is introduced. This paper describes the procedures of opening door performed by service robot as a noticeable example of motion coordination. This paper presents well-structured framework for hand-manipulator motion coordination, which includes intelligent sensor data interpretation, object shape estimation, optimal grasping, on-line motion planning and behavior-based task execution. This proposed approach is focused on how to integrate the respective functions in harmony and enable the robot to complete its operation under the limitation of usable resources. As a practical example of implementation, the successful experimental results in opening door whose geometric parameters are unknown beforehand are provided.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.124-129
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• 1992

The goal of the proposed Intelligent Assisting System - IAS is to assist human operators in an intelligent way, while leaving decision and goal planning instances for the human. To realize the IAS the very important issue of manipulation skill identification and analysis has to be solved, which then is stored in a Skill Data Base. Using this data base the IAS is able to perform complex manipulations on the motion control level and to assist the human operator flexibly. We propose a model for manipulation skill based on the dynamics of the grip transformation matrix, which describes the dynamic transformation between object space and finger joint space. Interaction with a virtual world simulator allows the calculation and feedback of appropriate forces through controlled actuators of the sensor glove with 10 degrees-of-freedom. To solve the sensor glove calibration problem, we learn the nonlinear calibration mapping by an artificial neural network(ANN). In this paper we also describe the experimental system setup of the skill acquisition and transfer system as a first approach to the IAS. Some simple manipulation examples and simulation results show the feasibility of the proposed manipulation skill model.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.4
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• pp.756-765
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• 2001

An effective dexterous motion control method of redundant robot manipulators based on neural optimization network is proposed to satisfy multi-criteria such as singularity avoidance, minimizing energy consumption, and avoiding physical limits of actuator, while performing a given task. The method employs a neural optimization network with parallel processing capability, where only a simple geometric analysis for resolved motion of each joint is required instead of computing of the Jacobian and its pseudo inverse matrix. For dexterous motion, a joint geometric manipulability measure(JGMM) is proposed. JGMM evaluates a contribution of each joint differential motion in enlarging the length of the shortest axis among principal axes of the manipulability ellipsoid volume approximately obtained by a geometric analysis. Redundant robot manipulators is then controlled by neural optimization networks in such a way that 1) linear combination of the resolved motion by each joint differential motion should be equal to the desired velocity, 2) physical limits of joints are not violated, and 3) weighted sum of the square of each differential joint motion is minimized where weightings are adjusted by JGMM. To show the validity of the proposed method, several numerical examples are illustrated.