• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.296-300
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• 1992

This paper deals with a dual mode control system design for the starching work robot. From the feature of this work, the robot has redundant degree of freedom. In this paper, we try to split the whole movement the robot into a gross motion part ai. a fine motion part so as to achieve a good tracking performance. The preview learning control is applied to the gross motion part. The validity of the dual mode control architecture is demonstrated.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.65-70
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• 2001

We consider a hyper-redundant system that consists of many uniform units. The hyper-redundant system has many degrees of freedom and it can accomplish various tasks. Applysing the reinforcement learning to the hyper-redundant system is very attractive because it is possible to acquire various behaviors for various tasks automatically. In this paper we present a new reinforcement learning algorithm "Q-learning with propagation of motion". The algorithm is designed for the multi-agent systems that have strong connections. The proposed algorithm needs only one small Q-table even for a large scale system. So using the proposed algorithm, it is possible for the hyper-redundant system to learn the effective behavior. In this algorithm, only one leader agent learns the own behavior using its local information and the motion of the leader is propagated to another agents with time delay. The reward of the leader agent is given by using the whole system information. And the effective behavior of the leader is learned and the effective behavior of the system is acquired. We apply the proposed algorithm to a snake-like hyper-redundant robot. The necessary condition of the system to be Markov decision process is discussed. And the computer simulation of learning the locomotion is demonstrated. From the simulation results we find that the task of the locomotion of the robot to the desired point is learned and the winding motion is acquired. We can conclude that our proposed system and our analysis of the condition, that the system is Markov decision process, is valid.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.31B no.9
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• pp.47-56
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• 1994

In this paper, we demonstrate how to design a redundant robot which is suitable for the multiple task execution without any constraints on the work space. The implementation is possible by the rigid connection of a cacro-robot and a micro-robot. A 5 d.o.f. articulated robor designed for commercial purpose is utilized as a micro-robot which can perform a general task with the appropriate adjustment of its base location. The base of a micro-robot is located at a suitable position by the macro-robot designed and implemented through this research. A task assigned to this redundant robot is performed mainly by the micro-robot. However, when the micro-robot cannot perform the task by itself or when the micro-robot has difficulties in performing the task, the coordination of the macro-robot is requited. To monitor the task execution efficiency of the micro-robot, we used the 'Manipulability Measure' as a cost function. The coordination between the two robots are verified both by the simulation and the experiment.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.168-173
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• 1991

A neural optimization network is proposed to control the redundant robot manipulators in an environment with the obstacle. The weightings of the network are adjusted by considering both the joint dexterity and the capability of collision avoidance of joint differential motion. The fuzzy rules are proposed to determine the capability of collision avoidance of each joint. To show the validities of the proposed method, computer simulation results are illustrated for the redundant robot of the planner type with three degrees of freedom.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.2
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• pp.130-139
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• 2002

In this paper, at first, we investigate existing algorithms for finding the minimum infinity-norm solution of consistent linear equations and then propose a new algorithm. The proposed algorithm is intended to includes the advantages of computational efficiency as well as geometric explicitness. As a practical application example, optimum trajectory planning for redundant robot manipulators is considered. Also, an efficient approach avoiding discontinuity in trajectory is proposed by resolving the non-uniqueness problem of minimum infinity-norm solution. To be specific, the proposed method for checking possible discontinuity does not need any other algorithms in checking the possibility of discontinuity while previous work needs specially designed checking courses. To show the usefulness of the proposed techniques, an example calculating minimum infinity-norm solution for comparing the computational efficiency as well as the trajectory planning for a redundant robot manipulator are included.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1136-1139
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• 1996

For on-line trajectory planning such as teleoperation it is desirable to keep good manipulability of the robot manipulators since the motion command is not given in advance. To keep good manipulability means the capability of moving any arbitrary directions of task space. An optimization process with different manipulability measures are performed and compared for a redundant robot system moving in 2-dimensional task space, and gives results that the conventional manipulability ellipsoid based on the Jacobian matrix is not good choice as far as the optimal direction of motion is concerned.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10b
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• pp.564-568
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• 1992

A neural optimization network is designed to solve the collsion-free inverse kinematics problem for redundant robot manipulators under the constraints of joint limits, maximum velocities and maximum accelerations. And the fuzzy rules are proposed to determine the weightings of neural optimization networks to avoid the collision between robot manipulator and obstacles. The inputs of fuzzy rules are the resultant distance, change of the distance and sum of the changes. And the output of fuzzy rules is defined as the capability of collision avoidance of joint differential motion. The weightings of neural optimization networks are adjusted according to the capability of collision avoidance of each joint. To show the validities of the proposed method computer simulation results are illustrated for the redundant robot with three degrees of freedom,

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.638-647
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• 1996

Robot manipulators for harvesting fruits must be controlled to track the desired path of end-effector to avoid obstacles under the consideration of collision free area and safety path. This paper presents a robot path control algorithm to secure a collision free area with the recognition of work environments. The flexible space, which does not damage fruits or branches of tree due to their flexibility and physical properties , extends the workspace. Now the task is to control robot path in the extended workspace with the consideration of collision avoidance and velocity limitation at the time of collision concurrently. The feasibility and effectiveness of the new algorithm for redundant manipulators were tested through simulations of a redundant manipulator for different joint velocities.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.401-401
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• 2000

A mobile manipulator-a serial connection of a mobile robot and a task robot is redundant by itself. Using its redundant freedom, a mobile manipulator can move in various modes, and perform dexterous tasks. An interesting question,

• 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.