• IEMEK Journal of Embedded Systems and Applications
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• v.2 no.4
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• pp.221-226
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• 2007

This paper propose a control network system based on IEEE1394 for a multi body robot control. The IEEE1394 has the characteristic of high speed(400Mbps), real-time, stability and plug&play. And IEEE1394 also supports freeform daisy chaining, branching and hot plugging, which reduce cabling complexity and make a system simple. Especially, multi host and broad casting support network data sharing method which is suitable for control network for multi body robot. Through experiment, we show that the proposed control network can interface 48 joints (BLDC motors, gears, and encoders) and four 6-axis force/torque sensors with 4Khz communication bandwidth, which is adequate for a multi body robot.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.632-637
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• 1992

Recently many dynamics control algorithms using robot dynamic equation have been proposed. One of them, Kawato's feedback error learning scheme requires neither an accurate model nor parameter estimation and makes the robot motion closer to the desired trajectory by repeating operation. In this paper, the feedback error learning algorithm is implemented to control a robot system, 5 DOF revolute type movemaster. For this purpose, an actuator dynamic model is constructed considering equivalent robot dynamics model with respect to actuator as well as friction model. The command input acquired from the actuator dynamic model is the sum of products of unknown parameters and known functions. To compute the control algorithm, a parallel processing computer, transputer, is used and real-time computing is achieved. The experiment is done for the three major link of movemaster and its result is presented.

• 제어로봇시스템학회:학술대회논문집
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• 1992.10a
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• pp.387-392
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• 1992

This paper presents a local trajectory generation method which is based on a sequence of reference posture-velocities and the efficient low level control algorithm which constructs the complete smooth curve from the trajectory specification. The reference trajectory generator(RTG) which is in between the local path planner(LPP) and the robot motion controller(RMC) generates a sequence of set-points for each path segments from the LPP and pass it to the RMC. The RMC controls the motions of vehicle which should follow the sequence. In the feedback controller of VMC, the method which compensates robot posture-velocity error correctly is used. These methods are implemented on indoor autonomous vehicle, 'ALIVE' mobile robot. The ALIVE mobile robot system is implemented on the 32bit VME bus system: the two VME CPU's are used for RTG and RMC, while the 80C196KC-based VME board is used for motor controller.

• Proceedings of the IEEK Conference
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• 2000.06e
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• pp.159-162
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• 2000

This paper addresses the design and implementation problem of the mobile robot with the synchronous driving mechanism that consists of modular control systems based on the Universal Serial Bus (USB). Recently, the USB have attracted the hardware developers'interests due to its low cost, compatibility, and extenability. In particular, the USB enables us to organize the whole system in the modular manner very easily, and this property plays a very important role in shortening the developing time in implementing the target system, for example, the mobile robot system. In this paper, we implement the USB motion controller and the USB ultrasonic sensor system and verified the validity and the effectiveness of the proposed system through the real experiments including the mobile robot navigation and the environment recognition.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.478-478
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• 2000

In this paper, presents the design of mobile field robot controlled by microcomputer. The computer works as the supervisory system for decision making and sending command to control the field robot. An information from encoder, infrared sensors using for control task of the robot that can be interconnected with the computer through the serial communication RS422 standard. In this case, the user can design the program algorithm base on microcomputer in order to control and define the routing of the mobile robot. Moreover, the robot can be installed other equipment fer collect data such as picture, sound, temperature etc., in order to working in the dangerous area.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.415-415
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• 2000

The functions of a mobile robot such as obstacle knowledge and collision avoidance for in-door cleaning are necessary features, as has been much studied in the field of industrial automatic guided vehicle or general mobile robot. A mobile robot, in order to avoid collision with obstacles, has to gather data with environment knowledge sensors and recognize environment and the shape of obstacles from the data. In the study, a wall-following algorithm was suggested as a autonomous moving algorithm in which a mobile robot can recognize obstacles in indoor like environment and do cleaning work in effect. The system suggested in the study is for cleaning of nuclear material dusts generated in the process of nuclear fuel manufacturing and decontamination of devices in disorder which is performed in M6 radioactive ray shield hot-cell in IMEF(Irradiated Material Examination Facility) in the Korea Atomic Energy Research Institute.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.400-400
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• 2000

This paper purposed that verify the validity of Petri Net method for control progressive increase of system complexity, before extend the realized single robot system to multi-robot system. An autonomous mobile robot(AMR) needs decision making, motion control, path planning, tracking a path, obstacle avoidance, and sensor fusion, to complete its task. An AMR integrates and operates these technics through a consistent command system. An error in a command hierarchy which is like duplication or omission of a control command hierarchy for each module results in serious problems. This paper minimizes the error by modeling each module and whole system using Petri Net graphical representation and applies it to the exploration task of an AMR

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.256-256
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• 2000

In this parer, we present a real-time path planning algorithm which is integrated the artificial potential field(APF) and simulated annealing(SA) methods for mobile robot. The APF method in path planning has gained popularity since 1990's. It doesn't need the modeling of the complex configuration space of robot, and is easy to apply the path planning with simple computation. However, there is a major problem with APF method. It is the formation of local minima that can trap the robot before reaching its goal. So, to provide local minima recovery, we apply the SA method. The effectiveness of the proposed algorithm is verified through simulation.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.279-279
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• 2000

In this paper, we have proposed a new approach to the design of robot vision system to develop the technology for the automatic test and assembling of precision mechanical and electronic parts fur the factory automation. In order to perform real time implementation of the automatic assembling tasks in the complex processes, we have developed an intelligent control algorithm based-on neural networks control theory to enhance the precise motion control. Implementing of the automatic test tasks has been performed by the real-time vision algorithm based-on TMS320C31 DSPs. It distinguishes correctly the difference between the acceptable and unacceptable defective item through pattern recognition of parts by the developed vision algorithm. Finally, the performance of proposed robot vision system has been illustrated by experiment for the similar model of fifth cell among the twelve cell fur automatic test and assembling in S company.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.174-174
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• 2000

Q-learning, based on discrete state and action space, is a most widely used reinforcement Learning. However, this requires a lot of memory and much time for learning all actions of each state when it is applied to a real mobile robot navigation using continuous state and action space Region-based Q-learning is a reinforcement learning method that estimates action values of real state by using triangular-type action distribution model and relationship with its neighboring state which was defined and learned before. This paper proposes a new Region-based Q-learning which uses a reward assigned only when the agent reached the target, and get out of the Local optimal path with adjustment of random action rate. If this is applied to mobile robot navigation, less memory can be used and robot can move smoothly, and optimal solution can be learned fast. To show the validity of our method, computer simulations are illusrated.