• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.10a
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• pp.496-499
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• 1997

Field robot represented by excavator can be applied for various kinds of working in manufacturing, construction, agriculture etc. because of the flexibility of its multi-joint mechanism and the high power of hydraulic actuators. In general, the dynamics of field robot have strong coupling, various kinds of non-linearity, and time-varying parameters according to working conditions. Therefore, it is very difficult to describe the system well, and design controller systematically based on its model. This paper established the mathematical model of field robot driven by electro-hydraulic servomechanism and constructed the adaptive control system robust to external load variations. The proposed control system for the field robot was evaluated by the computer simulation and the performance results of trajectory tracking were compared with that of PID control system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.343-343
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• 2000

The conventional actuators with the speed reducer had weakness in supporting the weight of the body and leg itself. To overcome this, a new four bar link mechanism actuated by the ball screw was proposed. Using this, we developed a new type of 10 D.O.F biped robot. The dynamics model of the biped robot is investigated in this paper. In the modeling process, the robot dynamics are expressed in the joint coordinates using the Euler-Lagrange equation. Then, they are converted in to the sliding joint coordinates, and joint torques are expressed in the force along the sliding direction of the ball screw. To test modeling of the robot, a computer simulation was performed.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.10
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• pp.907-911
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• 2007

Compliance of joints must be considered when we analyze dynamics of a multi-body system. If the virtual model for CAE(computer aided engineering) analysis does not consider compliance, the result of CAE analysis can be very different from the actual experimental result. Especially in a biped walking robot, the robot may lose walking stability due to the compliance in joints of a walking robot. This paper proposed a method applying a compliance of joints in the biped walking robot to a virtual model. Also, through the 3-D displacement measurement using a laser tracker, it was demonstrated that the virtual model considering the joint compliance could effectively simulate the nonlinear motion of the real model.

• The Journal of Korea Robotics Society
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• v.2 no.2
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• pp.183-190
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• 2007

The most important thing for navigation of a mobile robot is to find the most suitable path and avoid the obstacles in the static and dynamic environment. This paper presents a method to search the optimal path in start space extended to time domain with considering a velocity and a direction of moving obstacles. A modified version of $A^*$ algorithm has been applied for path planning in this work and proposed a method of path search to avoid a collision with moving obstacle in space-tim domain with a velocity and an orientation of obstacles. The velocity and the direction for moving obstacle are assumed as linear form. The simulation result shows that a mobile robot navigates safely among moving obstacles of constant linear velocity. This work can be applied for not only a moving robot but also a legged humanoid robot and all fields where the path planning is required.

• The Journal of Korea Robotics Society
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• v.5 no.3
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• pp.209-215
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• 2010

This paper presents a graphic user interface system consisting of graphic simulator and remote control system for a building cleaning robot. It provides a tool of convenient 3D graphical map construction for real world. The 3D map is reconstructed from existing 2D building CAD data with DXF format by using OpenGL graphic API. Through this system, graphic display of robot's status information, remote control and cleaning scheduling can be done for a building cleaning robot. This proposed system is expected to give efficient way of graphic simulation and remote monitoring and control system for a building cleaning robot.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2001.11a
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• pp.233-238
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• 2001

The mechanism design of the interactive emotional robot has been carried out. The two-wheeled inverted pendulum type mechanism was adopted to improve the mobility and make the innate clumsy monoaxial bicycle motion. Even though the system is unstable in itself, it is expected for the robot to move freely in a plane, keeping the upright position only with two wheels. Two motors attached on head can make 4 motion sets, and two motors on the wheels can make 8. Therefore, 32 independent motion sets can be achieved from the robot to communicate the emotions with humans. The motion's equation of the robot was derived based on nonholonomic dynamics, and the necessary power to the wheel's rotational axis was found by simulation.

• Journal of Drive and Control
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• v.15 no.3
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• pp.1-7
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• 2018

Humanoid robots have fascinated many researchers since they appeared decades ago. For the requirement of both accurate tracking control and the safety of physical human-robot interaction, torque control is basically desirable for humanoid robots. Humanoid robots are highly nonlinear, coupled, complex systems, accordingly the calculation of robot model is difficult and even impossible if precise model of the humanoid robots are unknown. Therefore, it is difficult to control using traditional model-based techniques. To realize model-free torque control, time-delay control (TDC) for humanoid robot was proposed with time-delay estimation technique. Using optimal walking trajectory obtained by particle swarm optimization, TDC with proposed scheme is implemented on whole body of a humanoid, not on biped legs even though it is performed by a virtual humanoid robot. The simulation results show the validity of the proposed TDC for humanoid robots.

• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2834-2836
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• 2000

This paper introduces the behavior-based learning controller for mobile robot using topological map. When the mobile robot navigates to the goal position, it utilizes given information of topological map and its location. Under navigating in unknown environment, the robot classifies its situation using ultrasonic sensor data, and calculates each motor schema multiplied by respective gain for all behaviors, and then takes an action according to the vector sum of all the motor schemas. After an action, the information of the robot's location in given topological map is incorporated to the learning module to adapt the weights of the neural network for gain learning. As a result of simulation, the robot navigates to the goal position successfully after iterative gain learning with topological information.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.9-14
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• 2006

This paper presents methods of robot localization using recent radio frequency identification technology. If the absolute position and orientation of a tag are given in an indoor environment where RFID tags are installed, a robot can estimate its location using the relationship of the identified tag and the robot in a relative coordinate. To derive this relationship, we propose three estimation techniques using a model of a RFID reader, the direction of identification and the detection range. In this algorithm, a suitable estimation method is selected out of the three proposed techniques depending on the situations and trajectory of robot in the detection range. Simulation and experimental results show that the proposed methods can provide good performance for localization.

• Journal of Institute of Control, Robotics and Systems
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• v.3 no.3
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• pp.252-258
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• 1997

This paper presents neural networks for hybrid position/force control which is a type of position and force control for robot manipulators. The performance of conventional hybrid position/force control is excellent in the case of the exactly-known dynamic model of the robot, but degrades seriously as the uncertainty of the model increases. Hence, the neural network control scheme is presented here to overcome such shortcoming. The introduced neural term is designed to learn the uncertainty of the robot, and to control the robot through uncertainty compensation. Further more, the learning rule of the neural network is derived and is shown to be effective in the sense that it requires neither desired output of the network nor error back propagation through the plant. The proposed scheme is verified through the simulation of hybrid position/force control of a 6-dof robot manipulator.