Robot manipulators are highly nonlinear system with multi-inputs multi-outputs, and various control methods for the robot manipulators have been developed to acquire good trajectory tracking performance and improve the system stability lately. The computed torque controller has nonlinear feedforward control elements and so it is very effective to control robot manipulators. If the control gains of the computed torque controller is adjusted according the payload, then more precise control performance is attained. This paper extends the conventional computed torque controller in the joint space to the Cartesian space, and optimize the control gains for some specified payloads in both joint and Cartesian spaces using genetic algorithms. Also a neural network is employed to have proper control gains for arbitrary payloads using generalization properties of the neural network. Computer simulation results show that the proposed control system for robot manipulators has excellent performance in various conditions.

The predictive control system using model-based predictive control is a very effective way to optimize the present inputs considering the states and future errors of the reference trajectory, but it has a drawback in that a control input matrix must be repeatedly calculated with a long calculation time at every sampling for minimizing future errors in a predictive interval. In this study, we applied the neural network simulating the predictive control method for the trajectory tracking control of the mobile robot to reduce complex control method and computation time which are the disadvantage of predictive control. In addition, the neural network showed excellent performance by the generalization even for a different reference trajectory. Therefore, The controller is designed by modeling the model-based predictive control gains for the reference trajectory using a neural networks. Through the computer simulation, the proposed control method showed better performance than the general predictive control method.

Recently, a study on reducing the weight of the robot arm, which enables a high-speed operation and enables reducing the energy consumption has been actively carried out. A lightweight robot arm is hard to control because it behaves like a flexible body rather than a rigid body. This paper proposes a controller which combines a PID controller and a fuzzy logic controller for control the position and vibration of the flexible robot arm. In order to show the effectiveness of the proposed controller, MSC.ADAMS computational model which incorporates the finite element flexible robot arm model is developed, and is used for performing simulations. Simulations are carried out with two reference inputs, and three end masses. Simulation results show that the proposed controller controls the position and vibration of the flexible robot arm adaptively without being affected by the reference input and the end mass.

최근 창의적 공학 교육의 일환으로 많이 활용되고 있는 레고 마인드스톰 NXT와 체감형 인터페이스의 닌텐도사의 wii가 큰 인기를 얻고 있다. 본 논문에서는 Wii 컨트롤러인 위모트를 이용하여 체감형 인터페이스 기반 NXT 로봇을 구동하였다. 각각의 사용으로도 다양한 인터페이스 및 설계가 가능하지만, NXT와 위모트를 함께 사용한다면 더욱 다양한 인터페이스의 창의적인 로봇 및 체감형 인터페이스를 기대하여 본다.

본 논문에서는 ATMEGA128칩을 사용하여 소형 2족 보행로봇의 제어기를 설계 및 구현하였다. 로봇 제 어기는 빠른 연산속도 및 안정된 보행상태를 유지하기 위해 다양한 센서가 필요하다. 본 논문에서는 관절의 구동부로 22개의 RC서보모터를 사용한 소형 2족 보행로봇의 제어기 구조를 제안하고 설계 구현하였다. ATMEGA128칩을 이용하여 각각의 서보모터를 제어하고 호스트컴퓨터와 블루투스통신을 통한 실시간 제어가 가능하도록 설계하였다. 또한 음성인식칩을 사용하여 인간의 명령을 로봇에 전달할 수 있도록 구현하였으며 다양한 실험을 통하여 제안된 2족 로봇제어기의 성능을 고찰하였다.

Autonomous mobile robots based on the Web have been already used in public places such as museums. There are many kinds of problems to be solved because of the limitation of Web and the dynamically changing environment. We present a methodology for intelligent mobile robot that demonstrates a certain degree of autonomy in navigation applications. In this paper, we focus on a mobile robot navigator equipped with neuro-fuzzy controller which perceives the environment, make decisions, and take actions. The neuro-fuzzy controller equipped with collision avoidance behavior and target trace behavior enables the mobile robot to navigate in dynamic environment from the start location to goal location. Most telerobotics system workable on the Web have used standard Internet techniques such as HTTP, CGI and Scripting languages. However, for mobile robot navigations, these tools have significant limitations. In our study, C# and ASP.NET are used for both the client and the server side programs because of their interactivity and quick responsibility. Two kinds of simulations are performed to verify our proposed method. Our approach is verified through computer simulations of collision avoidance and target trace.

This paper deals with the development and application of control algorithms for series elastic relief robots for rescue operations in harsh environment like disasters or battlefield. The joint controller applied in this paper has a cascade structure combining inner loop for torque control and outer loop for position control. The torque loop contains feedforward and feedback controller and disturbance observer for independent, decentralized joint control. The effect of the elastic component and motor dynamics are treated as the nonlinear disturbance and compensated with the disturbance observer of torque controller. For the collision detection, Band Designed Disturbance Observer is configured to recognize/respond to external disturbance robustly in the continuously changing environment. The controller is applied to a 7-dof series elastic manipulator to evaluate the torque tracking and collision detection/response performance.

This paper proposes a method of avoiding obstacles and tracking a moving object continuously and simultaneously by using new concepts of virtual tow point and fuzzy danger factor for differential wheeled mobile robots. Since differential wheeled mobile robot has smaller degree of freedom to control and are non-holonomic systems, there exist multiple solutions (trajectories) to control and reach a target position. The paper proposes 'fuzzy danger factor' for obstacles avoidance, 'virtual tow point' to solve non-holonomic object tracking control problem for unique solution and three kinds of fuzzy logic controller. The fuzzy logic controller is policy decision controller with fuzzy danger factor to decide which controller's result is more valuable when the mobile robot is tracking a moving object with obstacles to be avoided.