In this paper, we present a path-smoothing algorithm for a robot arm manipulator that finds the path using a joint space-based rapidly-exploring random tree. Unlike other smoothing algorithms which require complex mathematical computation, the proposed path-smoothing algorithm is done using a Gaussian process. To find the optimal hyperparameters of the Gaussian process, we use differential evolution hybridized with opposition-based learning. The simulation result indicates that the Gaussian process whose hyperparameters were optimized by hybrid differential evolution successfully smoothed the path generated by the joint space-based rapidly-exploring random tree.

Recently the automobile engine has been developed in achieving the high performance, fuel economy, and emission reduction. In a conventional spark ignition engine the fuel and air are mixed together in the intake system, inducted through the intake valve into the cylinder, and then compressed. Under normal operating conditions, the combustion is initiated towards the end of the compression stroke at the spark plug by an electric discharge. Following inflammation, a flame develops and propagates through this premixed fuel-air mixture. Therefore the state of mixture is very important in the combustion and emission characteristics. In this study the combustion and emission characteristics were tested and analyzed with changing the mixture composition and engine operating parameters in order to improve the combustion and performance in engine.

Since the oil shock of 1970's there was a strong upward tendency for the use of the high viscosity and poorer quality fuels. Therefore the misfiring engine occurs due to the decrease of quantity injected for lean burn and emission control in CI diesel engine. In this study, it is designed and used the test bed which is installed with turbocharger and intercooler. In addition to equipped using CRDI by controlling injection timing with mapping modulator, it has been tested and analyzed the engine performance, combustion characteristics, and emission as operating parameters.

Recently Manipulation capability is important for a robot. Interaction between a robot hand and objects can be properly controlled only is suitable sensors are available. Recently the tendency is to create robot hands more compact and high integrated sensors system, in order to increase the grasping capability and in order to reduce cabling through the finger, the palm and the arm. As a matter of fact, miniaturization and cabling harness represents a significant limitation to the design of small sized embedded sensor. Ongoing work is focusing on a flexible manipulation system, which consists of a dual flexible multi-fingered hand-arm system, and a dual active vision system.

In this paper, it is presented a learning controller for repetitive walking control of biped walking robot. We propose the iterative learning control algorithm which can learn periodic nonlinear load change ocuured due to the walking period through the intelligent control, not calculating the complex dynamics of walking robot. The learning control scheme consists of a feedforward learning rule and linear feedback control input for stabilization of learning system. The feasibility of intelligent control to biped robotic motion is shown via dynamic simulation with 25-DOF biped walking robot.

In this paper, it was presented to design and analyze the kinematics of grasping a rigid object by means of multi-degrees-of-freedom hand fingers. It is shown firstly that a set of kinematic equation describing dynamics system of the arm and object together with geometric constraint of tight area-contacts is formulated by Lagrange's equation. It has been presented secondly that the problems of controlling both the forces of pressing object and the rotation angle of the object under the geometric constraints are discussed. In this research, the control method for static stable grasping and enhancing dexterity in manipulating things is proposed. It is illustrated by computer simulation that the control system gives the performance improvement in the kinematic grasping of the hand fingers of robot.

The purpose of this research is to develop the pattern recognition algorithm based on smart camera for inspection automation, and including external surface state of molding parts or optical parts. By performance verification, this development can be applied to establish for existing reflex data because inputting surface badness degree of scratch's standard specification condition directly. And it is pdssible to distinguish from schedule error of badness product and normalcy product within schedule extent after calculating the error comparing actuality measurement reflex data and standard reflex data mutually. The proposed technology cab be applied to test for masearing of the smallest 10 pixel unit. It is illustrated the relibility pf proposed technology by an experiment.

In this research, we proposed a new approach to implement the real-time motion control of biped robot based on voice command for unmanned FA. Voice is one of convenient methods to communicate between human and robots. To command a lot of robot task by voice, voice of the same number have to be able to be recognition voice is, the higher the time of recognition is. In this paper, a practical voice recognition system which can recognition a lot of task commands is proposed. The proposed system consists of a general purpose microprocessor and a useful voice recognition processor which can recognize a limited number of voice patterns. Given biped robots, each robot task is, classified and organized such that the number of robot tasks under each directory is net more than the maximum recognition number of the voice recognition processor so that robot tasks under each directory can be distinguished by the voice recognition command. By simulation and experiment, it was illustrated the reliability of voice recognition rates for application of the manufacturing process.

This study presents new scheme for various walking pattern of biped robot under the limitted enviroments. We show that the neural network is significantly more attractive intelligent controller design than previous traditional forms of control systems. A multilayer backpropagation neural network identification is simulated to obtain a learning control solution of biped robot. Once the neural network has learned, the other neural network control is designed for various trajectory tracking control with same learning-base. The main advantage of our scheme is that we do not require any knowledge about the system dynamic and nonlinear characteristic, and can therefore treat the robot as a black box. It is also shown that the neural network is a powerful control theory for various trajectory tracking control of biped robot with same learning-vase. That is, we do net change the control parameter for various trajectory tracking control. Simulation and experimental result show that the neural network is practically feasible and realizable for iterative learning control of biped robot.