Accurate estimation of pest density is a prerequisite in achieving efficient pest management. An automatic pest detection system with image processing was installed on a robot to recognize brown marmorated stink bug (Halyomorphahalys) on leaves of paprika(Capsicumannuumvar.angulosum). The shape of pest was recognized and subsequently the robot arm was moved toward the leaves to spray pesticides. The detection system was efficient along with increasing population densities increased. The robot with image processing system was useful for estimating population densities in spatial and temporal domain efficiently.

This paper proposes a study for accurate surface localization system using DWT(Discrete Wavelet Transform) and GPS/INS fusion algorithm. Because the propagation in the underwater is not passed by characteristics of the medium unlike the ground, the sonar system like DVL is used instead of GPS. But since these systems are installed on the seafloor and operated, a long time is required for installation and navigation systems are limited outside of the range area. And it is difficult to estimate position in a three-dimensional considering the depth in actual marine environment. In this paper, before the development of underwater localization system, precisely estimated position system is proposed in a two-dimensional by developing surface localization system using removing noise and disturbance with DWT and relatively inexpensive GPS and INS sensor.

This paper proposes an optimal posture for the task-oriented movement of dual arm manipulator. A stability criterion function which consists of three kinds of feature-representative parameters has been utilized to define the optimal posture. The first parameter is the force which is applied to the object. The torque of each joint and position of arm are attained from the current sensor and encoder, respectively. From these two data, the applied force to an object is estimated using sum of vectors of the joint torques estimated from the measured current. In order to investigate the robustness of each posture, the variation of the end-effector from the encoder information has been utilized as the second parameter. And for the last parameter for the optimality, the total energy consumption has been used. The total consuming energy of each posture can be computed from the current information and the battery voltage. The proposed robot structure consists of a mobile inverted pendulum and dual manipulators. In order to define the optimal posture for the each object, external disturbances are applied to the mobile inverted pendulum robot and the first and second parameters are investigated to find the optimal posture among the pre-selected most representative postures. Finally, the proposed optimal posture has been verified by the proposed stability criterion function which consists of total force to the object, the fluctuation of the end-effector position, and total energy consumption. The effectiveness of the proposed algorithms has been verified and demonstrated through the practical simulations and real experiments.

This paper proposed a new magnetic field detection algorithm to detect metal pieces in food producing processes. This algorithm can detect mixed metal pieces by sensing magnetic field. Some metal pieces are passed through an over-current circuit to magnetize them. The magnetic field sensor can detect the change in the magnetic field on the conveyor belt caused by the flow of the metal pieces in the food product. However, such a method detects the output of signals that change their amplitude and phase according to the movement of the conveyor belt with the food product, in which the equilibrium of the positive signal that is created in the receiver coil loses its balance due to the magnetized material. This includes not only the signal elements resulting from the effect of the alternating magnetic fields of the mixed metals, but also the signal elements resulting from the effect of the alternating magnetic fields of the examined object itself.

The field of robots is being widely accepted as a new technology today. Many robots are produced continuously to impart amusement to people. Especially the robot which operates with a wheelbarrow was enough of a work of art to arouse excitement in the audiences. All the wheelbarrow robots share the same technology in that the direction of roll and pitch are acting as balance controllers, allowing the robots to maintain balance for a long period by continuously moving forward and backward. However one disadvantage of this technology is that they cannot avoid obstacles in their way. Therefore movement in sideways is a necessity. For the control of rotation of yawing direction, the angle and direction of rotation are adjusted according to the velocity and torque of rotation of a motor. Therefore this study aimed to inquire into controlling yawing direction, which is responsible for rotation of a robot. This was followed by creating a simulation of a wheelbarrow robot and equipping the robot with a yawing direction controlling device in the center of the body so as to allow sideway movements.

Recently, with the development of service robots and with the new concept of ubiquitous world, the position estimation of mobile objects has been raised to an important problem. As pre-liminary research results, some of the localization schemes are introduced, which provide the absolute location of the moving objects subjected to large errors. To implement a precise and convenient localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter: 1. The RFID receiver gets the synchronization signal from the mobile robot and 2. The ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. Since it is not easy to install the beacons at a specific position precisely, there exists a large localization error and the installation time takes long. To overcome these problems, and provide a precise and convenient localization system, a new auto calibration algorithm is developed in this paper. Also the extended Kalman filter has been adopted for improving the localization accuracy during the mobile robot navigation. The localization accuracy improvement through the proposed auto calibration algorithm and the extended Kalman filter has been demonstrated by the real experiments.

This paper introduces a novel approach that can provide the three dimensional information about the movement of a spreader by using two CCD cameras and a laser distance measuring unit in order to derive ALS (Automatic Landing System) in the crane used at a harbor. So far a kind of 2D Laser scanner sensor or laser distance measuring units are used as comer detectors for the geometrical matching between the spreader and a container. Such systems provide only two dimensional information which is not enough for an accurate and fast ALS. In addition to this deficiency in performance, the price of the system is too high to adapt to the ALS. Therefore, to overcome these defects, we proposed a novel method to acquire the three dimensional spreader information using two CCD cameras and a laser distance measuring unit. To show the efficiency of proposed method, real experiments are performed to show the improvement of accuracy in distance measurement by fusing the sensory information of the CCD cameras and a laser distance measuring unit.

This paper proposes a sensor-fusion technique where rho data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent only on the current data sets. As the results, more of sensors are required to measure a certain physical promoter or to improve the accuracy of the measurement. However, in this approach, intend of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples md the effectiveness is proved through the simulation. Finally, the new space and time sensor fusion (STSF) scheme is applied to the control of a mobile robot in the indoor environment and the performance was demonstrated by the real experiments.