Search

This paper proposes positioning and trajectory tracking for Caterpillar Vehicles (CVs) in unknown environments. To do these tasks, the following are performed. Firstly, a system modeling of the Caterpillar Vehicle is presented. Secondly, solving the complicated tracking control problem in unknown environments is a challenging mission. Therefore, to guarantee the Caterpillar Vehicle system to be strong robustness against external disturbances in the unknown environments, a MIMO robust servo controller for tracking the desired trajectory is designed by using a Linear Shift Invariant Differential (LSID) operator. The CVs are able to accomplish various tasks in dangerous places where workers cannot enter. Thirdly, the positioning of the CV can be obtained using a Simultaneous Localization and Mapping (SLAM) method. This paper develops perfectly the SLAM algorithm for positioning of the CV based on laser sensor Lidar. The main advantage of this method is that it does not need to use more landmarks. Landmarks can be obtained from the unknown environment. Thus, the CV can work even in unknown environments and unsafe places. Finally, to verify the effectiveness of the proposed MIMO robust servo controller and the SLAM positioning algorithm, the experimental results are presented. The experimental results demonstrate the adequate tracking performance of the proposed MIMO robust servo controller in the unknown environment.

Motion control with high accuracy for each axial system is the fundamental requirement to reduce a synchronous motion error of a multi-axis system. Especially, designing a model-based controller for an uncertainty system with unknown parameters is not easy without using system identification. To overcome the mentioned issue, this article proposes a cross-coupling synchronous velocity controller using a backstepping-based model reference adaptive control scheme in an unsymmetrical biaxial winding system called a transformer winding system. The proposed controller deals not only with the uncertainty but also with the recursive structure of the system. The backstepping technique for the recursive structural system and the model reference adaptive control method for the uncertainty of the system are designed to stabilize two axial systems with unknown parameters. An auxiliary system is added to build the proposed controller for coping with input constraints of physical actuators. To improve the proposed controller’s ability to cope with external disturbances, a dead-zone modification is utilized to modify the adaptation laws to avoid the drift phenomenon. Moreover, a cross-coupling mechanism is integrated into the proposed controller to reduce the synchronous velocity error between the velocities of the biaxial winding system. The proposed controller is also transformed into discrete time to be run on a digital signal processor alone chip. The experimental results are shown to verify the high performance and efficiency of the proposed controller for practical applications.

This paper proposes a servo system designed for speed control of AC induction motors using polynomial differential operator to track more complicated types of reference speed and disturbance such as step, ramp, parabola, etc. To do this task, the followings are done. First, modeling for an induction motor is introduced and is linearized at equilibrium points using Taylor’s series. Second, observer is designed to estimate flux, and an extended system incorporating the internal model principle to construct the servo system is derived using polynomial differential operator in case that the types of reference inputs are polynomials. Third, a state feedback control law for the servo system to track the given reference input is designed. To implement the proposed controller and servo system, a control system is constructed for speed control of 1.5 Kw AC induction motor. The simulation and experimental results are conducted to verify the effectiveness and the applicability of the proposed controller compared to the conventional PID controller and MRAC controller for more complicated higher order types of the references and disturbance such as step, ramp, parabola, etc.

This paper presents a backstepping-based model reference adaptive controller for a multi-axial system in the presence of external disturbance and saturated input. The proposed controller synthesizes the backstepping technique and the model reference adaptive control method to construct control inputs for recursive structure and uncertain modelling of the multi-axial system. To cope with the limit of saturated input, an auxiliary system is adopted. A dead-zone modification is introduced to avoid the drift phenomenon of adjusted adaptive parameters. The stability of the proposed controller is proven by Lyapunov’s theory while considering the effect of the auxiliary system and the dead-zone modification in the design stage. The effectiveness and performance of the proposed controller are evaluated by experiment on a transformer winding system.

This paper proposes a MIMO robust servo controller design method for a three wheeled Omnidirectional Automated Guided Vehicles (OAGVs) with a disturbance to track desired references using a polynomial differential operator. The process for designing the proposed controller can be described as follows: Firstly, modeling of the MIMO three-wheeled OAGV are presented. Secondly, a new extended system is obtained by applying the polynomial differential operator to the state space model and the output velocity error vector. Thirdly, the proposed controller for the given plant is designed by using the pole assignment method. By applying an inverse polynomial differential operator, a servo compensator is obtained. Finally, in order to verify the effectiveness of the proposed controller, the numerical simulation results are shown. The simulation results show that the proposed controller has good tracking performance under a step type of disturbance and the complicated higher order reference signals such as ramp and parabola. These simulation results are compared with those of the adaptive controller proposed by Bui, T. L. in 2013. The proposed controller shows the better tracking performance than the adaptive controller.

This paper proposes a fish contour detection method using a local kernel regression method. To do this task, the followings are done. Firstly, 3D depth map of a fish is obtained by using Kinect camera sensor. Secondly, edge of the fish in 3D depth map is transformed into numerous points. Thirdly, all information is removed except the edge points. However, recognition points for recognizing the fish are appropriately left because there are only points left to distinguish between background and fish. Fourthly, the points are recognized as contour of the fish by using the local kernel regression method. Finally, experiment results using Kinect camera sensor are shown to verify the validity of the proposed method compared to Canny method.

This paper proposes a visual servoing controller design based on Barrier Lyapunov function for a picking system. Visual servoing uses feedback data provided by the camera to control the movement of a picking system in a closed loop system. Visual servoing requires an object in the field of view of the camera in order to control the picking system. To improve the visual servoing controller, the image-based visual servoing and the position-based visual servoing are presented. To apply this method an offline trajectory is developed to perform the image-based visual servoing and the position-based visual servoing tasks for the picking system. Two different control approaches i.e. the visual servoing controller with the limit orientation using the Barrier Lyapunov function and the visual servoing controller with a quadratic Lyapunov function are presented. The proof of asymptotic stability is presented and simulation results from two visual servoing controllers are presented to verify the effectiveness of the proposed controller.

This paper presents positioning and obstacle avoidance of Automatic Guidance Vehicle (AGV) in partially known environment. To do this task, the followings are done. Firstly, the system configuration of AGV is described. Secondly, mathematical kinematic modeling of the AGV is presented to understand its characteristics and behavior. Thirdly, the Simultaneous Localization and Mapping (SLAM) algorithm based on the laser measurement system and encoders is proposed. The encoders are used for detecting the motion state of the AGV. In a slippery environment and a high speed AGV condition, encoder positioning method generates big error. Therefore, Extended Kalman Filter (EKF) is used to get the best position estimation of AGV by combining the encoder positioning result and landmark positions obtained from the laser scanner. Fourthly, to achieve the desired coordinate, D* Lite algorithm is used to generate a path from the start point to the goal point for AGV and to avoid unknown obstacles using information obtained from laser scanner. A backstepping controller based on Lyapunov stability is proposed for tracking the desired path generated by D* Lite algorithm. Finally, the effectiveness of the proposed algorithms and controller are verified by using experiment. The experimental results show that the AGV successfully reaches the goal point with an acceptable small error.

This paper presents a new type of four wheel independent steering automatic guided vehicle (4WIS-AGV) for carrying heavy baggage and proposes a controller for the 4WIS-AGV to track reference trajectories. To do this task, the followings are done. Firstly, a 4WIS-AGV is designed and manufactured for experimental purpose. Secondly, a kinematic modeling for the 4WIS-AGV is introduced based on a single track vehicle model. Thirdly, based on the modeling, a controller is designed based on Backstepping method for the 4WIS-AGV to track reference trajectories. Fourthly, a control system is developed using industrial PC and AVR ATmega128 microcontrollers to implement the designed controller. Finally, simulations and experiments are conducted to verify the effectiveness and performances of the proposed controller in tracking two types of reference trajectories: a trajectory with sharp edges for parallel steering maneuver and a circular trajectory for zero-sideslip maneuver. The results show that the proposed controller can make the 4WIS-AGV track the trajectory with sharp edges and the circular trajectory very well.

This paper proposes two measurement methods for injured rate of fish surface using color image segmentation method based on K-means clustering algorithm and Otsu’s threshold algorithm. To do this task, the following steps are done. Firstly, an RGB color image of the fish is obtained by the CCD color camera and then converted from RGB to HSI. Secondly, the S channel is extracted from HSI color space. Thirdly, by applying the K-means clustering algorithm to the HSI color space and applying the Otsu’s threshold algorithm to the S channel of HSI color space, the binary images are obtained. Fourthly, morphological processes such as dilation and erosion, etc. are applied to the binary image. Fifthly, to count the number of pixels, the connected-component labeling is adopted and the defined injured rate is gotten by calculating the pixels on the labeled images. Finally, to compare the performances of the proposed two measurement methods based on the K-means clustering algorithm and the Otsu’s threshold algorithm, the edge detection of the final binary image after morphological processing is done and matched with the gray image of the original RGB image obtained by CCD camera. The results show that the detected edge of injured part by the K-means clustering algorithm is more close to real injured edge than that by the Otsu’ threshold algorithm.

This paper presents an implementation and experimental validation of trajectory tracking and fault detection algorithm for sensors and actuators of Automatic Guided Vehicle (AGV) system based on multiple positioning modules. Firstly, the system description and the mathematical modeling of the differential drive AGV system are described. Secondly, a trajectory tracking controller based on the backstepping method is proposed to track the given trajectory. Thirdly, a fault detection algorithm based on the multiple positioning modules is proposed. The AGV uses encoders, laser scanner, and laser navigation system to obtain the position information. To understand the characteristics of each positioning module, their modeling are explained. The fault detection method uses two or more positioning systems and compares them using Extended Kalman Filter (EKF) to detect an unexpected deviation effected by fault. The pairwise differences between the estimated positions obtained from the sensors are called as residue. When the faults occur, the residue value is greater than the threshold value. Fault isolation is obtained by examining the biggest residue. Finally, to demonstrate the capability of the proposed algorithm, it is applied to the differential drive AGV system. The simulation and experimental results show that the proposed algorithm successfully detects the faults when the faults occur.

An outbreak of nosocomial infections with Middle East respiratory syndrome coronavirus occurred in South Korea in May 2015. Spike glycoprotein genes of virus strains from South Korea were closely related to those of strains from Riyadh, Saudi Arabia. However, virus strains from South Korea showed strain-specific variations.

Determination of injury rate on fish surface is one of the major cause for increasing quality of fish on the markets. Detecting injury fishes manually is hard work with low efficiently. An image processing method is usually considered to do this work as an online selecting method of injury area. The injury area segmentation of fish image is based on color features with Fuzzy C-means clustering and L*a*b* color space. Although there are many different color spaces. CIE L*a*b* color space of them is most used for detecting fish injury due to its uniform color distribution and is closest to the one human eye. Generally, a fish injury image has overlapped data with fish body image. The K-means clustering algorithm cannot give the good solution to segment whether pixels of the overlapped data belong to fish body or injury area. To solve this problem, this paper is to present the good solution for segmentation of fish injury image with the overlapped data from the fish body image and determine injury rate on fish surface based on Fuzzy C-means clustering and L*a*b* color space using ZED stereo camera. The proposed image processing method is tested on fishes. The experimental results show that the injury rate measured using the proposed image processing method is close to the real injury rate.

In May 2015, Middle East respiratory syndrome coronavirus infection was laboratory confirmed in South Korea. Patients were a man who had visited the Middle East, his wife, and a man who shared a hospital room with the index patient. Rapid laboratory confirmation will facilitate subsequent prevention and control for imported cases.

This paper presents a backstepping-based modified model reference adaptive velocity tracking controller design method to track a trapezoidal reference type of rotational velocity for a winding spindle system (WSS). To design the proposed controller, the following are done. Firstly, the system modeling of the WSS is obtained as a combined form of direct current motor and mechanical parts. Secondly, a modified model reference system with known parameters is chosen according to the dimension of the given system such that its rotational velocity output tracks the trapezoidal reference input of the winding spindle system. Then, the rotational velocity output of the winding spindle system is controlled to track the rotational velocity output of the modified model reference system by designing update laws for updating the adaptive controller parameters. Thirdly, since the system modeling of the winding spindle system is constructed as a recursive structure system, the modified model reference adaptive controller is developed based on backstepping approach. Finally, the effectiveness and performance of the proposed controller are evaluated with comparison to those of a conventional PID controller by experimental results.

Based on basic properties of L*A*B* color space, this paper proposes a new approach of 2D image processing which is used for measurement of injury rate on fish by a modified K-means clustering algorithm and Otsu’s threshold algorithm. Then, experimental results of the proposed method are compared to the results of a manual threshold method on L*A*B* color space. To do this issue, the following tasks are done. Firstly, an original color image is transferred into L*A*B color space. Secondly, channel “a” is separated from L*A*B* color space image. Thirdly, the value of channel “a” is adjusted by changing the contrast algorithm. Fourthly, the modified K-means clustering algorithm on a new channel “a” image is applied to define and divide data elements into different groups. Fifthly, Gaussian Filter is used to filter the random “noises” in shape of injury and fish images. Sixthly, Otsu’s threshold algorithm is used to transfers the filtered images into binary images. Seventhly, final images are obtained after filtering the rest of “noises” by morphological processing. Finally, the areas of injury and fish shapes are obtained by counting pixels on both of the final binary images. The experiment results show that the proposed new approach is closer to the real injury and injury rate on fish than the results of the manual threshold method on L*A*B color image.

This paper proposes recognition and grasping objects from 3D environment by combining depth and color stereo image in the mobile picking robot system. To do this task, the followings are done. Firstly, an image processing system including Kinect camera sensor is described. Secondly, RGB color map and new depth map for image inpainting are obtained using Kinect SDK mapping function to align RGB image with depth image. Thirdly, the new depth map are segmented to distinguish between the image background and the objects that should be recognized. The feature colours are generated based on colour histograms. Euclidean distance is used to measure the similarity between the feature vectors computed from the colour image and the feature vectors stored in a database. Fourthly, by converting RGB map and new depth map into 3D point clouds, an algorithm for localizing handle-like grasp affordances is proposed. The main idea is to search the point cloud for neighborhoods that satisfy handle-like grasp affordances and can be grasped by the end-effector of the manipulator. Finally, the effectiveness of the proposed algorithms is verified by using experiment. The experimental results show that the mobile picking robot successfully detects an object and finds its grasping points with an acceptable small error.

This paper proposes determination of the fish surface area and volume using ellipsoid approximation method applied for image processing based on two views of fish images. The fishes are considered as ellipsoid shapes on three view sides that appropriate analytical models for their surface and volume estimation. To do this task, the followings are done. Firstly, mathematical modeling of surface area and volume of ellipsoid analytically are presented. Secondly, ellipsoid shape is segmented to determine surface area and volume for computation. Finally, surface area and volume of fish are measured by image processing technique. The surface area and volume computed are compared with analytical and experimental results.

This paper proposes a new hybrid method for efficiency optimization of induction motor drives. To do this task, the followings are done. Firstly, the induction motor drive with 3 phases is modeled by using copper loss and core loss to find out the optimal efficiency of the power losses. Secondly, golden section technique is used to optimize the drive efficiency by searching for an optimal value according to the reference rotor flux current. In this way, the electrical input power of the system can also be minimal. Finally, the improved algorithm is proposed to enhance the system response speed and reduce torque ripple. The simulation is done to validate the proposed algorithm. The simulation results show that the proposed algorithm can be better than a convention method such as loss model control (LMC) and search control (SC) in terms of lower torque ripple and higher response speed.

This paper proposes a tracking controller for omni-directional automated guided vehicle using backstepping and model reference adaptive control. To design the proposed controller, the followings are done. Firstly, a system modeling is obtained by analyzing a dynamic modeling of an OAGV. Secondly, a backstepping technique is utilized to design the proposed controller since a system modeling has a recursive structure. Moreover, there exist uncertain parameters in the system modeling, a known reference model is utilized so that the output of the reference model tracks a virtual control input. Thirdly, uncertain parameters are estimated by update laws in order that the velocity vector of the OAGV tracks the output vector of the reference model control input. Finally, the stability of the proposed controller is analyzed by Lyapunov theory and the effectiveness of the proposed controller are verified by simulation results.

This paper is about control of Automated Guided Vehicle for path following using fuzzy logic controller. The Automated Guided Vehicle is a tricycle wheeled mobile robot with three wheels, two fixed passive wheels and one steering driving wheel. First, kinematic and dynamic modeling for Automated Guided Vehicle is presented. Second, a controller that integrates two control loops, kinematic control loop and dynamic control loop, is designed for Automated Guided Vehicle to follow an unknown path. The kinematic control loop based on Fuzzy logic framework and the dynamic control loop based on two PID controllers are proposed. Simulation and experimental results are presented to show the effectiveness of the proposed controllers.

Autonomous Guided Vehicle Systems (AGVs) are used to transport goods and products in manufacturing fields where navigation can be done in a structured environment. In order to track the given trajectory, a tracking control based on Lyapunov stability theory is introduced. The use of the nonlinear Lyapunov technique provides robustness for load disturbance and sensor noise. To apply Lyapunov's theorem, the kinematic model of AGV is given. To recognize its position in indoor environment, in this paper, a laser sensor device NAV200 is used to detect the AGV position in real-time. For simulation and experiment, software and hardware are described. The AGV consists of 4 wheels with two passive wheels and two driving wheels. A controller is developed based on industrial computer. The effectiveness of the proposed controller is proved by simulation and experimental results. [AGV Trajectory Control, Laser Sensor Navigation]

Modified-model reference adaptive controller (M-MRAC) for belt conveyors of induction conveyor line in the presence of saturated inputs and bounded disturbances is proposed in this paper. In this controller, M-MRAC architecture is combined with robust e-modification adaptive laws for the MIMO linear system with unknown bounded disturbances. For the M-MRAC system, adaptation rate can increase largely without generating high frequency oscillation in control input signal and e-modification update laws prevents a drift of estimate parameters in the presence of the bounded disturbance. Moreover, an auxiliary system is introduced for compensating the error dynamics of the MIMO linear system when the saturation input occurs. Therefore, the steady state error is guaranteed around bounded range when a saturated input error does not equal to zero. Finally, the simulation results are shown to verify the effectiveness and the performance of the proposed controller for the MIMO linear system with the bounded disturbance and saturated input and a conventional MRAC controller.

Motion planning, such as mobile manipulation, must not only be able to move safety through their environments, but also be able to manipulate objects in their environments. This paper proposes localization and path planning for a mobile robot (MR) in a picking robot system using Kinect camera in a partially known environment. A Kinect RGB and depth camera is utilized to recognize objects for obstacle avoidance and path planning. To do this task, the followings are done. Firstly, the system configuration of MR is described. Secondly, mathematical kinematic modeling of the MR in the picking robot system is presented. Thirdly, an object detection algorithm based on the Kinect camera is proposed to detect landmark. Extended Kalman Filter (EKF) is used to get the best localization estimation of the MR by combining the encoder positioning result and landmark positions obtained from the Kinect camera. Fourth, D* Lite algorithm is used to generate a path from the start point to the goal point for MR and to avoid unknown obstacles using information obtained from Kinect camera. A controller design for the MR to track the trajectory generated by the D* algorithm based on backstepping method is proposed. Finally, the effectiveness of the proposed algorithms and controller is verified by using experiment. The experimental results show that the MR successfully reaches the goal point with an acceptable small error.

This paper presents locomotion control of a hexapod robot with tripod gait using central pattern generator (CPG) network. To do this task, the followings are done. First, kinematic modeling of one leg with four links and four rotational joints of hexapod robot is proposed using Denavit-Hartenberg (DH) convention. Second, the walking gait of hexapod robot is generated by central pattern generator network. Third, mapping functions are proposed to map the output of CPG network into a desired trajectory of the end effector of the hexapod robot. It is difficult and complex to get the angles of four rotational joints of one leg using inverse kinematics. To solve this problem, differential kinematics algorithm is used to realize the end effector following the trajectory obtained by the mapping functions for six legs. Finally, simulation results for walking motion of the hexapod robot are shown to prove the effectiveness and applicability of the proposed controller.

A self-propelled pilot mining robot was developed by KRISO (Korea Research Institute of Ships and Ocean Engineering) in 2012. The purpose of the pilot mining robot is to gather manganese nodules that spread on the seabed and to transfer them into buffer. The pilot mining robot consists of two independent unit modules where each unit module has two tracks, two pick-up devices, and one delivery pump. The manganese nodules are gathered by a pick-up device while the robot is driving. The pick-up device has to keep an appropriate distance from the seabed for efficient gathering. Then an actuator of the pick-up device is operated by hydraulic system. This paper discusses the distance control of the pick-up device. The distance control is implemented by a PI controller from gain tuning formulas based on the model identification. First, the input-output function of the hydraulic pick-up actuator is calculated. Second, the transfer function of the pick-up device is identified by a closed-loop response of a second order under-damped system. Next, PI gains are tuned by tuning formulas based on the identified system parameters. Finally, the tuned PI gains are compared, and one of them is selected as a distance controller.