• The Journal of the Korea institute of electronic communication sciences
• /
• v.17 no.4
• /
• pp.687-694
• /
• 2022

In this paper, we proposed an autonomous vehicle platform that delivers goods to a designated destination based on the SLAM (Simultaneous Localization and Mapping) map generated indoors by applying the Visual SLAM technology. To generate a SLAM map indoors, a depth camera for SLAM map generation was installed on the top of a small autonomous vehicle platform, and a tracking camera was installed for accurate location estimation in the SLAM map. In addition, a convolutional neural network (CNN) was used to recognize the label of the destination, and the driving algorithm was applied to accurately arrive at the destination. A prototype of an indoor delivery autonomous vehicle was manufactured, and the accuracy of the SLAM map was verified and a destination label recognition experiment was performed through CNN. As a result, the suitability of the autonomous driving vehicle implemented by increasing the label recognition success rate for indoor delivery purposes was verified.

• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.1916-1917
• /
• 2011

A development of a new moving obstacle avoidance algorithm using a delay-time Compensation for a network-based autonomous mobile robot is proposed in this paper. The moving obstacle avoidance algorithm is based on a Kalman filter through moving obstacle estimation and a Bezier curve for path generation. And, the network-based mobile robot, that is a unified system composed of distributed environmental sensors, mobile actuators, and controller, is compensated by a network delay compensation algorithm for degradation performance by network delay. The network delay compensation method by a sensor fusion using the Kalman filter is proposed for the localization of the robot to compensate both the delay of readings of an odometry and the delay of reading of environmental sensors. Through some simulation tests, the performance enhancement of the proposed algorithm in the viewpoint of efficient path generation and accurate goal point is shown here.

• Proceedings of the KIEE Conference
• /
• 2006.07d
• /
• pp.1832-1833
• /
• 2006

This paper presents a design method of the wavelet neural network(WNN) controller based on a direct adaptive control scheme for the intelligent control of Autonomous Underwater Vehicle(AUV) steering systems. The neural network is constructed by the wavelet orthogonal decomposition to form a wavelet neural network that can overcome nonlinearities and uncertainty. In our control method, the control signals are directly obtained by minimizing the difference between the reference track and original signal of AUV model that is controlled through a wavelet neural network. The control process is a dynamic on-line process that uses the wavelet neural network trained by gradient-descent method. Through computer simulations, we demonstrate the effectiveness of the proposed control method.

• Journal of information and communication convergence engineering
• /
• v.18 no.3
• /
• pp.183-187
• /
• 2020

The general Wi-Fi network connection structure is that a number of IoT (Internet of Things) sensor nodes are directly connected to one AP (Access Point) node. In this structure, the range of the network that can be established within the specified specifications such as the range of signal strength (RSSI) to which the AP node can connect and the maximum connection capacity is limited. To overcome these limitations, multiple middleware bridge technologies for dynamic scalability and load balancing were studied. However, these network expansion technologies have difficulties in terms of the rules and conditions of AP nodes installed during the initial network deployment phase In this paper, an intelligent edge computing IoT device is developed for constructing an intelligent autonomous cluster edge computing network and applying it to real-time road danger context aware and notification system through an intelligent risk situation recognition algorithm.

• International Journal of Computer Science & Network Security
• /
• v.22 no.9
• /
• pp.183-188
• /
• 2022

Recently, more and more researches aimed at the development of automated and autonomous ships are appearing in the scientific environment. One of the main reason is the need to solve the problems of safe navigation and reducing accidents due to human factor, as well as the ever-increasing problem associated with the lack of qualified maritime personnel. Development of technologies based on application of artificial intelligence also plays important role, after all for realization of autonomous navigation concept and enhancement of ship automatic maneuvering processes, advancement of maneuvering functions and elaboration of specific algorithms on prevention of close quarter situations and dangerous approach of ships will be required. The purpose of this work is the review of preconditions of occurrence of the autonomous ship navigation conception, overview of introduction stages and prospects for ship remote control based on unmanned technologies, analysis of technical and intellectual decisions of autonomous surface ships, main research tendencies. The research revealed that the technology of autonomous ship navigation requires further development and improvement, especially in terms of the data transmission protocols upgrading, sensors of navigation information and automatic control systems modernization, which allows to perform monitoring of equipment with the aim of improving the functions of control over the autonomous surface ship operation.

• Journal of Sensor Science and Technology
• /
• v.10 no.5
• /
• pp.329-336
• /
• 2001

This paper describes the steering control and geomagnetism cancellation for an autonomous vehicle using an MR sensor. The magneto-resistive (MR) sensor obtains the vector summation of the magnetic fields from embedded magnets and the Earth. The vehicle is controlled by the magnetic fields from embedded magnets. So, geomagnetism is the disturbance in the steering control system. In this paper, we propose a new method of the sensor arrangement in order to remove the geomagnetism and vehicle body interference. The proposed method uses two MR sensors located in a level plane and the steering controller has been developed. The controller has three input variables ($dB_x$, $dB_y$, $dB_z$) using the measured magnetic field difference, and an output variable (the steering angle). A simulation program was developed to acquire the data to teach the neural network, in order to test the ability of a neural network to learn the steering control process. Also, the computer simulation of the vehicle (including vehicle dynamics and steering) was used to verify the steering performance of the vehicle controller using the neural network. From the simulation and field test, good result was obtained and we confirmed the robustness of the neural network controller in a real autonomous vehicle.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
• /
• 2021.11a
• /
• pp.72-73
• /
• 2021

As discussions of the International Maritime Organization for the introduction of the Marine Autonomous Surface Ship (MASS) began in earnest, discussions were conducted to prioritize cybersecurity (Cyber Risk Management) when developing a system to support MASS operation at the 27th ENAV Committee Working Group (WG2). Korea launched a technology development project for autonomous ships in 2020, and has been promoting detailed tasks for cybersecurity technology development since 2021. MASS operation in a digital maritime communication system environment requires network security of various digital equipment that was not considered in the existing maritime communication environment. This study introduces the basic concept of network security equipment to support MASS operation in the detailed task of cybersecurity technology development, and defines the network security equipment interface for MASS ship application in the basic stage.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.40 no.3
• /
• pp.516-528
• /
• 2015

In this paper, we propose mobile ad hoc network configuration and its autonomous network construction method for efficiency and scalability of media streaming for mobile smart devices. To provide scalable network configuration for streaming traffic distribution, an IEEE 802.11 infrastructure network and ad hoc networks are hierarchically built. The proposed method autonomously configures a hierarchical streaming network by competition based on performance and states of devices and the wireless network, not depending on any specific nodes. Finally, we conduct performance measurement for the proposed configuration and analyze the experimental result.

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.9
• /
• pp.402-412
• /
• 2002

The navigation algorithms enable autonomous mobile robots to reach given target points without collision against obstacles. To achieve safe navigations in unknown environments, this paper presents an effective navigation algorithm for the autonomous mobile robots with ultrasonic sensors. The proposed navigation algorithm consists of an obstacle-avoidance behavior, a target-reaching behavior and a fuzzy-based decision maker. In the obstacle-avoidance behavior and the target-reaching behavior, artificial immune networks are used to select a proper steering angle, make the autonomous mobile robot avoid obstacles and approach a given target point. The fuzzy-based decision maker combines the steering angles from the target-reaching behavior and the obstacle-avoidance behavior in order to steer the autonomous mobile robot appropriately. Simulational and experimental results show that the proposed navigation algorithm is very effective in unknown environments.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.12
• /
• pp.1023-1030
• /
• 2002

Since the dynamics of autonomous underwater vehicles (AUVs) are highly nonlinear and their hydrodynamic coefficients vary with different vehicle's operating conditions, high performance control systems of AUVs are needed to have the capacities of teaming and adapting to the variations of the vehicle's dynamics. In this paper, a linearly parameterized neural network (LPNN) is used to approximate the uncertainties of the vehicle dynamics, where the basis function vector of the network is constructed according to the vehicle's physical properties. The network's reconstruction errors and the disturbances in the vehicle dynamics are assumed be bounded although the bound may be unknown. To attenuate this unknown bounded uncertainty, a certain estimation scheme for this unknown bound is introduced combined with a sliding mode scheme. The proposed controller is proven to guarantee that all signals in the closed-loop system are uniformly ultimately bounded (UUB). Numerical simulation studies are performed to illustrate the effectiveness of the proposed control scheme.