• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1791-1796
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• 2004

Recently, great improvements have been made in developing autonomous underwater vehicles (AUVs) using stateof- the-art technologies for various kinds of sophisticated underwater missions. To meet increasing demands posed on AUVs, a powerful on-board computer system and an accurate sensor system with an well-organized control system architecture are needed. In this paper, a new control system architecture is proposed for AUV, ORCA (Oceanic Reinforced Cruising Agent) which is being currently developed by Korea Research Institute of Ships and Ocean Engineering (KRISO). The proposed architecture uses a hybrid architecture that combines a hierarchical architecture and a behavior based control architecture with an evaluator for coordinating between the architectures. This paper also proposed a sensor fusion structure based on the definition of 4 categories of sensors called grouping and 5-step data processing procedure. The development of the AUV, ORCA involving the system architecture, vehicle layout, and hardware configuration of on-board system are described.

• Journal of information and communication convergence engineering
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• v.11 no.4
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• pp.274-281
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• 2013

In this study, we consider the challenges in motion planning for automated driving systems. Most of the existing online motion-planning algorithms, which take dynamics into account, find it difficult to operate in an environment with narrow passages. Some of the existing algorithms overcome this by offline preprocessing if environment is known. In this work an online algorithm for motion planning with dynamics in an unknown cluttered environment with narrow passages is presented. It utilizes an idea of hybrid planning with sampling- and discretization-based motion planners, which run simultaneously in a full configuration space and a derived reduced space. The proposed algorithm has been implemented and tested with a real autonomous vehicle. It provides significant improvements in computational time performance over basic planning algorithms and allows the generation of smoother paths than those generated by the recently developed hybrid motion planners.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.6 no.4
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• pp.314-320
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• 2006

Navigation in environments that are densely cluttered with obstacles is still a challenge for Autonomous Ground Vehicles (AGVs), especially when the configuration of obstacles is not known a priori. Reactive local navigation schemes that tightly couple the robot actions to the sensor information have proved to be effective in these environments, and because of the environmental uncertainties, STSF(Space and Time Sensor Fusion)-based fuzzy behavior systems have been proposed. Realization of autonomous behavior in mobile robots, using STSF control based on spatial data fusion, requires formulation of rules which are collectively responsible for necessary levels of intelligence. This collection of rules can be conveniently decomposed and efficiently implemented as a hierarchy of fuzzy-behaviors. This paper describes how this can be done using a behavior-based architecture. The approach is motivated by ethological models which suggest hierarchical organizations of behavior. Experimental results show that the proposed method can smoothly and effectively guide a robot through cluttered environments such as dense forests.

• Journal of Positioning, Navigation, and Timing
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• v.10 no.2
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• pp.91-102
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• 2021

In this paper, a fully reconfigurable Software Defined Radio (SDR) for multi-constellation and multi-frequency Global Navigation Satellite System (GNSS) receivers is presented. The reconfigurability with respect to the data structure, variability of signal and receiver parameters, and receiver's internal functionality is presented. The configuration file, that is modified to lead to an entirely different operation of the SDR in response to specific target signal scenarios, directly determines the operating characteristics of the SDR. In this manner, receiver designers can effectively reduce the effort to develop many different combinations of multi-constellation and/or multi-frequency GNSS receivers. Finally, the implementation of the presented fully reconfigurable SDR is included with the experimental processing results such as acquisition, tracking, navigation for the received signals in the realistic fields.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.758-771
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• 2021

This paper presents a novel control scheme for the three-dimensional (3D) path following of underactuated Autonomous Underwater Vehicle (AUVs) subject to unknown internal and external disturbances, in term of the time scale decomposition method. As illustration, two-time scale motions are first artificially forced into the closed-loop control system, by appropriately selecting the control gain of the integrator. Using the singular perturbation theory, the integrator is considered as a fast dynamical control law that designed to shape the space configuration of fast variable. And then the stabilizing controller is designed in the reduced model independently, based on the time scale decomposition method, leading to a relatively simple control law. The stability of the resultant closed-loop system is demonstrated by constructing a composite Lyapunov function. Finally, simulation results are provided to prove the efficacy of the proposed controller for path following of underactuated AUVs under internal and external disturbances.

• Journal of Korea Society of Digital Industry and Information Management
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• v.18 no.4
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• pp.41-46
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• 2022

In the fourth industrial era, when various technologies are fused and combined, new and advanced technologies from other industries are used extensively in the maritime industry field. New security threats are also increasing along with the development of new technologies. In addition, in incorporating convergence technologies into the maritime industry, various problems, such as communication definitions and procedures between technologies and customer-customized delays, occur. In this paper, for the problems mentioned above, research results on the network configuration of safer autonomous vessels by supplementing and fusing existing solutions rather than developing new technologies are proposed. In conclusion, the entire network consists of VDI and presents additional configurations to ensure confidentiality, integrity, and availability, which are the three security elements. According to the composition of such a convergence network, it is intended to help prepare countermeasures to protect internal data from external threats.

• Educational Technology International
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• v.6 no.1
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• pp.65-79
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• 2005

In a widely distributed environment, many occasions arise when people need to filter informationwith email clients. The existing information agents such as Maxims and Message Assistant have capabilities of filtering email messages either by an autonomous agent or by user-defined rules. FlexMA, a variation of FAMES (Flexible Asynchronous Messaging System) is proposed as an information filtering agent. Agents in our system can be scaled up to adapt user's various demands by controlling messages delivered among heterogeneous email clients. Several functionalities are split into each agent in terms of component configuration with the addition of multiple agents'cooperation and negotiation. User-defined rules are collected and executed by these agents in a semi-autonomous manner. This paper demonstrates how this design is feasible in a flexible message system.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.53.3-53
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• 2002

1. Introdition : The necessity of DGPS data compensation. 2. Configuration of the GPS and coordinates conversion 2-1. Coordinates conversion of CCD 3. Vehicle Model and Evaluation 4. Accurate error position algorithm. 5. Experiment and result. 6. Conclusion: It was possible that we converted the CCD data into the GPS coordinates data.

• Korean Journal of Agricultural Science
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• v.47 no.2
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• pp.205-217
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• 2020

In this study, a deep learning-based tillage boundary detection method for autonomous tillage by a tractor was developed, which consisted of image cropping, object classification, area segmentation, and boundary detection methods. Full HD (1920 × 1080) images were obtained using a RGB camera installed on the hood of a tractor and were cropped to 112 × 112 size images to generate a dataset for training the classification model. The classification model was constructed based on convolutional neural networks, and the path boundary was detected using a probability map, which was generated by the integration of softmax outputs. The results show that the F1-score of the classification was approximately 0.91, and it had a similar performance as the deep learning-based classification task in the agriculture field. The path boundary was determined with edge detection and the Hough transform, and it was compared to the actual path boundary. The average lateral error was approximately 11.4 cm, and the average angle error was approximately 8.9°. The proposed technique can perform as well as other approaches; however, it only needs low cost memory to execute the process unlike other deep learning-based approaches. It is possible that an autonomous farm robot can be easily developed with this proposed technique using a simple hardware configuration.

• Smart Structures and Systems
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• v.31 no.4
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• pp.335-349
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• 2023

Bridges constantly undergo deterioration and damage, the most common ones being concrete damage and exposed rebar. Periodic inspection of bridges to identify damages can aid in their quick remediation. Likewise, identifying components can provide context for damage assessment and help gauge a bridge's state of interaction with its surroundings. Current inspection techniques rely on manual site visits, which can be time-consuming and costly. More recently, robotic inspection assisted by autonomous data analytics based on Computer Vision (CV) and Artificial Intelligence (AI) has been viewed as a suitable alternative to manual inspection because of its efficiency and accuracy. To aid research in this avenue, this study performs a comparative assessment of different architectures, loss functions, and ensembling strategies for the autonomous segmentation of bridge components and damages. The experiments lead to several interesting discoveries. Nested Reg-UNet architecture is found to outperform five other state-of-the-art architectures in both damage and component segmentation tasks. The architecture is built by combining a Nested UNet style dense configuration with a pretrained RegNet encoder. In terms of the mean Intersection over Union (mIoU) metric, the Nested Reg-UNet architecture provides an improvement of 2.86% on the damage segmentation task and 1.66% on the component segmentation task compared to the state-of-the-art UNet architecture. Furthermore, it is demonstrated that incorporating the Lovasz-Softmax loss function to counter class imbalance can boost performance by 3.44% in the component segmentation task over the most employed alternative, weighted Cross Entropy (wCE). Finally, weighted softmax ensembling is found to be quite effective when used synchronously with the Nested Reg-UNet architecture by providing mIoU improvement of 0.74% in the component segmentation task and 1.14% in the damage segmentation task over a single-architecture baseline. Overall, the best mIoU of 92.50% for the component segmentation task and 84.19% for the damage segmentation task validate the feasibility of these techniques for autonomous bridge component and damage segmentation using RGB images.