• Journal of Digital Convergence
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• v.20 no.5
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• pp.463-476
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• 2022

After industrialization in the past, the automobile industry has continued to grow centered on internal combustion engines, but is facing a major change with the recent 4th industrial revolution. Most companies are preparing for the transition to electric vehicles and autonomous driving. Therefore, in this study, topic modeling was performed based on LDA algorithm by collecting 4,002 domestic papers and 68,372 overseas papers that contain keywords related to CASE (Connectivity, Autonomous, Sharing, Electrification), which represent future automobile trends. As a result of the analysis, it was found that domestic research mainly focuses on macroscopic aspects such as traffic infrastructure, urban traffic efficiency, and traffic policy. Through this, the government's technical support for MaaS (Mobility-as-a-Service) is required in the domestic shared car sector, and the need for data opening by means of transportation was presented. It is judged that these analysis results can be used as basic data for the future automobile industry.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.4
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• pp.110-126
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• 2024

Several studies have focused on developing the safest and most efficient path from the current location to the available parking area for vehicles entering a parking lot. In the present study, the parking lot structure and parking environment such as the lane width, width, and length of the parking space, were vaired by referring to the actual parking lot with vertical and horizontal parking. An automatic parking path planning model was proposed by collecting path data by various setting angles and environments such as a starting point and an arrival point, by putting the collected data into a deep learning model. The existing algorithm(Hybrid A-star, Reeds-Shepp Curve) and the deep learning model generate similar paths without colliding with obstacles. The distance and the consumption time were reduced by 0.59% and 0.61%, respectively, resulting in more efficient paths. The switching point could be decreased from 1.3 to 1.2 to reduce driver fatigue by maximizing straight and backward movement. Finally, the path generation time is reduced by 42.76%, enabling efficient and rapid path generation, which can be used to create a path plan for autonomous parking during autonomous driving in the future, and it is expected to be used to create a path for parking robots that move according to vehicle construction.

• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.137-149
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• 2020

Several multi-agent reinforcement learning (MARL) algorithms have achieved overwhelming results in recent years. They have demonstrated their potential in solving complex problems in the field of real-time strategy online games, robotics, and autonomous vehicles. However these algorithms face many challenges when dealing with massive problem spaces in sparse reward environments. Based on the centralized training and decentralized execution (CTDE) architecture, the MARL algorithms discussed in the literature aim to solve the current challenges by formulating novel concepts of inter-agent modeling, credit assignment, multiagent communication, and the exploration-exploitation dilemma. The fundamental objective of this paper is to deliver a comprehensive survey of existing MARL algorithms based on the problem statements rather than on the technologies. We also discuss several experimental frameworks to provide insight into the use of these algorithms and to motivate some promising directions for future research.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.9
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• pp.795-802
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• 2002

We present a new dynamic routing scheme for multiple autonomous guided vehicles (AGVs) systems. There have been so many results concerned with scheduling and routing of multiple AGV systems; however, most of them are only applicable to systems with a small number of AGVs under a low degree of concurrency. With an increased number of AGVs in recent applications, these AGV systems are faced with another problem that has never been occurred in a system with a small number AGVs. This is the stop propagation problem. That is, if a leading AGV stops then all the following AGVs must stop to avoid any collision. In order to resolve this problem, we propose a nonstop preferential detour (NPD) algorithm which is a new dynamic routing scheme employing an election algorithm. For real time computation, we introduce two stage control scheme and propose a new path searching scheme, k-via shortest path scheme for an efficient dynamic routing algorithm. Finally, the proposed new dynamic routing scheme is illustrated by an example.

• Ocean Systems Engineering
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• v.9 no.4
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• pp.391-407
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• 2019

Tracking the location (position) of a surface or underwater marine vehicle is important as part of guidance and navigation. While the Global Positioning System (GPS) works well in an open sea environment but its use is limited whenever testing scaled-down models of such vehicles in the laboratory environment. This paper presents the design, development and implementation of a low energy ultrasonic augmented single beacon-based localization technique suitable for such requirements. The strategy consists of applying Extended Kalman Filter (EKF) to achieve location tracking from basic dynamic distance measurements of the moving model from a fixed beacon, while on-board motion sensor measures heading angle and velocity. Iterative application of the Extended Kalman Filter yields x and y co-ordinate positions of the moving model. Tests performed on a free-running ship model in a wave basin facility of dimension 30 m by 30 m by 3 m water depth validate the proposed model. The test results show quick convergence with an error of few centimeters in the estimated position of the ship model. The proposed technique has application in the real field scenario by replacing the ultrasonic sensor with industrial grade long range acoustic modem. As compared with the existing systems such as LBL, SBL, USBL and others localization techniques, the proposed technique can save deployment cost and also cut the cost on number of acoustic modems involved.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.6
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• pp.810-820
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• 2014

Recently many studies of Radar systems mounted on ground vehicles for autonomous driving, SLAM (Simultaneous localization and mapping) and collision avoidance are reported. In near field, several hits per an object are generated after signal processing of Radar data. Hence, clustering is an essential technique to estimate their shapes and positions precisely. This paper proposes a method of grouping hits in range-doppler domains into clusters which represent each object, according to the pre-defined rules. The rules are based on the perceptual cues to separate hits by object. The morphological connectedness between hits and the characteristics of SNR distribution of hits are adopted as the perceptual cues for clustering. In various simulations for the performance assessment, the proposed method yielded more effective performance than other techniques.

• Journal of Ocean Engineering and Technology
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• v.25 no.1
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• pp.27-31
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• 2011

The previously developed method of estimating the propulsion performance of a UUV was applied to the high speed UUV, which is propelled by commercial thrusters. The thrusters were selected with an overdesign mentality; in other words, their capacities were excessive. At that point, the designer's concern was focused on a question regarding at what rpm the UUV could reach the design speed. Because the developed method required thrust coefficient curve data, the researchers asked for the POW data of the thrusters from the manufacturer. From the data, the researchers extracted the thrust coefficient and estimated the rpm value of design speed for the UUV. Finally, the researchers compared the estimated value and the result from a self-propulsion test using a VPMM (Vertical planar motion mechanism) test at a towing tank in MOERI.

• Advances in aircraft and spacecraft science
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• v.7 no.1
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• pp.53-78
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• 2020

Automatic trajectory planning is an important task that will have to be performed by truly autonomous vehicles. The main method proposed, for unmanned airplanes to do this, consists in concatenating elementary segments of trajectories such as rectilinear, circular and helical segments. It is argued here that because these cannot be expected to all be flyable at a same constant speed, it is necessary to consider segments on which the airplane accelerates or decelerates. In order to preserve the planning advantages that result from having the speed constant, it is proposed to do all speed changes at maximum deceleration or acceleration, so that they are as brief as possible. The constraints on the load factor, the lift and the power required for the motion are derived. The equation of motion for such accelerated motions is solved numerically. New results are obtained concerning the value of the angle and the speed for which the longest distance and the longest duration glides happen, and then for which the steepest, the fastest and the most fuel economical climbs happen. The values obtained differ from those found in most airplane dynamics textbooks. Example of tables are produced that show how general speed changes can be effected efficiently; showing the time required for the changes, the horizontal distance traveled and the amount of fuel required. The results obtained apply to all internal combustion engine-propeller driven airplanes.

• Journal of the Korea Society for Simulation
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• v.27 no.2
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• pp.101-114
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• 2018

Several different approaches and mechanisms are introduced to solve the UAV path planning problem. In this paper, we designed and implemented an agent-based simulation software using the Repast platform and Java Genetic Algorithm Package to examine an evolutionary path planning method by implementing and testing within the Repast environment. The paper demonstrates the life-cycle of an agent-based simulation software engineering project while providing a documentation strategy that allows specifying autonomous, adaptive, and interactive software entities in a Multi-Agent System. The study demonstrates how evolutionary path planning can be introduced to improve cognitive agent capabilities within an agent-based simulation environment.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.463-467
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• 2005

In this paper, the guidance law applicable to formation flight of UAV in three-dimensional space is proposed. The concept of miss distance, which is commonly used in the missile guidance laws, and Lyapunov stability theorem are effectively combined to obtain the guidance commands of the wingmen. The propose guidance law is easily integrated into the existing flight control system because the guidance commands are given in terms of velocity, flight path angle and heading angle to form the prescribed formation. In this guidance law, communication is required between the leader and the wingmen to achieve autonomous formation. The wingmen are only required the current position and velocity information of the leader vehicle. The performance of the proposed guidance law is evaluated using the complete nonlinear 6-DOF aircraft system. This system is integrated with nonlinear aerodynamic and engine characteristics, actuator servo limitations for control surfaces, various stability and control augmentation system, and autopilots. From the nonlinear simulation results, the new guidance law for formation flight shows that the vehicles involved in formation flight are perfectly formed the prescribed formation satisfying the several constraints such as final velocity, flight path angle, and heading angle.