• 드라이브 ㆍ 컨트롤
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• 제19권4호
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• pp.110-116
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• 2022

Recently, various studies have been conducted to apply deep learning and AI to various fields of autonomous driving, such as recognition, sensor processing, decision-making, and control. This paper proposes a controller applicable to path following, static obstacle avoidance, and pedestrian avoidance situations by utilizing reinforcement learning in autonomous vehicles. For repetitive driving simulation, a reinforcement learning environment was constructed using virtual environments. After learning path following scenarios, we compared control performance with Pure-Pursuit controllers and Stanley controllers, which are widely used due to their good performance and simplicity. Based on the test case of the KNCAP test and assessment protocol, autonomous emergency steering scenarios and autonomous emergency braking scenarios were created and used for learning. Experimental results from zero collisions demonstrated that the reinforcement learning controller was successful in the stationary obstacle avoidance scenario and pedestrian collision scenario under a given condition.

• 한국멀티미디어학회논문지
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• 제13권12호
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• pp.1820-1831
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• 2010

When interacting with unknown environments, an autonomous agent needs to decide which action or action order can result in a good state and determine the transition probability based on the current state and the action taken. The traditional multiple sequential learning model requires predefined probability of the states' transition. This paper proposes a multiple sequential learning and prediction system with definition of autonomous states to enhance the automatic performance of existing AI algorithms. In sequence learning process, the sensed states are classified into several group by a set of proposed motivation filters to reduce the learning computation. In prediction process, the learning agent makes a decision based on the estimation of each state's cost to get a high payoff from the given environment. The proposed learning and prediction algorithms heightens the automatic planning of the autonomous agent for interacting with the dynamic unknown environment. This model was tested in a virtual library.

• International Journal of Control, Automation, and Systems
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• 제6권2호
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• pp.282-287
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• 2008

In distributed mobile robot systems, autonomous robots accomplish complicated tasks through intelligent cooperation with each other. This paper presents behavior learning and online distributed evolution for cooperative behavior of a group of autonomous robots. Learning and evolution capabilities are essential for a group of autonomous robots to adapt to unstructured environments. Behavior learning finds an optimal state-action mapping of a robot for a given operating condition. In behavior learning, a Q-learning algorithm is modified to handle delayed rewards in the distributed robot systems. A group of robots implements cooperative behaviors through communication with other robots. Individual robots improve the state-action mapping through online evolution with the crossover operator based on the Q-values and their update frequencies. A cooperative material search problem demonstrated the effectiveness of the proposed behavior learning and online distributed evolution method for implementing cooperative behavior of a group of autonomous mobile robots.

• 정보교육학회논문지
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• 제17권4호
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• pp.507-513
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• 2013

본 연구는 사이버학습에서 온라인 학습매체와 서책형 학습매체를 활용했을 때의 자율적 학습 효과성을 비교 분석하는 연구이다. 본 연구에서는 같은 학습 내용의 온 오프라인 학습 매체를 동질 집단에 제공하고, 통제된 환경에서 자율적인 학습을 시행한 후 학습 결과물인 객관적 성적 자료를 수집하여 비교 분석하는 연구 방법을 사용하였다. 전반기와 후반기 학습 결과를 취합하여 자료를 분석한 결과 두 집단 간의 유의미한 차이를 확인할 수 있었으며, 온라인 매체를 이용한 학습이 오프라인 인쇄 매체를 이용한 학습보다 자율적인 학습 면에서 효과성이 높다고 볼 수 없다는 결론을 얻게 되었다. 이에 온라인 학습의 자율적인 학습 효과성을 높이기 위한 방안을 제안하고자 한다.

• 로봇학회논문지
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• 제14권2호
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• pp.122-130
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• 2019

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

• 전자통신동향분석
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• 제35권6호
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• pp.1-11
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• 2020

Recently, autonomous things, which are pieces of equipment or devices that grasp the context of circumstances on their own and perform actions appropriate for the situation in the surrounding environment, are attracting much research interest. This is because autonomous things are expected to be able to interact with humans more naturally, supersede humans in many tasks, and further solve problems by themselves by collaborating with each other without human intervention. This prospect leans heavily on AI as deep learning has delivered astonishing breakthroughs recently and broadened its range of applications. This paper surveys application trends in deep learning-based AI techniques for autonomous things, especially autonomous driving vehicles, because they present a wide range of problems involving perception, decision, and actions that are very common in other autonomous things.

• 전자공학회논문지S
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• 제34S권8호
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• pp.56-64
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• 1997

In distributed autonomous robotic systems, each robot must behaves by itself according to the its states and environements, and if necessary, must cooperates with other orbots in order to carray out a given task. Therefore it is essential that each robot has both learning and evolution ability to adapt the dynamic environments. In this paper, the new learning and evolution method based on reinforement learning having delayed reward ability and distributed genectic algorithms is proposed for behavior learning and evolution of collective autonomous mobile robots. Reinforement learning having delayed reward is still useful even though when there is no immediate reward. And by distributed genetic algorithm exchanging the chromosome acquired under different environments by communication each robot can improve its behavior ability. Specially, in order to improve the perfodrmance of evolution, selective crossover using the characteristic of reinforcement learning is adopted in this paper, we verify the effectiveness of the proposed method by applying it to cooperative search problem.

• International Journal of Internet, Broadcasting and Communication
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• 제16권1호
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• pp.63-71
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• 2024

Deep reinforcement learning (RL) significantly influences autonomous vehicle development by optimizing decision-making and adaptation to complex driving environments through simulation-based training. In deep RL, an activation function is used, and various activation functions have been proposed, but their performance varies greatly depending on the application environment. Therefore, finding the optimal activation function according to the environment is important for effective learning. In this paper, we analyzed nine commonly used activation functions for RL to compare and evaluate which activation function is most effective when using deep RL for autonomous vehicles to learn highway merging. To do this, we built a performance evaluation environment and compared the average reward of each activation function. The results showed that the highest reward was achieved using Mish, and the lowest using SELU. The difference in reward between the two activation functions was 10.3%.

• ETRI Journal
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• 제45권5호
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• pp.735-745
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• 2023

This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.

• Corrosion Science and Technology
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• 제18권6호
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• pp.253-257
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• 2019

A deep learning based real-time painting surface inspection algorithm is proposed herein, designed for developing an autonomous inspection drone. The painting surface inspection is usually conducted manually. However, the manual inspection has a limitation in obtaining accurate data for correct judgement on the surface because of human error and deviation of individual inspection experiences. The best method to replace manual surface inspection is the vision-based inspection method with a camera, using various image processing algorithms. Nevertheless, the visual inspection is difficult to apply to surface inspection due to diverse appearances of material, hue, and lightning effects. To overcome technical limitations, a deep learning-based pattern recognition algorithm is proposed, which is specialized for painting surface inspections. The proposed algorithm functions in real time on the embedded board mounted on an autonomous inspection drone. The inspection results data are stored in the database and used for training the deep learning algorithm to improve performance. The various experiments for pre-inspection of painting processes are performed to verify real-time performance of the proposed deep learning algorithm.