• Journal of Positioning, Navigation, and Timing
• /
• v.12 no.1
• /
• pp.67-73
• /
• 2023

As interest in underwater structures and ocean exploration increases, many researchers are proposing methods for modeling and controlling various remotely operated vehicles (ROVs). Recently, hybrid systems composed of an autonomous underwater vehicle and an ROV capable of remote control and autonomous navigation are being developed. In this study we introduce a method that models Ariari-aROV, an ROV consisting of five thrusters, and performs navigation. The proposed ROV can be controlled manually and by autonomous navigation when given a target point. An extended Kalman filter is utilized for sensor measurement correction for more precise navigation. The proposed method is verified through a simulation.

• The Journal of Korea Robotics Society
• /
• v.17 no.3
• /
• pp.334-338
• /
• 2022

In visual simultaneous localization and mapping (vSLAM), the correct recognition of a place benefits in relocalization and improved map accuracy. However, its performance is significantly affected by the environmental conditions such as variation in light, viewpoints, seasons, and presence of dynamic objects. This research addresses the problem of feature occlusion caused by interference of dynamic objects leading to the poor performance of visual place recognition algorithm. To overcome the aforementioned problem, this research analyzes the role of scene semantics in correct detection of a place in challenging environments and presents a semantics aided visual place recognition method. Semantics being invariant to viewpoint changes and dynamic environment can improve the overall performance of the place matching method. The proposed method is evaluated on the two benchmark datasets with dynamic environment and seasonal changes. Experimental results show the improved performance of the visual place recognition method for vSLAM.

• IEMEK Journal of Embedded Systems and Applications
• /
• v.18 no.2
• /
• pp.81-87
• /
• 2023

With the development of BCI devices, it is now possible to use EEG control technology to move the robot's arms or legs to help with daily life. In this paper, we propose a customized vehicle control model based on BCI. This is a model that collects BCI-based driver EEG signals, determines information according to EEG signal analysis, and then controls the direction of the vehicle based on the determinated information through EEG signal analysis. In this case, in the process of analyzing noisy EEG signals, controlling direction is supplemented by using a camera-based eye tracking method to increase the accuracy of recognized direction . By synthesizing the EEG signal that recognized the direction to be controlled and the result of eye tracking, the vehicle was controlled in five directions: left turn, right turn, forward, backward, and stop. In experimental result, the accuracy of direction recognition of our proposed model is about 75% or higher.

• Steel and Composite Structures
• /
• v.47 no.4
• /
• pp.513-521
• /
• 2023

The goal of this work is to create a new and improved GWO (Grey Wolf Optimizer), the so-called Robot GWO (RGWO), for dynamic and static target tracking involving multiple robots in unknown environmental conditions. From applying ourselves with the Gray Wolf Optimization Algorithm (GWO) and how it works, as the name suggests, it is a nature-inspired metaheuristic based on the behavior of wolf packs. Like other nature-inspired metaheuristics such as genetic algorithms and firefly algorithms, we explore the search space to find the optimal solution. The results also show that the improved optimal control method can provide superior power characteristics even when operating conditions and design parameters are changed.

• Journal of Sensor Science and Technology
• /
• v.32 no.6
• /
• pp.475-480
• /
• 2023

When modeling a sensor system mathematically, we assume that the sensor noise is Gaussian and white to simplify the model. If this assumption fails, the performance of the sensor model-based controller or estimator degrades due to incorrect modeling. In practice, non-Gaussian or non-white noise sources often arise in many digital sensor systems. Additionally, the noise parameters of the sensor model are not known in advance without additional noise statistical information. Moreover, disturbances or high nonlinearities often cause unknown sensor modeling errors. To estimate the uncertain noise and model parameters of a sensor system, this paper proposes an iterative batch calibration method using data-driven machine learning. Our simulation results validate the calibration performance of the proposed approach.

• Annual Conference of KIPS
• /
• 2023.11a
• /
• pp.748-749
• /
• 2023

본 논문은 평탄한 지형뿐만이 아닌 턱과 계단 같은 비평탄 요소가 있는 지형에서도 주행이 가능한 바퀴 구동형 지능 로봇 설계를 목적으로 지형 극복 기능을 구현하기 위한 구동 방식을 크랭크의 원리를 이용한 기어 구조를 이용했고, 지능로봇의 지능적 요소를 구현하기 위해 구성된 임베디드 시스템에 대해 정리한 논문이다.

• Journal of Industrial Technology
• /
• v.43 no.1
• /
• pp.49-55
• /
• 2023

Cluster tools are extensively employed in various wafer fabrication processes within the semiconductor manufacturing industry, including photo lithography, etching, and chemical vapor deposition. Contemporary fabrication facilities encounter customer orders with technical specifications that are similar yet slightly varied. Consequently, modern fabrications concurrently manufacture two or three different wafer types using a cluster tool to maximize chamber utilization and streamline the flow of wafer lots between different process stages. In this review, we introduce two methods of concurrent processing of multiple wafer types: 1) concurrent processing of multiple wafer types with different job flows, 2) concurrent processing of multiple wafer types with identical job flows. We describe relevant research trends and achievements and discuss future research directions.

• Journal of information and communication convergence engineering
• /
• v.22 no.1
• /
• pp.70-79
• /
• 2024

Bad weather conditions such as haze lead to a significant lack of visibility in images, which can affect the functioning and reliability of image processing systems. Accordingly, various single-image dehazing (SID) methods have recently been proposed. Existing SID methods have introduced effective visibility improvement algorithms, but they do not reflect the image's perspective, and thus have limitations that distort the sky area and nearby objects. This study proposes a new SID method that reflects the sense of space by defining the correlation between image brightness and haze. The proposed method defines the haze intensity by calculating the airlight brightness deviation and sets the weight factor of the depth map by classifying images based on the defined haze intensity into images with a large sense of space, images with high intensity, and general images. Consequently, it emphasizes the contrast of nearby images where haze is present and naturally smooths the sky region to preserve the image's perspective.

• International conference on construction engineering and project management
• /
• 2024.07a
• /
• pp.1041-1048
• /
• 2024

In this study, we aim to enhance the navigation method for autonomous systems, including various types of robots and UAVs. Previous work has made progress in enhancing the autonomous flight capabilities of UAVs through both simulation and real-world tests, yet it lacked detailed insights into the aspect of global route planning. To address this gap, we propose a novel method to automatically generate routes for autonomous robots using BIM and 3D city models. Specifically, we create a program that utilizes geometry and attribute information extracted from BIM and 3D City Model to compute the optimal route rapidly. The program automatically generates routes that optimize the efficiency of various autonomous robots by considering the conditions on the routes.

• Annual Conference of KIPS
• /
• 2024.10a
• /
• pp.825-826
• /
• 2024

본 연구는 TFLite 경량화 AI 모델을 활용한 자동 쓰레기 분리수거 시스템을 제안한다. 라즈베리파이 환경에서 아두이노를 통해 두 개의 로봇팔을 제어하여, 기존 방식보다 작업 효율성을 높였다. 쓰레기 종류별 데이터 분포를 일정하게 맞추고, 고품질의 데이터를 사용함으로써 모델의 인식 성능을 크게 개선하였다. 실시간 객체 인식 테스트 결과 평균 정확도는 90% 이상에 달했다. 이러한 과정을 통해 자동 분리수거 시스템의 정확성과 효율성을 한층 더 높일 수 있을 것으로 기대된다.