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A Temporal Path Planner for Solving Information Inconsistency in an Integrated Path Planner
석준홍,이준우,왕정현,이주장,이호주 제어·로봇·시스템학회 2013 International Journal of Control, Automation, and Vol.11 No.6
This paper proposes a temporal path planner (TPP) in an integrated path planner (IPP), which is composed of a global path planner (GPP) and a local path planner (LPP). The LPPs are able to avoid obstacles within the range of built-in sensors, but it is difficult to generate an efficient path outside of the sensor range and to avoid getting stuck in cul-de-sacs. The GPPs can generate efficient global paths in a target region using a built-in global map, but the accuracy is not always sufficient to avoid collision, and the performance is highly dependent upon the accuracy of the global map. A simple combination of a GPP and an LPP causes path mismatch problems due to inconsistencies between the information acquired from the local sensors and the information from the preliminary global map. When erroneous global waypoints caused by low-accuracy information or a change of terrain are given to the unmanned ground vehicle (UGV), the proposed method attempts to find a detour via the original global waypoints located nearby to accomplish successful navigation using only sensory information and the global waypoint sequence from the GPP result provided initially. The TPP includes three sub-algorithms: the Temporal Waypoint Reviser (TWR), the Temporal Map Reviser (TMR) and the Temporal Distance-Heuristic-based Decision (TDHD). The simulation results demonstrate that the performance of the TPP outperforms other planners.
석준홍,김정중,이준용,이주장,송영주,신강욱 제어·로봇·시스템학회 2014 International Journal of Control, Automation, and Vol.12 No.6
A short-term hourly water demand forecasting algorithm is needed in order to ensure a stable and safe supply of water. Unlike daily or monthly water demand forecasting, there are a large amount of fluctuation of hourly water demand. Hourly water demand is affected by short time period and abnormal data caused by the sensor, communication, and water treatment plant problems. An ef-fective refinement method that detects and corrects the abnormal data among the historical data is needed to achieve accurate and practical hourly water demand forecasting. In this paper, we suggest an abnormal data refinement out of a confidence interval (ADR-CI) method and an error percentage correction (EPC) method. These methods try to distribute and revise the incoming hourly water demand and past water demand data. The proposed methods are verified by the experiments in a real water supply plant during a year.
이동 차량의 계층적 통합 경로 계획의 경로 부조화 문제 해결을 위한 임시 경유점 수정법
이준우(Joon-Woo Lee),석준홍(Joon-Hong Seok),하정수(Jung-Su Ha),이주장(Ju-Jang Lee),이호주(Ho-Joo Lee) 제어로봇시스템학회 2012 제어·로봇·시스템학회 논문지 Vol.18 No.7
Hierarchical IPP (Integrated Path Planning) combining the GPP (Global Path Planner) and the LPP (Local Path Planner) is interesting the researches who study about the mobile vehicle in recent years. However, in this study, there is the path mismatch problem caused by the difference in the map information available to both path planners. If ever a part of the path that was found by the GPP is available to mobile vehicle, the part may be unavailable when the mobile vehicle generates the local path with its built-in sensors while the vehicle moves. This paper proposed the TWR (Temporal Waypoint Reviser) to solve the path mismatch problem of the hierarchical IPP. The results of simulation provide the performance of the IPP with the TWR by comparing with other path planners.
임성순(Sung-Soon Yim),석준홍(Joon-Hong Seok),이주장(Ju-Jang Lee) 제어로봇시스템학회 2012 제어로봇시스템학회 각 지부별 자료집 Vol.2012 No.12
Air pollution became an issue due to the rapid industrialization and the development of car industry. Emissions form automobile cause destroying ozone layer, global warming, photochemical smog to environment and cause cancer, declined nerve center, respiratory diseases to humankind. From 1960s, society has concerned about the environment problem, and since established California Air Resources Boar(CARB-1967) and Environmental Protection Agency(EPA-1970), they tighten regulation at exhaust gases. Since 1980s, three-way catalyst has been used to purify the car exhaust fumes. Characteristics of three-way catalyst are oxidize CO and HC when the air fuel ration is rich, deoxidize NOx when the air fuel ratio is lean. When air fuel ratio is nearly to stoichiometric value, the efficiency of catalyst is almost 100%. It is desirable to adjust air fuel ratio nearly stoichiometric value to reduce harmful gases. As TWC have the different characteristics and parameter changed by reason of durability, temperature and environment, the model has to corrected accurately. Gradient descent method is widely used to estimate parameters, but in this thesis we used nonlinear Kalman filter such as the extended Kalman filter(EKF) and the unscented Kalman filter(UKF) to estimate parameters and oxygen storage state. While EKF uses first order of Taylor expansion to approximate nonlinear system, UKF performs a stochastic linearization by using a weighted statistical linear regression process based on the unscented transform. The advantage of Kalman filter is it can estimate well instead of noise in system or sensor measurement. Bias compensator using polynomial regression function is also proposed to reduce bias error that occurs due to nonequilibrium effect. Finally, proposed estimation method is verified by simulations.