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굴삭기를 위한 레이저 스캐너 기반 확률 및 예견 작업 위험도 평가 알고리즘 개발
오광석,박성렬,서자호,이근호,이경수,Oh, Kwang Seok,Park, Sung Youl,Seo, Ja Ho,Lee, Geun Ho,Yi, Kyong Su 유공압건설기계학회 2016 드라이브·컨트롤 Vol.7 No.4
This paper presents a stochastic and predictive working-risk-assessment algorithm for excavators based on a one-layer laser scanner. The one-layer laser scanner is employed to detect objects and to estimate an object's dynamic behaviors such as the position, velocity, heading angle, and heading rate. To estimate the state variables, extended and linear Kalman filters are applied in consideration of laser-scanner information as the measurements. The excavator's working area is derived based on a kinematic analysis of the excavator's working parts. With the estimated dynamic behaviors and the kinematic analysis of the excavator's working parts, an object's behavior and the excavator's working area such as the maximum, actual, and predicted areas are computed for a working risk assessment. The four working-risk levels are defined using the predicted behavior and the working area, and the intersection-area-based quantitative-risk level has been computed. An actual test-data-based performance evaluation of the designed stochastic and predictive risk-assessment algorithm is conducted using a typical working scenario. The results show that the algorithm can evaluate the working-risk levels of the excavator during its operation.
적응형 슬라이딩 모드 제어를 이용한 위상 궤적 해석 기반 굴삭기의 안전제어 알고리즘 개발
오광석(Kwang Seok Oh),서자호(Ja Ho Seo),이근호(Geun Ho Lee) 유공압건설기계학회 2018 드라이브·컨트롤 Vol.15 No.3
This paper presents a phase portrait analysis–based safety control algorithm for excavators, using adaptive sliding mode control. Since working postures and material types cause the excavator"s rotational inertia to vary, the rotational inertia was estimated, and this estimation was used to design an adaptive sliding mode controller for collision avoidance of the excavator. In order to estimate the rotational inertia, the recursive least-squares estimation with multiple forgetting was applied with the information of the swing velocity of the excavator. For realistic evaluation, an actual working scenario–based performance evaluation was conducted. Based on the estimated rotational inertia and an analysis of estimation errors, sliding mode control inputs were computed. The actual working scenario–based performance evaluation of the designed safety algorithm was conducted, and the results showed that the developed safety control algorithm can efficiently avoid a collision with an object in consideration of rotational inertia variations.
오명식(Myeong Sik Oh),서자호(Ja Ho Seo),정슬(Seul Jung) 제어로봇시스템학회 2016 제어·로봇·시스템학회 논문지 Vol.22 No.12
This paper presents the implementation and control of a small-scaled excavator system. The commercial miniature of an excavator system has been modified and its control hardware is embedded to access the feedback control. Encoder sensors are attached to the joint and a force sensor is mounted on the end-effector so that feedback position control is accessible as well as force control. The dynamic model of the excavator system is derived as a four linkage robot arm and its control performances are simulated. Experimental studies of contact force control tasks are conducted to test the control algorithm for the excavator system.
굴삭기를 위한 레이저 스캐너 기반 확률 및 예견 작업 위험도 평가 알고리즘 개발
오광석(Kwang Seok Oh),박성렬(Sung Youl Park),서자호(Ja Ho Seo),이근호(Geun Ho Lee),이경수(Kyong Su Yi) 유공압건설기계학회 2016 드라이브·컨트롤 Vol.13 No.4
This paper presents a stochastic and predictive working-risk-assessment algorithm for excavators based on a one-layer laser scanner. The one-layer laser scanner is employed to detect objects and to estimate an object’s dynamic behaviors such as the position, velocity, heading angle, and heading rate. To estimate the state variables, extended and linear Kalman filters are applied in consideration of laser-scanner information as the measurements. The excavator’s working area is derived based on a kinematic analysis of the excavator’s working parts. With the estimated dynamic behaviors and the kinematic analysis of the excavator’s working parts, an object’s behavior and the excavator’s working area such as the maximum, actual, and predicted areas are computed for a working risk assessment. The four working-risk levels are defined using the predicted behavior and the working area, and the intersection-area-based quantitative-risk level has been computed. An actual test-data-based performance evaluation of the designed stochastic and predictive risk-assessment algorithm is conducted using a typical working scenario. The results show that the algorithm can evaluate the working-risk levels of the excavator during its operation.