굴삭기를 위한 레이저 스캐너 기반 확률 및 예견 작업 위험도 평가 알고리즘 개발

Abstract

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 excavators working area is derived based on a kinematic analysis of the excavators working parts. With the estimated dynamic behaviors and the kinematic analysis of the excavators 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.