Damage states of an underground tunnel structure need to be defined in the estimation of its seismic fragility. They are identified in this paper by applying pushover analyses of an typical tunnel structure. Latin Hypercube sampling (LHS) technique is used to explicitly consider uncertainties in the associated design variables.

In this paper, a localization error recovery method based on bias estimation is provided for outdoor localization of mobile robot using different-type sensors. In the previous data integration method with DGPS, it is difficult to localize mobile robot due to multi-path phenomena of DGPS. In this paper, fault data due to multi-path phenomena can be recovered by bias estimation. The proposed data integration method uses a Kalman filter based estimator taking into account a bias estimator and a free-bias estimator. A performance evaluation is shown through an outdoor experiment using mobile robot.

In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) operating in container terminals is presented. The developed navigation algorithm takes the form of a federated information filter used to detect other AGVs and avoid obstacles using fused information from multiple sensors. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. It is proved that the information state and the information matrix of the suggested filter, which are weighted in terms of an information sharing factor, are equal to those of a centralized information filter under the regular conditions. Numerical examples using Monte Carlo simulation are provided to compare the centralized information filter and the proposed one.