Application Of Kalman Filter In Navigation Process Of Automated Guided Vehicles

Abstract

In the paper an example of application of the Kalman filtering in the navigation process of automatically guided vehicles was presented. The basis for determining the position of automatically guided vehicles is odometry -the navigation calculation. This method of determining the position of a vehicle is affected by many errors. In order to eliminate these errors, in modern vehicles additional systems to increase accuracy in determining the position of a vehicle are used. In the latest navigation systems during route and position adjustments the probabilistic methods are used. The most frequently applied are Kalman filters.

Reliability of Pulse Measurements in Videoplethysmography

“…In the future it would be also interesting to verify DFT-based methods that can improve the estimation accuracy in the case of a low signal-to-noise ratio (e.g. [26]) and to apply the Kalman filter [27].…”

Selection of Filtration Methods in the Analysis of Motion of Automated Guided Vehicle

“…An example run registered during the motion using laser rangefinders is shown in Fig.2. [17]. The determination on a straight test section of radial motion on the curvature of the course will make it possible to determine amendments to the calculated algorithm for the correction of the course.…”

“…The basis for determining the current position in the majority of cases is odometry -computational navigation. This method of determining a vehicle's position is laden with many errors [8]- [11], [14], [16] and requires correction [17]. In the process of correction, a range of measurement methods is used.…”

“…The results of the measurements are burdened with distortions; therefore, they must be filtered [17]. Currently, there are many techniques for data filtering.…”

Yaw Stability Control of Unmanned Emergency Supplies Transportation Vehicle Considering Two-Layer Model Predictive Control

“…Kalman filtering (KF) is a filtering algorithm that filters out various mixed noise disturbances from signal observation and estimates the required signal, which is widely used in intelligent vehicle fields, such as positioning [23], parameter identification [24,25], target tracking [26,27], etc. Both the KF [28,29] and the extended Kalman filter (EKF) [30,31] are based on the premise that the statistical characteristics of the process noise and measurement noise of the system are known. However, vehicles are subject to sensor measurement errors, and it can be difficult to obtain statistical information about the measurement noise in real operation, which is sometimes time-variable and uncertain.…”