In this study, the problem of navigation in dynamic and unknown environment is investigated and a navigation method based on force field approach is suggested. It is assumed that the robot performs navigation in...

in many autonomous mobile applications, robots must be capable of analyzing motion of moving objects in their environment. duringmovement of robot the quality of images is affected by quakes of camera which cause high errors in image processing outputs. in thispaper, we propose a novel method to effectively overcome this problem using neural networks and kalman filtering theory. thistechnique u...

obviously navigation is one of the most complicated issues in mobile robots.intelligent algorithms are often used for error handling in robot navigation. thispaper deals with the problem of inertial measurement unit (imu) error handling byusing extended kalman filter (ekf) as an expert algorithms. our focus is put onthe field of mobile robot navigation in the 2d environments. the main challenge...

to perform any economic management of a petroleum reservoir in real time, a predictable and/or updateable model of reservoir along with uncertainty estimation ability is required. one relatively recent method is a sequential monte carlo implementation of the kalman filter: the ensemble kalman filter (enkf). the enkf not only estimate uncertain parameters but also provide a recursive estimate of...

For high performance speed and position control of electrical drives fast online identification is needed for time-varying inertia or load conditions in combination with adaptive controllers. In this paper Extended Kalman Filters are applied and optimized for deterministic parameter variations by integrating basis function networks into the common structure of the Kalman Filter. It is shown tha...

The aim of this study is to fuse GPS(Global Positioning System)/IMU(Inertial Measurement System) by using Kalman filter. We acquired the 6D pose data and compared the accuracy of 3D world model by using the data with Kalman filter and 3D world model by without filtering. Using proposed Kalman filter method, we obtain the exact pose data. This indicates that reconstructed 3D world model, using t...

In this paper the robustness of Kalman filtering against uncertainties in process and measurement noise covariances is discussed. It is shown that a standard Kalman filter may not be robust enough if the process and measurement noise covariances are changed. A new filter is proposed which addresses the uncertainties in process and measurement noise covariances and gives better results than the ...

The Kalman filter was named after Rudolph E. Kalman published in 1960 his famous paper (Kalman, 1960) describing a recursive solution to the discrete-data linear filtering problem. There are several tutorial papers and books dealing with the subject for a great variety of applications in many areas from engineering to finance (Grewal & Andrews, 2001; Sorenson, 1970; Haykin, 2001; Bar-Shalom & L...

The xed lag Kalman smoother was proposed recently as a framework for providing retrospective data assimilation capability in atmospheric reanalysis projects Cohn et al Mon Wea Rev Retrospective data assimilation refers to the dynamically consistent incorporation of data observed well past each analysis time into each analysis Like the Kalman lter the xed lag Kalman smoother requires statistical...