A Solution to Partial Observability in Extended Kalman Filter Mobile Robot Navigation

Abstract

Partial ob servab ility in EKF b ased mob ile rob ot navigation is investigated in this paper to find a solution that can prevent erroneous estimation. By only considering certain landmarks in an environment, the computational cost in mob ile robot can b e reduced b ut with an increase of uncertainties to the system. This is known as sub optimal condition of the system. Fuzzy Logic technique is proposed to ensure that the estimation achieved desired performance even though some of the landmarks were excluded for references. The Fuzzy Logic is applied to the measurement innovation of Kalman Filter to correct the positions of b oth mob ile rob ot and any ob served landmarks during ob servations. The simulation results shown that the proposed method is capab le to secure reliab le estimation results even a numb er of landmarks b eing excluded from Kalman Filter update process in b oth Gaussian and non -Gaussian noise conditions.

Pulse-width modulation direct torque control induction motor drive with Kalman filter

“…The ITAE index has the advantages of producing smaller overshoots and oscillations than the integral of the absolute error (IAE) or the integral square error (ISE) indices [34]. In this situation, it is modified according to (14): It is easy to see that starting duration for NF is shorter than that for KF (see Figure 3). The reason for this problem is Kalman filter reduces ripple of stator current (see Figure 4) and even ripple of stator flux (see Figure 5), and therefore, it lengthens process of reaching rated value of stator flux and limit of stator current.…”

“…Kalman filter (KF) which was invented 60 years ago [10,11], was applied to smooth noised quantities in various fields [12] for example charging state estimation of large-scale battery energy storage systems [13], mobile robot navigation [14], impedance parameters estimation for medium transmission line [15], estimation of the angle between receiver orientation and receiver-transmitter line in LED communication system [16], wildfire progress estimation [17], dimension reduction in X-ray reconstructions of undersampled dynamic X-ray tomography system [18]. The Kalman filter brings the optimal estimators for linear systems with additive independent Gaussian process and measurement noises.…”

Robot Keseimbangan dengan Kendali PID dan Kalman Filter

“…Terdapat beberapa macam pengembangan dari kalman filter seperti Extended Kalman Filter (EKF) [15], Unscented Kalman Filter (UKF) [16], Ensemble Kalman Filter (EnKF) [17]. Kelebihan lain Kalman filter adalah telah banyak diterapkan pada suatu sistem seperti pada Quadrotor [18] [19], Kesehatan [20], AUV [21], Magnetic Levitation Ball [22] dan Robot [23].…”

Maximum likelihood estimation-assisted ASVSF through state covariance-based 2D SLAM algorithm

“…The most popular one is extended kalman filter which has the basic task to update the state and covariance with an assumption all the related variable comply with Gaussian distribution. Generally, extended kalman filter [11,[19][20][21][22][23] is known as an nonlinear version of its predecessor named kalman filter [18][19][20][24][25][26][27][28]. The easiness to apply extended kalman filter makes it has been widely used to solve in many different fields such as for the state and parameter estimation including SLAM, signal processing, fault detection and diagnosis and target tracking [29].…”