Localization using fuzzy and Kalman filtering data fusion (original) (raw)
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Control Engineering Practice, 1999
Accurate estimates of mobile robot location, if available, can be used to improve the performance of a vehicle dynamics control system. To this purpose, the data provided by odometric and sonar sensors are here fused together by means of an extended Kalman "lter, providing robot position and orientation estimates at each sampling instant. To cope with the tracking of long trajectories, the performance of the "lter is improved by introducing an on-line fuzzy-rule-based adaptation scheme.
A novel fuzzy Kalman filter for mobile robots localization
A new method to implement fuzzy Kalman filters is introduced in this paper. This has special application in fields where inaccurate models or sensors are involved, such as in mobile robotics. The innovation consists in using possibility distributions, instead of gaussian distributions. The main advantage of this approach is that uncertainty is not needed to be symmetric, while a region of possible solutions is allowed. The contribution of this work also includes a method to propagate uncertainty through both the process and the observation models. This one is based on quantifying uncertainty as trapezoidal possibility distributions. Finally, the way to reduce the EKF inconsistence when large number of iterations are carried out is shown.
Adaptive Fuzzy Logic System for Sensor Fusion in Dead-Reckoning Mobile Robot Navigation
IFAC Proceedings Volumes, 2002
This paper presents the sensor fusion for dead-reckoning mobile robot navigation. Odometry and sonar measurement signals are fused together using Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS). Two methods of adaptation scheme are used, the first one uses Q and R , the second one only uses Q. The first method gives faster result than the second one. The fused signal is more accurate than any of the original signals considered separately. The enhanced, more accurate signal is used to guide and navigate the robot.
Localization and controlling the mobile robot by sensory data fusion
Agricultural Engineering International: The CIGR Journal, 2019
Localization of a mobile robot with any structure, work space and task is one of the most fundamental issues in the field of robotics and the prerequisite for moving any mobile robot that has always been a challenge for researchers. In this paper, the Dempster-Shafer and Kalman filter methods are used as the two main tools for the integration and processing of sensor data in robot localization to achieve the best estimate of positioning according to the unsteady environmental conditions and a framework for GPS and IMU sensor data fusion through the Dempster-Shafer method. Also, by providing a new method, the initial weighing on each of these GPS sensors and wheel encoders is done based on the reliability of each one. The methods were compared with the simulation model and the best method was chosen in each situation. In addition to obtaining the geometric equations governing the robot, a PID controller was used for the kinematic control of the robot and implemented in the MATLAB Si...
Mobile Robot Localization Using Fuzzy Segments
International Journal of Advanced Robotic Systems, 2013
This paper presents the development of a framework based on fuzzy logic for multi-sensor fusion and localization in indoor environments. Such a framework makes use of fuzzy segments to represent uncertain location information from different sources of information. Fuzzy reasoning, based on similarity interpretation from fuzzy logic, is then used to fuse the sensory information represented as fuzzy segments. This approach makes it possible to fuse vague and imprecise information from different sensors at the feature level instead of fusing raw data directly from different sources of information. The resulting fuzzy segments are used to maintain a coherent representation of the environment around the robot. Such an uncertain representation is finally used to estimate the robot position. The proposed multi-sensor fusion localization approach has been validated with a mobile platform using different range sensors.
Odometry and Sonar Data Fusion for Mobile Robot Navigation
IFAC Proceedings Volumes, 2000
To solve the problems in guidance, navigation, and control for an autonomous robot, its accurate positioning and localization are needed. Two or more different sensors are often used to obtain reliable data useful for control system. Extended Kalman Filter (EKF) is widely used to fuse those data to obtain one optimal result. The signals used during navigation cannot be always considered as white noise signals. On the other hand, colored signals will cause the EKF to diverge. This paper presents the data fusion system for mobile robot navigation. Odometry and sonar signals are fused using Extended Kalman Filter (EKF) and Adaptive Fuzzy Logic System (AFLS). The AFLS was used to adapt the gain and therefore prevent the Kalman filter divergence. The fused signal is more accurate than any of the original signals considered separately. The enhanced, more accurate signal is used to guide and navigate the robot.
Extended Kalman Filter based fusion of reliable sensors using fuzzy logic
2017 Moratuwa Engineering Research Conference (MERCon), 2017
Precise localization for autonomous robots is necessary for advancement in the world of unmanned robotics. Probabilistic algorithms are used to fuse multiple position sensors in order to locate a robot. But failure of any sensor in this process drastically lowers the performance of these algorithms. Here comes the need to facilitate these probabilistic models with intelligence. This paper presents an intelligent localization technique for autonomous maneuvering of robots. Localization of the robot is done by fusing three different types of position sensors using an Extended Kalman Filter (EKF) and a Kalman Filter (KF). The fusing method is made intelligent by keeping track of the relative error among the sensors and deciding a reliability factor on each sensor accordingly. A Fuzzy inference model has been adopted to predict the reliability factor for each sensor. According to the predicted reliability of each sensor, an error covariance matrix is set up, which is fed into the traditional KF and EKF algorithms. This helps the fusion algorithms to fuse the sensors intelligently and the final output is more accurate. A high precision localization is achieved by this intelligent method of fusing. A simulation is carried out in MATLAB considering three position sensors. The simulation is validated by making one of the sensors erroneous and comparing the output results of the new fusion algorithm with the traditional algorithm.
REAL TIME FILTER AND FUSION OF MULTI-SENSOR DATA FOR LOCALIZATION OF MOBILE ROBOT
This project work presents the sensor fusion of Global Positioning System (GPS), Inertial Measurement Unit (IMU) and Odometry data from wheel encoders which is used to estimate localization of mobile robot. GPS, IMU, Wheel encoders are interfaced with MBED. Filters are used to remove erroneous noise from the data obtained from sensors. Low pass IIR filter is used for Differential Global Positioning System (DGPS) data, Complementary filter for IMU data. The project work discusses each of these approaches for Real time filtering of fusion of sensor in an Outdoor environment. The above Fusion algorithm is implemented on MBED Platform.
Mobile robot localization using fuzzy neural network based extended Kalman filter
2012 IEEE International Conference on Control System, Computing and Engineering, 2012
This paper proposes a novel approach to improve the performance of the extended Kalman filter (EKF) for the problem of mobile robot localization. A fuzzy logic system is employed to continuously adjust the noise covariance matrices of the filter. A neural network is implemented to regulate the membership functions of the antecedent and consequent parts of the fuzzy rules. The aim is to gain the accuracy and avoid the divergence of the EKF when the noise covariance matrices are fixed or wrongly determined. Simulations and experiments have been conducted. The results show that the proposed filter is better than the EKF in localizing the mobile robot.