| Author | Das, Tanmoy Kuma |
| Note | A thesis submitted in partial fulfillment of the requirements for the
degree of Master of Engineering in
Mechatronics |
| Publisher | Asian Institute of Technology |
| Abstract | 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 sensors using an Extended Kalman Filter (EKF). In our case, IMU is used to formulate the Inertial Navigation System (INS), GPS is used to calculate the global position of the Wheeled Mobile Robot (WMR) and optical flow sensor is used to calculate the velocity of the WMR by calculating the flow velocity of pixels. The fusing method is made intelligent by keeping track of the strength of each sensor participating in the fusion 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 conventional EKF architecture. This helps the fusion algorithm to fuse the sensors intelligently and the final output is more accurate. A high precision localization is achieved by this intelligent method of fusing. Our proposed algorithm, Kalman Intelligent Filter (KIF), is validated by localizing a WMR in both indoor and outdoor environments. |
| Year | 2017 |
| Type | Thesis |
| School | School of Engineering and Technology (SET) |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Microelectronics (ME) |
| Chairperson(s) | Abeykoon, A. M. Harsha S. |
| Examination Committee(s) | Manukid Parnichkun;Bohez, Erik L. J.; |
| Scholarship Donor(s) | AIT Fellowship; |
| Degree | Thesis (M. Eng.) -- Asian Institute of Technology, 2017 |