A solution to finite escape time in H∞ filter slam
Knowledge about the position and orientation of autonomous mobile robots is very useful in different tasks. In 1986, an available technique for designing based on the interdependence between mapping and positioning was introduced, called Synchronous Positioning and Mapping (SLAM). SLAM has been rece...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English |
| Published: |
2022
|
| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/35884/1/13.A%20solution%20to%20finite%20escape%20time%20in%20H%E2%88%9E%20filter%20slam.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Knowledge about the position and orientation of autonomous mobile robots is very useful in different tasks. In 1986, an available technique for designing based on the interdependence between mapping and positioning was introduced, called Synchronous Positioning and Mapping (SLAM). SLAM has been receiving attention from researchers for decades. However, there are still many uncertainties that need to be considered in the observation process of mobile robots. The extended Kalman filter (EKF) has become the most popular technique in SLAM, but the limitations of non-Gaussian noise environments need to be considered. Therefore, another filter that exceeds the performance of EKF, called H∞ filter (HF), may provide a better solution. HF can work in a non-Gaussian noise environment, but is limited by another problem called Finite Escape Time (FET), which needs to be considered to ensure HF performance. In order to pursue the best performance of SLAM, this research proposes a solution to avoid the problem of Finite Escape Time (FET) by using H∞ filter fuzzy logic technology (FHF). The main goal is to propose a new solution to the SLAM problem using FHF with trapezoid and triangular membership. The proposed technique applies the information extracted from the HF measurement innovation, the fuzzy logic controller is applied before gain K in order to control the size of the covariance by controlling the measurement input of each landmark to ensure the best output for the positioning of the mobile robot during the observation period. The investigation is done in two cases which use fuzzy logic with trapezoid membership and triangular membership. Triangular membership is chosen as it simple and easy to handle while trapezoid is good in computation cost compare to Gaussian members type. Based on the analysis, the result proved the present of FET in both original HF and proposed FHF. With suitable range of each membership produces the simulation result that proves FHF can be used to refrain the FET from occurring in the mobile robot localization in order to achieve better estimation. This study may emphasize the application of autonomous mobile robot in a real life application may be considered since the environment noise are imprecise. |
|---|