dc.contributor.advisor |
Chandima, DP |
|
dc.contributor.advisor |
Jayasekara, AGBP |
|
dc.contributor.author |
Somasiri, JAAS |
|
dc.date.accessioned |
2018-11-21T20:12:10Z |
|
dc.date.available |
2018-11-21T20:12:10Z |
|
dc.identifier.citation |
Somasiri, J.A.A.S. (2018). Extended kalman filter and stereoscopic vision based autonomous flying system for quadcopters [Master’s theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.mrt.ac.lk/handle/123/13698 |
|
dc.identifier.uri |
http://dl.lib.mrt.ac.lk/handle/123/13698 |
|
dc.description.abstract |
This thesis can be divided into two main modules. First module is implementation of an Ex-tended Kalman filter and introduce into existing flight control algorithm which is used to con-trol multi-rotor unmanned vehicles. Purpose of this implementation is to improve flight per-formance and reliability of the system. Second module is implementation of an obstacle avoid-ance system based on stereo vision and fuzzy logic for same flight control algorithm to avoid crashes and avoid obstacles during navigation. In this thesis Chapter 1 introduce basic modules of this implementations and explain about flight control algorithm and its major components which is used in here. This chapter also explains the theory behind the Extended Kalman Fil-ters, stereo vision systems and fuzzy logic. Chapter 2 described literature survey about existing implementation of Extended Kalman filters on multi-rotor platforms, stereo vision system im-plementations and related obstacle avoidance implementations like artificial potential field and fuzzy logic. First section of chapter 3 focused into implementation details and experimenting results of Extended Kalman filter and also explained how Extended Kalman filter outputs are combined to Attitude and Position controllers of flight control algorithm. Second section of chapter 3 focused into implementation and experimenting results of the stereo vision system. This section explained detail implementation of stereo vision system like stereo camera cali-bration, image rectification, disparity map generation and depth calculation. Mainly OpenCV was used in this implementation. Third section of chapter 3 focused into explained implemen-tation of fuzzy decision-making system. In here described deciding of fuzzy inputs and outputs using depth image, creation of fuzzy inference system, selection of membership functions and combined fuzzy decision-making system with flight control algorithm. Flight testing and ex-perimental results of Extended Kalman filter and obstacle avoidance system were described in chapter 4, both systems were tested on outdoor environments and improvement of the perfor-mance and reliability was discussed in this chapter. Chapter 5 is the final chapter of this thesis and it includes conclusion of the thesis, recommendations and further works. |
en_US |
dc.language.iso |
en |
en_US |
dc.subject |
ELECTRICAL ENGINEERING-Dissertation |
en_US |
dc.subject |
INDUSTRIAL AUTOMATION-Dissertation |
en_US |
dc.subject |
QUADCOPTERS |
en_US |
dc.subject |
UNMANNED VEHICLES |
en_US |
dc.subject |
FLIGHT PERFORMANCE |
en_US |
dc.subject |
OBSTACLE AVOIDANCE |
|
dc.subject |
KALMAN FILTERS |
|
dc.title |
Extended kalman filter and stereoscopic vision based autonomous flying system for quadcopters |
en_US |
dc.type |
Thesis-Full-text |
en_US |
dc.identifier.faculty |
Engineering |
en_US |
dc.identifier.degree |
Master of Science in Industrial Automation |
en_US |
dc.identifier.department |
Department of Electrical Engineering |
en_US |
dc.date.accept |
2018-06 |
|
dc.identifier.accno |
TH3631 |
en_US |