Localization of an autonomous forklift using a neural network and a curvature-based mathematical model for template matching | |
| Author | Zahir, Ebad |
| Call Number | AIT Diss. no.ISE-24-05 |
| Subject(s) | Forklift trucks--Mathematical models Neural networks (Computer science) |
| Note | A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Engineering in Mechatronics |
| Publisher | Asian Institute of Technology |
| Abstract | This research is aimed at aiding autonomous Forklifts with accurate localization in a factory or warehouse environment. The presented work in this dissertation investigates the accuracy of a localization algorithm using lidar (NAV-350) pose data in a GPS denied environment. The experiment was successfully performed using a Xilin Counterbalanced Electric Stacker at the Asian Institute of Technology in Thailand.A template matching solution is presented using the corners of the workspace area as markers.The algorithm is designed based on a hybrid curvature model and a Neural Network. Results are also compared against the lidar NAV-350’s own software solution which are based on average reflector distance. The presented algorithm has shown to have a higher accuracy in most cases. Data is analyzed for approximately 100 different locations in a workspace of 8.9 m by 10.7 m using an industry standard forklift with a base dimension of 1 m x 1.3 m. This book explains how the algorithm can successfully determine the x, y coordinates and heading angle of the Forklift in real time. |
| Year | 2024 |
| Type | Dissertation |
| School | School of Engineering and Technology |
| Department | Department of Industrial Systems Engineering (DISE) |
| Academic Program/FoS | Mechatronics |
| Chairperson(s) | Manukid Parnichkun |
| Examination Committee(s) | Mongkol Ekpanyapong;Dailey, Matthew N. |
| Scholarship Donor(s) | AIT Fellowship |
| Degree | Thesis (Ph. D.) - Asian Institute of Technology, 2024 |