@inproceedings{92e229fc6aaf49b9a1f92131c44f372a,
title = "Path Following of Field Tracked Robots Based on Model Predictive Control with Visual-Inertial Odometry and Identified State-Space Dynamic Model",
abstract = "Model predictive control (MPC) with prediction and control horizons under multivariable constraints can prompt field tracked vehicles to follow the reference path accurately. However, a kinematic model or a classic dynamic model of a vehicle is needed in MPC, and both of them must be linearized and hence the computation cost is large. Also, the parameters of a classic dynamic model are difficult to be measured. In this paper, system identification approach for estimated the linear state-space dynamic model of a field tracked vehicle in farm has been utilized. The dynamic model has been identified with more than 50% estimated fitting. Using the dynamic model, a linear MPC can be adopted, and hence the computation can be saved more than 2/3, compared with the conventional nonlinear MPC with a kinematic model. Furthermore, the tracked vehicle adopted the linear MPC with the dynamic model can achieve superior S-curve and L-shape path following.",
keywords = "model predictive control, pathing following, System identification, tracked robot, visual inertial odometry",
author = "Sung, {Chun Ting} and Tseng, {Wen Chuan} and Hsu, {Meng Hui} and Chen, {Shean Jen}",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; Optics, Photonics and Digital Technologies for Imaging Applications VII 2022 ; Conference date: 09-05-2022 Through 15-05-2022",
year = "2022",
doi = "10.1117/12.2619458",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Peter Schelkens and Tomasz Kozacki",
booktitle = "Optics, Photonics and Digital Technologies for Imaging Applications VII",
address = "United States",
}