Integration of Vehicle Dynamic Model and System Identified Model for Navigation in Autonomous Mobile Robots

Penggao Yan, Li-Ta Hsu, and Weisong Wen

Peer Reviewed

Abstract:	vehicle dynamic models are the basis of various navigation algorithms in autonomous mobile robots (AMRs), describing the vehicle motion purely by physical law. However, its simplifications on the system complexity and assumptions on the environments prevent it from providing accurate positioning results. Instead of introducing sensors to correct its pose estimation error, this study aims to utilize the endogenous information of AMRs to improve positioning performance. A system identification process is conducted to identify the system dynamics of the plants in AMRs, where the identified system dynamics is integrated into the development of vehicle dynamic models. Experiments on two scenarios show that the proposed method achieves better positioning results and navigation performance than conventional vehicle dynamic models, demonstrating the potential of endogenous information in AMRs to enhance their ability on navigation tasks. In addition, this study contributes to the literature that builds the bridge between system identification and navigation in AMRs.
Published in:	Proceedings of the 2023 International Technical Meeting of The Institute of Navigation January 24 - 26, 2023 Hyatt Regency Long Beach Long Beach, California
Pages:	153 - 160
Cite this article:	Yan, Penggao, Hsu, Li-Ta, Wen, Weisong, "Integration of Vehicle Dynamic Model and System Identified Model for Navigation in Autonomous Mobile Robots," Proceedings of the 2023 International Technical Meeting of The Institute of Navigation, Long Beach, California, January 2023, pp. 153-160. https://doi.org/10.33012/2023.18637
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