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

1) What problem is this paper solving?

Context: Vehicle Dynamic Model (VDM) simplifications and environmental assumptions limit positioning accuracy in Autonomous Mobile Robots (AMRs).
Core contribution: Utilizing endogenous information (System Identification) to improve VDM.
Achieved goal: Bridging the gap between system identification and navigation in AMRs.

2) Why is this paper important?

What changed: AMRs need to operate reliably without heavy reliance on external corrections.
Problem created: Simple physical models don’t capture complex real-world dynamics.
Why current solutions fail: They treat the vehicle model as static and known, which is rarely true.

3) How does this paper solve it?

Contribution 1: Conducts a system identification process to identify plant dynamics.
Contribution 2: Integrates identified dynamics into the VDM development.
Key result: Better positioning and navigation performance than conventional VDM.

🎯 Takeaway: Endogenous information is a powerful, underused resource for autonomous navigation.

@inproceedings{yan2023itm,
  author = {Yan, Penggao and Hsu, Li-Ta and Wen, Weisong},
  title = {Integration of Vehicle Dynamic Model and System Identified Model for Navigation in Autonomous Mobile Robots},
  booktitle = {Proceedings of the 2023 International Technical Meeting of The Institute of Navigation},
  year = {2023},
  pages = {153--160},
  doi = {10.33012/2023.18637}
}