Optimization-Based System Identification and Moving Horizon Estimation Using Low-Cost Sensors for a Miniature Car-Like Robot
<|reference_start|>Optimization-Based System Identification and Moving Horizon Estimation Using Low-Cost Sensors for a Miniature Car-Like Robot: This paper presents an open-source miniature car-like robot with low-cost sensing and a pipeline for optimization-based system identification, state estimation, and control. The overall robotics platform comes at a cost of less than $700 and thus significantly simplifies the verification of advanced algorithms in a realistic setting. We present a modified bicycle model with Pacejka tire forces to model the dynamics of the considered all-wheel drive vehicle and to prevent singularities of the model at low velocities. Furthermore, we provide an optimization-based system identification approach and a moving horizon estimation (MHE) scheme. In extensive hardware experiments, we show that the presented system identification approach results in a model with high prediction accuracy, while the MHE results in accurate state estimates. Finally, the overall closed-loop system is shown to perform well even in the presence of sensor failure for limited time intervals. All hardware, firmware, and control and estimation software is released under a BSD 2-clause license to promote widespread adoption and collaboration within the community.<|reference_end|>
@article{bodmer2024optimization-based,
    title={Optimization-Based System Identification and Moving Horizon Estimation
  Using Low-Cost Sensors for a Miniature Car-Like Robot},
    author={Sabrina Bodmer, Lukas Vogel, Simon Muntwiler, Alexander Hansson,
  Tobias Bodewig, Jonas Wahlen, Melanie N. Zeilinger, and Andrea Carron},
    journal={arXiv preprint arXiv:2404.08362},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.08362},
    primaryClass={cs.RO cs.SY eess.SY}
}