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Optimization-Based System Identification and Moving Horizon Estimation Using Low-Cost Sensors for a Miniature Car-Like Robot

2024-04-12 09:57:28
Sabrina Bodmer, Lukas Vogel, Simon Muntwiler, Alexander Hansson, Tobias Bodewig, Jonas Wahlen, Melanie N. Zeilinger, Andrea Carron


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.

Abstract (translated)

本文介绍了一种低成本、可开源的微型车类机器人,具有用于优化基于系统识别、状态估计和控制的管道。总体机器人平台成本低于700美元,因此显著简化了在现实环境中验证先进算法的复杂性。我们提出了一个带有Pacejka轮胎负荷的修改后的自行车模型来模拟所考虑的全轮驱动车辆的动力学,并防止低速时模型的 singularities。此外,我们还提供了基于优化的系统识别方法和移动目标估计(MHE)方案。在广泛的硬件实验中,我们证明了所提出的系统识别方法具有高预测精度,而MHE则具有准确的状态估计。最后,我们展示了整个闭环系统在传感器故障存在的情况下也能表现良好,尤其是在有限的时间间隔内。所有硬件、固件和控制和估计软件都通过BSD 2-条款许可发布,以促进社区内的广泛采用和合作。



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