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.

翻译：本文提出一种配备低成本传感器的开源微型类车机器人，并构建了基于优化的系统辨识、状态估计与控制流程框架。该机器人平台整体成本低于700美元，显著简化了高级算法在真实场景中的验证过程。我们提出一种改进的自行车模型，结合Pacejka轮胎力以建模所研究全轮驱动车辆的动力学特性，并避免模型在低速工况下的奇异性问题。此外，我们提供基于优化的系统辨识方法与移动水平估计方案。通过大量硬件实验验证，所提出的系统辨识方法可获得具有高预测精度的模型，而移动水平估计器能实现精确的状态估计。最终实验表明，即使在有限时间间隔内出现传感器故障的情况下，整体闭环系统仍能保持良好的控制性能。所有硬件设计、固件、控制与估计软件均基于BSD-2条款许可证开源发布，以促进学术界的广泛采用与合作。