让共享电动滑板车自主导航到停车点，靠路径跟随控制实现精准定位。
Path-following model predictive control for autonomous e-scooters
- 用模型预测控制实现路径跟随与定位，闭环协同平衡
- 可在树莓派5上运行，满足路径宽度等约束条件
- 适合关注无人自平衡交通工具的开发者与研究者
为应对共享电动滑板车系统面临的经济、生态与社会挑战，我们开发了一款自主电动滑板车原型。目标是设计一款能自主寻路至下一个停车点、高需求区域或充电站的全自主原型。本文提出一种路径跟随型模型预测控制方法，使滑板车在城市环境中沿给定路径实现定位与导航。我们设计了闭环架构，同时解决定位与路径跟踪问题，并利用已开发的反作用轮机制维持平衡。该模型预测控制方法支持状态与输入约束（如路径宽度限制），且能在树莓派5上实时执行。我们在原型车上进行了真实世界实验，验证了该方法的有效性。
原文摘要 · Abstract (English)
In order to mitigate economical, ecological, and societal challenges in electric scooter (e-scooter) sharing systems, we develop an autonomous e-scooter prototype. Our vision is to design a fully autonomous prototype that can find its way to the next parking spot, high-demand area, or charging station. In this work, we propose a path-following model predictive control solution to enable localization and navigation in an urban environment with a provided path to follow. We design a closed-loop architecture that solves the localization and path following problem while allowing the e-scooter to maintain its balance with a previously developed reaction wheel mechanism. Our model predictive control approach facilitates state and input constraints, e.g., adhering to the path width, while remaining executable on a Raspberry Pi 5. We demonstrate the efficacy of our approach in a real-world experiment on our prototype.
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