针对非完整系统在非凸环境中的运动规划，提出一种保证收敛的模型预测控制方法。
MPC-based motion planning for non-holonomic systems in non-convex domains
- 设计了一种输出跟踪型MPC，适用于非完整系统和非凸约束。
- 在合理假设下证明目标可达性，确保轨迹稳定收敛至目标点。
- 适合需要高可靠性的自动驾驶机器人路径规划场景。
为实现自主移动机器人运动规划中模型预测控制（MPC）的应用，本文研究了非完整系统在非凸约束下的输出跟踪型MPC。尽管已有文献证明了MPC在运动规划中的优势，但多数基础理论工作通常隐含假设系统为完整系统，且状态或输出约束需为凸集。因此，在应用类研究中，大多依赖经验结果，而关于算法完备性——特别是何种条件下目标必然可达——的研究相对较少。为此，本文提出一种新型MPC公式，可在实际可验证的合理假设下，保证系统收敛至期望目标，填补了该领域的理论空白。
原文摘要 · Abstract (English)
Motivated by the application of using model predictive control (MPC) for motion planning of autonomous mobile robots, a form of output tracking MPC for non-holonomic systems and with non-convex constraints is studied. Although the advantages of using MPC for motion planning have been demonstrated in several papers, in most of the available fundamental literature on output tracking MPC it is assumed, often implicitly, that the model is holonomic and generally the state or output constraints must be convex. Thus, in application-oriented publications, empirical results dominate and the topic of proving completeness, in particular under which assumptions the target is always reached, has received comparatively little attention. To address this gap, we present a novel MPC formulation that guarantees convergence to the desired target under realistic assumptions, which can be verified in relevant real-world scenarios.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。