LATIDIA · Robótica

De las decisiones semánticas a las trayectorias factibles: control óptimo auto-evolutivo guiado por LLM para estacionamiento en espacios estrechos

arXiv: 2609.24631v2Tipo de anuncio: reemplazar Resumen: El estacionamiento autónomo en entornos no convexos y estrechos sigue siendo un desafío. Aunque los métodos de control óptimo pueden hacer cumplir explícitamente la dinámica del vehículo y las desventajas de la colisión

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arXiv:2609.24631v2 Announce Type: replace Abstract: Autonomous parking in nonconvex and narrow environments remains challenging. Although optimal-control methods can explicitly enforce vehicle dynamics and collision constraints, nonconvexity compromises solver robustness and can cause failures. Large language models (LLMs) exhibit strong semantic reasoning capabilities, but directly generating dense trajectories makes it difficult to guarantee physical feasibility. We introduce SE-LLM-OCP, a unified framework in which LLMs make high-level discrete maneuver decisions, while an optimal-control module enforces low-level vehicle dynamics and collision constraints. Online, the LLM proposes sparse maneuver plans, decomposing the parking task into a sequence of short-horizon trajectory-optimization problems. A low-level solver then sequentially solves optimal-control problems. If the solver fails, the