Abstract
Automated vehicles and logistics robots must often position themselves in narrow environments with high precision in front of a specific target, such as a package or their charging station. Often, these docking scenarios are solved in two steps: path following and rough positioning followed by a high-precision motion planning algorithm. This can generate suboptimal trajectories caused by bad positioning in the first phase and, therefore, prolong the time it takes to reach the goal. In this work, we propose a unified approach, which is based on a Model Predictive Control (MPC) that unifies the advantages of Model Predictive Contouring Control (MPCC) with a Cartesian MPC to reach a specific goal pose. The paper's main contributions are the adaption of the dynamic weight allocation method to reach path ends and goal poses inside driving corridors, and the development of the so-called dynamic objective MPC. The latter is an improvement of the dynamic weight allocation method, which can inherently switch state-dependent from an MPCC to a Cartesian MPC to solve the path-following problem and the high-precision positioning tasks independently of the location of the goal pose seamlessly by one algorithm. This leads to foresighted, feasible, and safe motion plans, which can decrease the mission time and result in smoother trajectories.
Abstract (translated)
自动车辆和物流机器人常常需要在狭窄环境中精确地定位到特定目标，如包裹或充电站前。通常，这些对接场景的解决方法是分两步进行：首先是路径跟踪和粗略定位，然后使用高精度运动规划算法进行精确定位。这种方法可能由于初始阶段的定位不佳而生成次优轨迹，从而延长达到目标所需的时间。 本文提出了一种统一的方法，该方法基于模型预测控制（MPC），结合了轮廓模型预测控制（MPCC）与笛卡尔MPC的优势，以实现特定的目标姿态。论文的主要贡献在于将动态权重分配法应用于到达路径末端和驾驶走廊内的目标位置，并开发所谓的动态目标MPC。后者是对动态权重分配方法的改进，能够在状态依赖的情况下从MPCC无缝切换到笛卡尔MPC，独立于目标位置解决路径跟踪问题与高精度定位任务。这种方法能够产生预见性、可行性和安全性更高的运动计划，从而减少执行时间并生成更平滑的轨迹。
URL
https://arxiv.org/abs/2504.03280
