用动态目标MPC统一路径跟踪与精确定位，实现无缝对接。
Dynamic Objective MPC for Motion Planning of Seamless Docking Maneuvers
- 基于动态权重分配的MPC算法，自动切换路径跟踪与定位模式。
- 在狭窄通道内实现高精度对接，任务时间减少，轨迹更平滑。
- 适合物流机器人、自动驾驶车辆等高精度定位场景使用。
自动化车辆和物流机器人常需在狭小环境中高精度地停靠至特定目标位置，如包裹或充电桩。传统方法分两步：先路径跟踪粗定位，再用高精度规划算法调整。此过程易因初阶段定位不佳导致次优轨迹，延长任务时间。本文提出一种统一方法，基于模型预测控制（MPC），融合模型预测轮廓控制（MPCC）与笛卡尔空间MPC优势，实现目标姿态的精准到达。主要贡献包括将动态权重分配法拓展至行驶通道内的路径终点与目标位姿，并提出动态目标MPC。该方法能根据状态自适应从MPCC无缝切换至笛卡尔MPC，独立完成路径跟踪与精确定位，不依赖目标位置。结果生成前瞻性、可行且安全的运动规划，降低任务时间，提升轨迹平滑性。
原文摘要 · Abstract (English)
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.
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