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arXiv 2504.03280cs.ROcs.SYeess.SY
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面向无缝对接机动动作运动规划的动态目标MPC

Dynamic Objective MPC for Motion Planning of Seamless Docking Maneuvers

Oliver Schumann, Michael Buchholz, Klaus Dietmayer

AI总结:

提出一种统一MPCC与笛卡尔MPC的动态目标MPC方法,通过动态权重分配实现路径跟随与高精度定位的无缝切换,从而生成更平滑、更高效的对接轨迹。

摘要:

自动车辆和物流机器人通常必须在狭窄环境中高精度地将自身定位在特定目标(如包裹或其充电站)前方。这些对接场景通常分两步解决:路径跟随与粗略定位,随后进行高精度运动规划算法。这可能会因第一阶段定位不佳而产生次优轨迹,从而延长到达目标的时间。在本研究中,我们提出了一种统一方法,基于模型预测控制(MPC),将模型预测轮廓控制(MPCC)的优势与笛卡尔MPC相统一以到达特定目标姿态。论文的主要贡献是将动态权重分配方法适配于到达驾驶走廊内的路径末端和目标姿态,以及开发了所谓的动态目标MPC。后者是对动态权重分配方法的改进,能够根据状态固有地从MPCC切换至笛卡尔MPC,通过单一算法无缝解决路径跟随问题和高精度定位任务,且独立于目标姿态的位置。这带来了具有前瞻性、可行且安全的运动规划,可减少任务时间并产生更平滑的轨迹。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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