This paper proposes a novel and efficient optimization-based method for generating near time-optimal trajectories for holonomic vehicles navigating through complex but structured environments. The approach aims to solve the problem of motion planning for planar motion systems using magnetic levitation that can be used in assembly lines, automated laboratories or clean-rooms. In these applications, time-optimal trajectories that can be computed in real-time are required to increase productivity and allow the vehicles to be reactive if needed. The presented approach encodes the environment representation using free-space corridors and represents the motion of the vehicle through such a corridor using a motion primitive. These primitives are selected heuristically and define the trajectory with a limited number of degrees of freedom, which are determined in an optimization problem. As a result, the method achieves significantly lower computation times compared to the state-of-the-art, most notably solving a full Optimal Control Problem (OCP), OMG-tools or VP-STO without significantly compromising optimality within a fixed corridor sequence. The approach is benchmarked extensively in simulation and is validated on a real-world Beckhoff XPlanar system

翻译：本文提出了一种新颖高效的基于优化的方法，用于为在复杂但结构化的环境中导航的全向车辆生成近时间最优轨迹。该方法旨在解决采用磁悬浮技术的平面运动系统的运动规划问题，此类系统可用于装配线、自动化实验室或洁净室。在这些应用中，需要能够实时计算的时间最优轨迹，以提高生产率并使车辆在需要时具备反应能力。所提出的方法使用自由空间走廊对环境表示进行编码，并通过运动基元表示车辆在走廊内的运动。这些基元通过启发式方法选取，并以有限自由度定义轨迹，这些自由度在优化问题中确定。因此，与现有技术相比，该方法显著降低了计算时间，特别是在固定走廊序列内求解完整最优控制问题（OCP）、OMG-tools或VP-STO时，能在不明显损失最优性的前提下实现快速求解。该方法在仿真中进行了广泛基准测试，并在实际的Beckhoff XPlanar系统上得到了验证。