为结构化环境中的全向车辆设计实时近最优轨迹规划方法
Fast Near Time-Optimal Motion Planning for Holonomic Vehicles in Structured Environments
- 用自由空间通道和运动基元表示环境与轨迹，降低自由度
- 计算时间显著低于现有方法，且在固定通道序列下接近最优
- 适用于产线、实验室等需快速响应的高精度场景
本文提出一种新型高效基于优化的轨迹规划方法，用于全向车辆在复杂但结构化环境中生成近似时间最优的运动轨迹。该方法针对平面运动系统（如磁悬浮平台），适用于装配线、自动化实验室或无尘室等场景，要求轨迹能实时计算以提升效率并具备即时反应能力。通过将环境表示为自由空间通道，车辆在通道内的运动采用预定义的运动基元，仅保留有限自由度，由优化问题求解。相比当前最先进方法（如OMG-tools、VP-STO）所解决的完整最优控制问题，在固定通道序列下实现显著更低的计算耗时，且保持相近的最优性。方法在仿真中广泛测试，并在真实世界的Beckhoff XPlanar系统上验证。
原文摘要 · Abstract (English)
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
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