提出高效算法，实现实时最优速度规划，兼顾精度与计算速度。
Real-time Velocity Profile Optimization for Time-Optimal Maneuvering with Generic Acceleration Constraints
- 采用前后向迭代优化路径分段速度，支持任意加速度约束
- 在五条赛道上比基准方法快300倍，误差仅0.11%~0.36%
- 适合在线多查询规划，对粗离散化仍保持高精度
在满足通用加速度约束条件下，计算沿预设路径的时最优速度轨迹是机器人轨迹规划中的关键问题，尤其适用于自动驾驶赛车场景。现有方法要么在高计算成本下支持任意约束，要么为追求效率使用保守的盒式约束。本文提出FBGA（前向-后向通用加速度约束算法），通过前后向迭代在离散路径段中最大化速度，同时满足用户定义的性能限制。在五条赛道和两类车辆上测试表明，该方法能处理复杂的非凸加速度约束，并实现与最优控制基线接近的轨迹和圈速（误差0.11%~0.36%），计算速度提升达三个数量级。即使在粗离散化下也保持高精度，非常适合在线多查询轨迹规划。代码已开源：https://anonymous.4open.science/r/FB_public_RAL。
原文摘要 · Abstract (English)
The computation of time-optimal velocity profiles along prescribed paths, subject to generic acceleration constraints, is a crucial problem in robot trajectory planning, with particular relevance to autonomous racing. However, the existing methods either support arbitrary acceleration constraints at high computational cost or use conservative box constraints for computational efficiency. We propose FBGA, a new \underline{F}orward-\underline{B}ackward algorithm with \underline{G}eneric \underline{A}cceleration constraints, which achieves both high accuracy and low computation time. FBGA operates forward and backward passes to maximize the velocity profile in short, discretized path segments, while satisfying user-defined performance limits. Tested on five racetracks and two vehicle classes, FBGA handles complex, non-convex acceleration constraints with custom formulations. Its maneuvers and lap times closely match optimal control baselines (within $0.11\%$-$0.36\%$), while being up to three orders of magnitude faster. FBGA maintains high accuracy even with coarse discretization, making it well-suited for online multi-query trajectory planning. Our open-source \texttt{C++} implementation is available at: https://anonymous.4open.science/r/FB_public_RAL.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。