基于B样条参数化的高速公路场景时间最优轨迹规划
Time-Optimal Trajectory Planning in Highway Scenarios using Basis-Spline Parameterization
AI总结：
针对现有基于梯度的运动规划器无法保证递归可行性、难达不同时间尺度终端流形的问题，提出将样条断点纳入优化变量的非线性优化方法，结合样条基变换与断点移除策略，经超车场景验证了断点数量对求解质量和优化耗时的影响。
摘要：
B样条（基样条）可通过有限数量的约束实现时间连续的可行性检查。对于需要无碰撞且动力学可行轨迹的运动规划应用，约束作用于整条轨迹。现有依赖基于梯度优化的运动规划器采用时间缩放来实现收缩规划视界，它们既无法保证轨迹的递归可行性，也不能实现在不同时间尺度下到达两个终端流形部分。本文提出了一个非线性优化问题以解决现有方法的缺陷，为此将样条断点纳入优化变量，通过实现样条基之间的变换得到稀疏问题公式，并提出一种断点移除策略以实现向终端流形的收敛。在超车场景中的评估展示了断点数量对求解质量和优化所需时间的影响。
英文摘要：
Basis splines enable a time-continuous feasibility check with a finite number of constraints. Constraints apply to the whole trajectory for motion planning applications that require a collision-free and dynamically feasible trajectory. Existing motion planners that rely on gradient-based optimization apply time scaling to implement a shrinking planning horizon. They neither guarantee a recursively feasible trajectory nor enable reaching two terminal manifold parts at different time scales. This paper proposes a nonlinear optimization problem that addresses the drawbacks of existing approaches. Therefore, the spline breakpoints are included in the optimization variables. Transformations between spline bases are implemented so a sparse problem formulation is achieved. A strategy for breakpoint removal enables the convergence into a terminal manifold. The evaluation in an overtaking scenario shows the influence of the breakpoint number on the solution quality and the time required for optimization.
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。
