动力学感知的自适应走廊与可行性扰动信赖域序列二次规划用于认证非完整运动规划

中文摘要 AI 辅助

基于优化的停车规划器通常仅在时间采样点施加碰撞约束，因此车辆角点可能在采样点之间切入障碍物，并且在求解器收敛之前不存在可执行的轨迹。我们提出了一种适用于带倒挡的类车车辆的规划器，其中优化阶段的每次迭代都精确满足离散化动力学，并保持整个车辆矩形在采样点之间避开障碍物。每个时间间隔获得一个凸走廊，该走廊在两端容纳所有车辆角点，并通过扫描余量进行收缩，该余量界定了角点路径偏离其弦的最大距离。当初始猜测处于碰撞状态时，分离半平面赋予走廊一个远离障碍物的方向；随后，根据当前迭代的速度、曲率和步长生成朝向对齐的包围盒，并在接受步长后重建。一种可行性扰动的信赖域序列二次规划方法通过反馈将每一步投影到动力学上并精确验证；代价单调递减，一旦走廊停止变化，极限点即为走廊约束问题的Karush-Kuhn-Tucker点，或违反约束资格。在820个基准案例中，规划器在818个案例中成功且无穿透（其中797个来自第一个初始猜测），评估的7103个优化阶段迭代中没有一个不可用，并且在99%的初始猜测与障碍物相交的情况下，成功率为96.5%。其操作的中位时间比类似认证的精确碰撞基线长0.7%。保证适用于规划模型，而非物理车辆。

英文摘要

Optimisation-based parking planners usually impose collision constraints only at the time samples, so a vehicle corner can cut an obstacle between samples, and no executable trajectory exists until the solver converges. We present a planner for car-like vehicles with reverse gear in which every iterate of the optimisation phase satisfies the discretised dynamics exactly and keeps the whole vehicle rectangle clear of obstacles between the samples. Each time interval receives one convex corridor that holds all vehicle corners at both ends and is shrunk by a sweep margin bounding how far the corner paths leave their chords. Separating half-planes give the corridors a direction out of obstacles when the initial guess is in collision; later, heading-aligned boxes are grown from the speed, curvature and step of the current iterate and rebuilt after accepted steps. A feasibility-perturbed trust-region sequential quadratic programming method projects each step onto the dynamics by feedback and verifies it exactly; the cost decreases monotonically, and once the corridors stop changing, limit points are Karush-Kuhn-Tucker points of the corridor-constrained problem or violate a constraint qualification. On 820 benchmark cases the planner succeeds in 818 without penetration (797 from the first initial guess), none of the 7103 evaluated optimisation-phase iterates is unusable, and it succeeds in 96.5% of the cases when 99% of the initial guesses intersect an obstacle. Its maneuvers take 0.7% longer in the median than those of a similarly certified exact-collision baseline. The guarantees hold for the planning model, not for a physical vehicle.

发表机构

• Jiangsu College of Safety Technology（江苏安全技术职业学院）

机构由 AI 辅助整理，请以论文原文为准。