OCEAN：一种基于ADMM的自主泊车开放空间无碰撞轨迹规划器
OCEAN: An Openspace Collision-free Trajectory Planner for Autonomous Parking Based on ADMM
AI总结：
本研究提出基于ADMM的自主泊车开放空间无碰撞轨迹规划器OCEAN，通过热启动、凸对偶重构与并行求解提升效率鲁棒性，经仿真与实场测试验证性能优于现有基准。
摘要：
本文提出了一种面向自主泊车的开放空间无碰撞轨迹规划器（Openspace Collision-freE trAjectory plaNner，OCEAN）。OCEAN是一种基于优化、由交替方向乘子法（Alternating Direction Method of Multiplier，ADMM）加速的轨迹规划器，计算效率与鲁棒性均有所提升，适用于动态障碍物较少的各类场景。该方法从基于分层优化的避障框架出发，首先用无碰撞的Hybrid A*轨迹为轨迹规划问题提供热启动，随后将避障轨迹规划问题重构为光滑凸对偶形式，并通过ADMM并行求解。优化变量被精细划分为若干组，使得ADMM子问题被构建为可高效鲁棒求解的二次规划（Quadratic Programming，QP）、序列二次规划（Sequential Quadratic Programming，SQP）以及二阶锥规划（Second Order Cone Programming，SOCP）问题。我们在数百个仿真场景以及公共停车场的数百小时测试中验证了所提方法，结果表明，与其他基准方法相比，该方法具备更优的系统性能。
英文摘要：
In this paper, we propose an Openspace Collision-freE trAjectory plaNner (OCEAN) for autonomous parking. OCEAN is an optimization-based trajectory planner accelerated by Alternating Direction Method of Multiplier (ADMM) with enhanced computational efficiency and robustness, and is suitable for all scenes with few dynamic obstacles. Starting from a hierarchical optimization-based collision avoidance framework, the trajectory planning problem is first warm-started by a collision-free Hybrid A* trajectory, then the collision avoidance trajectory planning problem is reformulated as a smooth and convex dual form, and solved by ADMM in parallel. The optimization variables are carefully split into several groups so that ADMM sub-problems are formulated as Quadratic Programming (QP), Sequential Quadratic Programming (SQP),and Second Order Cone Programming (SOCP) problems that can be efficiently and robustly solved. We validate our method both in hundreds of simulation scenarios and hundreds of hours of public parking areas. The results show that the proposed method has better system performance compared with other benchmarks.
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