论文标题
考虑动态障碍的自主停车的基于优化的运动计划：分层框架
Optimization-based Motion Planning for Autonomous Parking Considering Dynamic Obstacle: A Hierarchical Framework
论文作者
论文摘要
本文介绍了一个层次结构框架，该框架集成了图形搜索算法和模型预测控制，以促进在约束环境中自动驾驶汽车（AVS）的有效停车操作。在高级计划阶段，该框架结合了基于方案的混合A*（SHA*），这是传统混合a*的优化变体，以在考虑静态障碍时生成初始路径。这种全局路径是低级NLP问题的初始猜测。在低水平优化阶段，基于非线性模型预测控制（NMPC）的框架被部署以规避动态障碍。 SHA*的性能通过148个模拟场景得到了经验验证，并且通过实时并行停车模拟证明了所提出的层次结构框架的功效。
This paper introduces a hierarchical framework that integrates graph search algorithms and model predictive control to facilitate efficient parking maneuvers for Autonomous Vehicles (AVs) in constrained environments. In the high-level planning phase, the framework incorporates scenario-based hybrid A* (SHA*), an optimized variant of traditional Hybrid A*, to generate an initial path while considering static obstacles. This global path serves as an initial guess for the low-level NLP problem. In the low-level optimizing phase, a nonlinear model predictive control (NMPC)-based framework is deployed to circumvent dynamic obstacles. The performance of SHA* is empirically validated through 148 simulation scenarios, and the efficacy of the proposed hierarchical framework is demonstrated via a real-time parallel parking simulation.
