自主泊车的全联立动态优化方法
Simultaneous dynamic optimization for autonomous parking

本文提出联立框架下的自主泊车动态优化方法. 不同于通常采用的几何方法, 该联立方法对障碍环境和车 辆模型进行统一的描述和处理. 提出基于互补约束的数学规划方法(MPCC)与R函数方法描述泊车过程的避障条件 约束, 并联立车辆的运动学、动力学、相关物理约束建立行车系统模型; 在此基础上以运动学相关的最短时间为优 化目标, 构造泊车轨迹动态优化命题; 基于有限元正交配置离散化方法实现该动态问题的精确求解, 得到具有时间 信息的、可直接用于指导车辆操作的泊车轨迹. 多种泊车位情形下的数值试验验证了本文方法的有效性.
A simultaneous framework is put forward for dynamic optimization for autonomous parking. In contrast to geometric methods, models of the vehicle as well as environment with obstacles are handled all together in this approach. Mathematical programs with complementarity constraints (MPCC) and R-functions methods are proposed to describe the conditional constraints relating to obstacle avoidance. Combined with the vehicles’ kinematic and physical constraints, a driving system model is established. Based on this model, a dynamic trajectory optimization problem is formulated with a kinematics related objective, the minimum operating time. Discretization based on orthogonal collocation over finite elements is applied to obtain accurate numerical solution of the dynamic problem. The resulting trajectory with time information can be employed directly to vehicle operation. Performance of the proposed method is demonstrated by numerical experiments of various parking cases.