基于半空间约束理论的自动泊车高性能轨迹优化方法

doi:10.3901/JME.2024.10.273

机械工程学报 ›› 2024, Vol. 60 ›› Issue (10): 273-288.doi: 10.3901/JME.2024.10.273

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基于半空间约束理论的自动泊车高性能轨迹优化方法

陈晓明¹, 李柏^1,2, 范丽丽³, 王涯舟¹, 张坦探¹, 张友民⁴, 曹东璞⁵

1. 湖南大学机械与运载工程学院长沙 410082;
2. 湖南大学整车先进设计制造技术全国重点实验室长沙 410082;
3. 北京理工大学信息与电子学院北京 100081;
4. 康考迪亚大学机械与航空航天工程学院蒙特利尔 H3G 1M8 加拿大;
5. 清华大学车辆与运载学院北京 100084

收稿日期:2023-06-14 修回日期:2024-02-16 出版日期:2024-05-20 发布日期:2024-07-24
作者简介:陈晓明,女,1999年出生。主要研究方向为基于计算最优控制方法的智能车自动泊车轨迹规划方法。
E-mail:xiaomingchen@hnu.edu.cn
李柏(通信作者),男,1989年出生,博士,长聘副教授,博士研究生导师。主要研究方向为智能车决策规划方法。
E-mail:libai@zju.edu.cn
范丽丽,女,1991年出生,博士后。主要研究方向为智能车感知、决策规划与控制等。
E-mail:lilifan@bit.edu.cn
王涯舟,男,2000年出生,硕士研究生。主要研究方向为复杂非结构化环境下的智能车轨迹规划与控制方法。
E-mail:albert@hnu.edu.cn
张坦探,男,1990年出生,博士,助理教授,硕士研究生导师。主要研究方向为智能网联汽车协同决策规划方法。
E-mail:zhangtantan@hnu.edu.cn
张友民,男,1963年出生,博士,教授,博士研究生导师。主要研究方向为面向自主无人系统的运动规划与容错控制方法。
E-mail:ymzhang@encs.concordia.ca
曹东璞,男,1978年出生,博士,教授,博士研究生导师。主要研究方向为认知自动驾驶与网联自动驾驶。
E-mail:dongpu.cao@uwaterloo.ca
基金资助:
国家自然科学基金(62103139)、湖南省芙蓉计划湖湘青年英才(2023RC3115)和湖南省自然科学基金(2021JJ40114)资助项目。

High-performance Trajectory Optimization for Automated Parking via Half-space Constraining Theory

CHEN Xiaoming¹, LI Bai^1,2, FAN Lili³, WANG Yazhou¹, ZHANG Tantan¹, ZHANG Youmin⁴, CAO Dongpu⁵

1. College of Mechanical and Vehicle Engineering, Hunan University, Changsha 410082;
2. State Key Laboratory of Advanced Design and Manufacturing Technology for Vehicle, Hunan University, Changsha 410082;
3. School of Information and Electronics, Beijing Institute of Technology, Beijing 100081;
4. Department of Mechanical, Industrial & Aerospace Engineering, Concordia University, Montreal H3G 1M8, Canada;
5. School of Vehicle and Mobility, Tsinghua University, Beijing 100084

Received:2023-06-14 Revised:2024-02-16 Online:2024-05-20 Published:2024-07-24

摘要/Abstract

摘要： 轨迹规划是车载自动泊车系统中的重要功能，而现有的泊车轨迹规划算法无法兼顾算法泛化性、计算精度、求解时效性以及结果最优性。现采用基于数值优化的轨迹规划技术路线，首先将泊车轨迹规划任务表述为一则通用的最优控制问题；随后提出半空间约束理论，结合概略轨迹先验信息将原本具有高维度、强非凸非线性特点的名义避障约束简化为线性不等式约束，继而利用信任域约束进一步降低线性不等式约束的规模；最后调用非线性规划求解器对简化后的最优控制问题进行数值求解，可在极短时间内生成高精度数值最优泊车轨迹：将上述泊车轨迹规划方法命名为预设空间快速优化法。大量仿真试验表明，在同样使用混合A*搜索算法提供先验的概略轨迹的前提下，预设空间快速优化法的求解成功率、计算耗时以及结果最优性均优于OBCA(Optimization-based collision avoidance)、LIOM(Lightweight iteratwe optimization method)等主流泊车轨迹优化算法。

关键词: 自动泊车, 轨迹规划, 计算最优控制, 非线性规划, 智能车

Abstract: Trajectory planning is a vital function in vehicular automatic parking systems. Existing algorithms for automatic parking trajectory planning fail to balance generalizability, precision, time efficiency, and solution optimality. Numerical-optimization-based trajectory planning is considered in this work. Initially, the concerned planning task is formulated as a unified optimal control problem. Subsequently, a half-space constraining theory is introduced, together with a reference trajectory and a trust-region constraint modeling method, to simplify the nominal large-scale and nonconvex collision-avoidance constraints as linear inequalities. Finally, the simplified optimal control problem is solved numerically to derive an optimal parking trajectory. We name this proposed planner predefined space rapid optimization (PSRO) method. Extensive simulations indicate that PSRO outperforms prevalent trajectory optimizers such as OBCA and LIOM with respect to success rate, solution quality, and computational speed.

Key words: automated parking, trajectory planning, computational optimal control, nonlinear program, intelligent vehicle

中图分类号:

U469

陈晓明, 李柏, 范丽丽, 王涯舟, 张坦探, 张友民, 曹东璞. 基于半空间约束理论的自动泊车高性能轨迹优化方法[J]. 机械工程学报, 2024, 60(10): 273-288.

CHEN Xiaoming, LI Bai, FAN Lili, WANG Yazhou, ZHANG Tantan, ZHANG Youmin, CAO Dongpu. High-performance Trajectory Optimization for Automated Parking via Half-space Constraining Theory[J]. Journal of Mechanical Engineering, 2024, 60(10): 273-288.

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基于半空间约束理论的自动泊车高性能轨迹优化方法

High-performance Trajectory Optimization for Automated Parking via Half-space Constraining Theory

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