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一种通过求解最优控制问题实现自动代客泊车的轨迹规划方法。

A Trajectory Planning Method for Autonomous Valet Parking via Solving an Optimal Control Problem.

作者信息

Chen Chen, Wu Bing, Xuan Liang, Chen Jian, Wang Tianxiang, Qian Lijun

机构信息

College of Automotive and Transportation Engineering, Hefei University of Technology, Hefei 230009, China.

出版信息

Sensors (Basel). 2020 Nov 11;20(22):6435. doi: 10.3390/s20226435.

DOI:10.3390/s20226435
PMID:33187151
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7698036/
Abstract

In the last decade, research studies on parking planning mainly focused on path planning rather than trajectory planning. The results of trajectory planning are more instructive for a practical parking process. Therefore, this paper proposes a trajectory planning method in which the optimal autonomous valet parking (AVP) trajectory is obtained by solving an optimal control problem. Additionally, a vehicle kinematics model is established with the consideration of dynamic obstacle avoidance and terminal constraints. Then the parking trajectory planning problem is modeled as an optimal control problem, while the parking time and driving distance are set as the cost function. The homotopic method is used for the expansion of obstacle boundaries, and the Gauss pseudospectral method (GPM) is utilized to discretize this optimal control problem into a nonlinear programming (NLP) problem. In order to solve this NLP problem, sequential quadratic programming is applied. Considering that the GPM is insensitive to the initial guess, an online calculation method of vertical parking trajectory is proposed. In this approach, the offline vertical parking trajectory, which is calculated and stored in advance, is taken as the initial guess of the online calculation. The selection of an appropriate initial guess is based on the actual starting position of parking. A small parking lot is selected as the verification scenario of the AVP. In the validation of the algorithm, the parking trajectory planning is divided into two phases, which are simulated and analyzed. Simulation results show that the proposed algorithm is efficient in solving a parking trajectory planning problem. The online calculation time of the vertical parking trajectory is less than 2 s, which meets the real-time requirement.

摘要

在过去十年中，关于停车规划的研究主要集中在路径规划而非轨迹规划上。轨迹规划的结果对实际停车过程更具指导意义。因此，本文提出了一种轨迹规划方法，通过求解最优控制问题来获得最优的自动代客泊车（AVP）轨迹。此外，考虑动态避障和终端约束建立了车辆运动学模型。然后将停车轨迹规划问题建模为最优控制问题，将停车时间和行驶距离设为成本函数。采用同伦方法对障碍物边界进行扩展，利用高斯伪谱法（GPM）将该最优控制问题离散为非线性规划（NLP）问题。为求解此NLP问题，应用序列二次规划。考虑到GPM对初始猜测不敏感，提出了一种垂直停车轨迹的在线计算方法。在此方法中，将预先计算并存储的离线垂直停车轨迹作为在线计算的初始猜测。合适初始猜测的选择基于实际停车起始位置。选择一个小型停车场作为AVP的验证场景。在算法验证中，将停车轨迹规划分为两个阶段进行仿真和分析。仿真结果表明，所提算法在解决停车轨迹规划问题方面是有效的。垂直停车轨迹的在线计算时间小于2秒，满足实时要求。

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