Hierarchical optimal planning and real-time tracking of parking trajectories based on risk field

IF 4.6 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice Pub Date : 2025-05-31 DOI:10.1016/j.conengprac.2025.106423

Dongxu Su, Zhiguo Zhao, Kun Zhao, Kaichong Liang, Qin Yu

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Abstract

In order to improve the safety, smoothness and robustness of the automatic parking trajectory for light commercial vehicles in dynamic environments, this paper designs a novel real-time parking trajectory planning and tracking control architecture. A parking risk field (PRF) is established to reflect the guiding role and constraints of the parking environment on the vehicle. Nonlinear Model Predictive Control (NMPC) incorporating the PRF is proposed to realize the trajectory smooth optimization and real-time dynamic obstacle avoidance while tracking the global trajectory. Firstly, in the global parking trajectory planning layer, the initial trajectory is generated based on hybrid A*, path quadratic smoothing and speed planning algorithms. The parking problem is then formulated as an optimization problem, with the global trajectory planned based on the initial one. Secondly, in the real-time tracking control layer, considering the influence of trajectory guidance, drivable area modeling and dynamic obstacle avoidance, the PRF is constructed, comprising the trajectory attraction field, parking boundary field and vehicle repulsion field. Based on the PRF, the NMPC cost function is formulated. Additionally, vehicle ellipse constraints are designed and work with the boundary field to confine the vehicle within the parking space. Finally, the proposed optimal planning and dynamic tracking method based on the PRF is verified through simulation and real vehicle experiments. Both simulation and experiment results demonstrate that the designed trajectory planning and control architecture can enable the vehicle to park safely, smoothly and accurately in the parking space, while avoiding dynamic obstacles in real time through NMPC.

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基于风险场的停车轨迹分层优化规划与实时跟踪

为了提高轻型商用车在动态环境下自动停车轨迹的安全性、平稳性和鲁棒性，设计了一种新型的实时停车轨迹规划与跟踪控制体系结构。建立了停车风险场（PRF），以反映停车环境对车辆的引导作用和约束。为了在跟踪全局轨迹的同时实现轨迹平滑优化和实时动态避障，提出了结合PRF的非线性模型预测控制（NMPC）。首先，在全局停车轨迹规划层，基于混合A*、路径二次平滑和速度规划算法生成初始轨迹；然后将停车问题表述为优化问题，在初始轨迹的基础上规划全局轨迹。其次，在实时跟踪控制层，考虑轨迹引导、可行驶区域建模和动态避障的影响，构建了由轨迹引力场、停车边界场和车辆斥力场组成的PRF；在PRF的基础上，构造了NMPC的成本函数。设计了车辆椭圆约束，并结合边界场将车辆限制在停车空间内。最后，通过仿真和实车实验验证了基于PRF的最优规划和动态跟踪方法。仿真和实验结果表明，所设计的轨迹规划和控制体系结构能够使车辆安全、平稳、准确地在车位内停车，并通过NMPC实时避开动态障碍物。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Control Engineering Practice 工程技术-工程：电子与电气

CiteScore

9.20

自引率

12.20%

发文量

183

审稿时长

44 days

期刊介绍： Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.