基于改进人工势场的智能车动态安全椭圆路径规划方法

doi:10.6040/j.issn.1672-3961.0.2024.003

摘要/Abstract

摘要： 针对传统人工势场法在智能车进行避障路径规划时极易出现的斥力参数难以标定、局部极值、势场力不平衡以及目标不可达等问题,提出一种基于改进人工势场的智能车动态安全椭圆路径规划方法。考虑到速度与跟踪误差关联性引入横向虚拟安全空间,构建基于动态补偿的横纵向椭圆动态安全距离模型,据此设置引斥力的作用范围,解决斥力参数难以标定的问题;基于高斯函数考虑位置矢量关系提出平衡因子和可变调节因子,解决局部极值、势场力不平衡以及目标不可达问题;设计模型预测控制器跟踪规划路径。试验结果表明:本研究提出的方法可有效解决传统人工势场法在智能车进行路径规划时出现的斥力参数难以标定、局部极值、势场力不平衡以及目标不可达等问题,同时与RRT^*算法相比,其算法运行效率提高69.5%,路径平均曲率降低62.2%,在单障碍物和多障碍物的车辆联合仿真规划场景中其平稳性和舒适性显著提高。实车试验结果表明算法可应用于实车路径规划。

关键词: 路径规划, 改进人工势场法, 位置矢量, 动态补偿, 模型预测控制

Abstract: This study addressed the challenges inherent in the traditional artificial potential field method used in intelligent vehicle path planning, specifically the intricate calibration of the repulsive force parameter, prevalence of local extreme values, imbalance of potential field force, and the issue of unreachable targets. A novel approach was proposed: a dynamic safe elliptical path planning method for intelligent vehicles based on the improved artificial potential field. This study delineated the concept of a transverse virtual safe space, taking into account the interplay between speed and tracking error. It developed an elliptic dynamic safe distance model, both transverse and longitudinal, predicated on dynamic compensation. This model facilitated the calibration of the gravitational repulsive force range, thereby addressing the prevalent challenge of repulsive parameter calibration. Building upon this framework, the paper introduced an equilibrium factor and a variable adjustment factor, both derived from the Gaussian function. These factors were designed considering the relationship between position vectors, effectively addressing issues such as local extremes, potential field force imbalances, and target inaccessibility. A model predictive controller was meticulously designed for the effective tracking control of this path. The experimental outcomes demonstrated the efficacy of the proposed method in resolving the intricate challenges associated with the calibration of repulsive force parameters, local extremes, potential field force imbalances, and target unreachability in intelligent vehicle path planning. Notably, the operational efficiency of the algorithm was enhanced by 69.5% relative to the RRT^* algorithm, and there was a notable 62.2% reduction in the average path curvature. The method significantly enhanced the smoothness and comfort in both single-obstacle and multi-obstacle vehicular simulation planning scenarios, demonstrating its versatility and effectiveness in varying conditions. The real-vehicle experiment results affirmed the applicability of the algorithm in actual path planning scenarios.

Key words: path planning, improved artificial potential field method, position vectors, dynamic compensation, model predictive control

中图分类号:

U491

赵红专,张鑫,张蓓聆,展新,李文勇,袁泉,王涛,周旦. 基于改进人工势场的智能车动态安全椭圆路径规划方法[J]. 山东大学学报 (工学版), 2025, 55(3): 46-57.

ZHAO Hongzhuan, ZHANG Xin, ZHANG Beiling, ZHAN Xin, LI Wenyong, YUAN Quan, WANG Tao, ZHOU Dan. Adynamic safe elliptical path planning method for intelligent vehicles based on improved artificial potential field[J]. Journal of Shandong University(Engineering Science), 2025, 55(3): 46-57.

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