Li Xiang, Li Gang, Bian Zijian
School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China.
Sensors (Basel). 2024 Jun 16;24(12):3899. doi: 10.3390/s24123899.
For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in the path planning process of autonomous vehicles. And the artificial potential field method (APF) applied to autonomous vehicles is prone to problems such as local optimality, unreachable targets, and inapplicability to global scenarios. A fusion algorithm combining the improved RRT* algorithm and the improved artificial potential field method is proposed. First of all, for the RRT* algorithm, the concept of the artificial potential field and probability sampling optimization strategy are introduced, and the adaptive step size is designed according to the road curvature. The path post-processing of the planned global path is carried out to reduce the redundant nodes of the generated path, enhance the purpose of sampling, solve the problem where oscillation may occur when expanding near the target point, reduce the randomness of RRT* node sampling, and improve the efficiency of path generation. Secondly, for the artificial potential field method, by designing obstacle avoidance constraints, adding a road boundary repulsion potential field, and optimizing the repulsion function and safety ellipse, the problem of unreachable targets can be solved, unnecessary steering in the path can be reduced, and the safety of the planned path can be improved. In the face of U-shaped obstacles, virtual gravity points are generated to solve the local minimum problem and improve the passing performance of the obstacles. Finally, the fusion algorithm, which combines the improved RRT* algorithm and the improved artificial potential field method, is designed. The former first plans the global path, extracts the path node as the temporary target point of the latter, guides the vehicle to drive, and avoids local obstacles through the improved artificial potential field method when encountered with unknown obstacles, and then smooths the path planned by the fusion algorithm, making the path satisfy the vehicle kinematic constraints. The simulation results in the different road scenes show that the method proposed in this paper can quickly plan a smooth path that is more stable, more accurate, and suitable for vehicle driving.
对于RRT算法，在自动驾驶车辆路径规划过程中存在随机性大、耗时较长、冗余节点较多以及在路径中遇到未知障碍物时无法进行局部避障等问题。而应用于自动驾驶车辆的人工势场法（APF）容易出现局部最优、目标不可达以及不适用于全局场景等问题。提出了一种将改进的RRT算法与改进的人工势场法相结合的融合算法。首先，对于RRT算法，引入人工势场概念和概率采样优化策略，并根据道路曲率设计自适应步长。对规划出的全局路径进行路径后处理，以减少生成路径的冗余节点，增强采样目的，解决在目标点附近扩展时可能出现的振荡问题，降低RRT节点采样的随机性，提高路径生成效率。其次，对于人工势场法，通过设计避障约束、添加道路边界排斥势场以及优化排斥函数和安全椭圆，可解决目标不可达问题，减少路径中不必要的转向，提高规划路径的安全性。面对U形障碍物时，生成虚拟重力点以解决局部极小值问题，提高障碍物通过性能。最后，设计了将改进的RRT*算法与改进的人工势场法相结合的融合算法。前者先规划全局路径，提取路径节点作为后者的临时目标点，引导车辆行驶，在遇到未知障碍物时通过改进的人工势场法避障，然后对融合算法规划出的路径进行平滑处理，使路径满足车辆运动学约束。不同道路场景下的仿真结果表明，本文提出的方法能够快速规划出一条更稳定、更精确且适合车辆行驶的平滑路径。
