对于RRT*算法,存在诸如更大的随机性之类的问题,较长时间的消费,更多的冗余节点,以及在自动驾驶车辆的路径规划过程中遇到未知障碍物时无法进行局部避障。而应用于自动驾驶汽车的人工势场法(APF)容易出现局部最优性、无法到达的目标,以及对全球场景的不适用性。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,介绍了人工势场的概念和概率抽样优化策略,根据道路曲率设计自适应步长。对规划的全局路径进行路径后处理,减少生成路径的冗余节点,提高抽样的目的,解决在目标点附近扩展时可能发生振荡的问题,减少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,增加道路边界斥力势场,优化排斥函数和安全椭圆,无法达到目标的问题可以解决,可以减少路径中不必要的转向,可以提高规划路径的安全性。面对U形障碍,生成虚拟重力点来解决局部最小问题,提高障碍物的通过性能。最后,融合算法,结合改进的RRT*算法和改进的人工势场法,是设计的。前者首先规划了全球道路,提取路径节点作为后者的临时目标点,引导车辆行驶,当遇到未知障碍物时,通过改进的人工势场法避开局部障碍物,然后平滑融合算法规划的路径,使路径满足车辆运动学约束。在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更加稳定的平滑路径,更准确,适合车辆驾驶。
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.