融合A^(*)算法与人工势场法的动态路径规划

Dynamic Path Planning Based on the Integration of A^(*)Algorithm and Artificial Potential Field Method

为了提升移动机器人全局路径规划及动态避障的能力,提出了一种融合A^(*)算法与人工势场法的混合算法(IA^(*)-APF)。首先,基于角度定义指数加权改进估价函数,利用障碍物的安全阈值优化子节点的选择以及对路径进行二次优化来提升A^(*)算法路径规划效率和安全性;同时,在人工势场法的斥力函数中加入了相对速度斥力,提升机器人在动态路径规划中的求解效率。最后融合改进的A^(*)算法和人工势场法,选取改进A^(*)算法得到的全局路径的拐点作为子目标点,引导人工势场法进行局部路径规划。在陷阱区域和动态环境下进行实验仿真的结果表明,IA^(*)-APF能够有效地避开局部陷阱区域,且能在提高实时避障的效率的基础上保证全局最优,是一种有效求解机器人动态路径规划的方法。

In order to improve the global path planning and dynamic obstacle avoidance capabilities of mobile robots,a hybrid algorithm(IA^(*)-APF)that combines the A^(*)algorithm and the artificial potential field method is proposed.Firstly,an exponentially weighted improved evaluation function is defined based on the angle,and the selection of sub-nodes is optimized by the safety threshold of obstacles and the secondary optimization of the path is carried out to improve the efficiency and safety of the path planning of the A^(*)algorithm.At the same time,the relative velocity repulsion is added to the repulsion function of the artificial potential field method to improve the solution efficiency of the robot in dynamic path planning.The basic idea of IA^(*)-APF is to select the inflection point of the global path obtained by the improved A^(*)algorithm as the sub-target point,and guide the artificial potential field method to carry out local path planning.The experimental simulation results in the trap area and dynamic environment show that IA^(*)-APF can effectively avoid the local trap area,and can ensure the global optimality on the basis of improving the efficiency of real-time obstacle avoidance.It is an effective solution to the robot Methods for dynamic path planning.