基于改进RRT~*的移动机器人路径规划算法

Path Planning Algorithm for Mobile Robot Based on Improved RRT~*

RRT~*算法在路径规划过程中确保了其概率完备性和渐进最优性,但仍存在收敛速度慢且产生大而密集的采样空间等问题。为此,在RRT~*的基础上提出一种新的离线路径规划算法。在开始采样前快速瞄准目标区域,生成包含起始点与目标点的有界连通性区域。采用人工势场法优化采样过程,减少计算量,在势场合力的作用下有界区域内执行目标偏差采样,调整采样区域,在较短时间内生成较优路径。实验结果表明,当生成的路径成本相同时,该算法所需的迭代次数和执行时间远小于RRT~*算法,算法效率更高。

The RRT^* algorithm ensures its probabilistic completeness and asymptotic optimality in the path planning process,but there are still some problems such as slow convergence and large and dense sampling space.To solve these problems,this paper proposes a new offline path planning algorithm based on RRT^*.The proposed algorithm quickly aims at the target area before starting sampling,and generates a bounded connected region that includes the starting point and the target point.The artificial potential field method is used to optimize the sampling process by reducing computational load.The target deviation sampling is executed in the bounded area under the action of the resultant force in the potential field,and generate a better path in a short period of time by flexible adjustment of the sampling area.Experimental results show that at the same cost of path planning,the number of required iterations and execution time of the proposed algorithm is much less than those of the RRT^* algorithm,which means the proposed algorithm is more efficient.