The sampling-based motion planning algorithms can solve the motion planning problem in high-dimensional state space efficiently. This article presents a novel approach to sample in the promising region and reduce planning time remarkably. The RRT# defines the Relevant Region according to the cost-to-come provided by the optimal forward-searching tree; however, it takes the cumulative cost of a direct connection between the current state and the goal state as the cost-to-go. We propose a batch sampling method that samples in the refined Relevant Region, which is defined according to the optimal cost-to-come and the adaptive cost-to-go. In our method, the cost-to-come and the cost-to-go of a specific vertex are estimated by the valid optimal forward-searching tree and the lazy reverse-searching tree, respectively. New samples are generated with a direct sampling method, which can take advantage of the heuristic estimation result. We carry on several simulations in both SE(2) and SE(3) state spaces to validate the effectiveness of our method. Simulation results demonstrate that the proposed algorithm can find a better initial solution and consumes less planning time than related work.
翻译:以抽样为基础的运动规划算法可以有效地解决高维状态空间的动作规划问题。 本条提出了在有希望的区域进行抽样并显著缩短规划时间的新颖方法。 RRT#根据最佳前瞻性研究树提供的成本到收益分别对相关区域进行定义; 但是,它将目前状态与目标状态之间直接联系的累积成本作为成本到成本的计算法。 我们提议了一种批量抽样方法,在精细的相关区域进行抽样,根据最佳成本到收益和适应成本到成本确定。 在我们的方法中,一个特定顶端的成本到收益和成本到成本分别由有效的最佳前瞻性研究树和懒惰的反向研究树估算。新样本是用直接抽样方法生成的,这可以利用超常估计结果。 我们在SE(2)和SE(3)国家空间进行若干次模拟,以验证我们的方法的有效性。 模拟结果表明,拟议的算法可以找到更好的初步解决方案,消耗比相关工作少的规划时间。