文章摘要
陈海洋, 王露楠.基于双向同时无碰撞检测目标偏置RRT算法的路径规划方法[J].空军工程大学学报:自然科学版,2022,23(3):60-67
基于双向同时无碰撞检测目标偏置RRT算法的路径规划方法
A Path Planning Algorithm Based on Two Way Simultaneous No Collision Goal RRT
  
DOI:
中文关键词: 路径规划  神经网络 双向同时搜索  椭圆状态子集  3次B样条曲线
英文关键词: path planning  neural network  two way simultaneous  subset of elliptic states  cubic B-spline curve
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作者单位
陈海洋, 王露楠 西安工程大学电子信息学院西安710048 
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中文摘要:
      针对传统RRT算法在复杂环境中对不必要区域的搜索和路径规划的时间代价过高等问题,提出了一种双向同时无碰撞检测目标偏置快速扩展随机树算法——TNCG RRT*。该算法将B RRT*中的双向搜索策略和BIT*中的启发式搜索融合作为文中的基础算法,引入神经网络的批量抓取数量决定一次采样的节点数目从而影响采样速度;然后,将正向树和反向树的扩展同时进行以加快路径搜索速度,通过对目标偏向策略中扩展顶点队列的改进和对采样区域的不断更新明确扩展方向,缩小随机树生长的范围;最后,利用3次B样条曲线使生成的路径趋于平滑。与B RRT算法和BIT*算法进行对比实验,实验结果表明:TNCG RRT*算法在路径生成时间上缩短4.5%,剪枝数增加80%,路径代价(即路径长度)缩短9%,证明了TNCG RRT*算法的有效性。
英文摘要:
      Aimed at the problems that the traditional RRT algorithm pays high price in searching unnecessary areas and in planning path under conditions of complex environment, a fast expanding random tree algorithm is proposed, i.e. TNCG RRT* (two way simultaneous non collision goal based RRT*). In this algorithm, the bidirectional search strategy in B RRT* and the heuristic search fusion in BIT* are taken as a basic algorithm of this paper, and the batch size of neural network is introduced to determine the number of nodes sampled at one time, affecting the sampling speed. And then, the expansion of forward and reverse trees is carried out simultaneously to speed up the path search, and the expansion direction is defined by improving the extended vertex queue in the target bias strategy and updating the sampling area, and the growth range of random trees is reduced. Finally, the cubic B spline curve is 〖JP2〗utilized for making the generated path smooth. The experimental results show that compared with the B RRT* algorithm and the BIT* algorithm, the TNCG RRT* algorithm can shorten the path generation time by 4.5%, the number of pruning increases by 80%, and the path cost (i.e. path length) is shortened by 9%, and is valid.
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