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Path Planning Based on Combinaion of Improved A-STAR Algorithm and DWA Algorithm

机译:基于改进A-STAR算法和DDA算法的组合的路径规划

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In the path planning of grid map, the influence of node search strategy makes the traditional A-STAR algorithm shown with the defects of non theoretically optimal path in actual path planning, many infection points and many redundant points. Aiming at the deficiency of path planning by traditional algorithm, this paper proposes to eliminate the limitation of node movement direction in traditional A-STAR algorithm by extending its search neighborhood, design a process to remove the redundant sub nodes in the same direction due to neighborhood extension, and combine with velocity evaluation function of the improved DWA algorithm to avoid temporary obstacles. The simulation results show that relative to the traditional A-STAR algorithm, the improved A-STAR algorithm can have shorter path and fewer inflection points; compared with the traditional DWA algorithm, the dynamic path planning combined with the improved A-STAR algorithm and DWA algorithm has fewer nodes and smoother path, which can improve the working security and efficiency of mobile robot in simulation environment.
机译:在网格图的路径规划中,节点搜索策略的影响使得传统的A-Star算法在实际路径规划中具有非理论上最佳路径的缺陷,许多感染点和许多冗余点。针对传统算法的路径规划缺陷,本文提出通过扩展其搜索邻域来消除传统A-STAR算法中节点移动方向的限制,设计一个由于邻域而在同一方向上移除冗余子节点的过程扩展,并结合改进DWA算法的速度评估功能,以避免临时障碍物。仿真结果表明,相对于传统的A-STAR算法,改进的A-Star算法可以具有较短的路径和更少的拐点;与传统的DWA算法相比,与改进的A-STAR算法和DWA算法相结合的动态路径规划具有更少的节点和更顺畅的路径,可以提高仿真环境中移动机器人的工作安全性和效率。

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