This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapi...This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.展开更多
RRT(rapidly exploring random tree)算法是一种基于采样的路径规划算法,可以在高维环境中搜索出一条路径。传统的RRT算法存在节点利用率低、计算量偏大的问题。针对这些问题,基于快速RRT*(Quick-RRT*)算法,通过优化重选父节点与剪枝范...RRT(rapidly exploring random tree)算法是一种基于采样的路径规划算法,可以在高维环境中搜索出一条路径。传统的RRT算法存在节点利用率低、计算量偏大的问题。针对这些问题,基于快速RRT*(Quick-RRT*)算法,通过优化重选父节点与剪枝范围策略、改进采样方式、引入自适应步长,对快速RRT*算法进行改进,使得算法耗时和路径长度更短。同时,加入节点连接筛选策略,消除路径中过大的转弯角。实验结果表明,改进后的算法在三维环境下能快速找到一条距离最短的无碰撞路径,且运行时间也大幅降低。展开更多
针对传统的快速扩展随机树(rapidly-exploring random tree,RRT)算法收敛速度较慢、规划航迹曲折的缺点,提出基于启发式引导策略、动态步长策略、双层平滑度优化策略的综合改进RRT算法。利用概率对随机树的生长方向进行引导;采用动态步...针对传统的快速扩展随机树(rapidly-exploring random tree,RRT)算法收敛速度较慢、规划航迹曲折的缺点,提出基于启发式引导策略、动态步长策略、双层平滑度优化策略的综合改进RRT算法。利用概率对随机树的生长方向进行引导;采用动态步长进行未知空间的搜索;通过双层平滑度优化策略进行规划航迹的平滑,规划出适合四旋翼无人机飞行的可行航迹。与其它改进方法进行仿真比较,实验结果表明,综合改进RRT算法规划的航迹更短且平滑度更好,已将其应用于四旋翼无人机两种类型的突发障碍的航迹规划中。展开更多
基金the National Natural Science Foundation of China(Grant No.42274119)the Liaoning Revitalization Talents Program(Grant No.XLYC2002082)+1 种基金National Key Research and Development Plan Key Special Projects of Science and Technology Military Civil Integration(Grant No.2022YFF1400500)the Key Project of Science and Technology Commission of the Central Military Commission.
文摘This study focuses on the improvement of path planning efficiency for underwater gravity-aided navigation.Firstly,a Depth Sorting Fast Search(DSFS)algorithm was proposed to improve the planning speed of the Quick Rapidly-exploring Random Trees*(Q-RRT*)algorithm.A cost inequality relationship between an ancestor and its descendants was derived,and the ancestors were filtered accordingly.Secondly,the underwater gravity-aided navigation path planning system was designed based on the DSFS algorithm,taking into account the fitness,safety,and asymptotic optimality of the routes,according to the gravity suitability distribution of the navigation space.Finally,experimental comparisons of the computing performance of the ChooseParent procedure,the Rewire procedure,and the combination of the two procedures for Q-RRT*and DSFS were conducted under the same planning environment and parameter conditions,respectively.The results showed that the computational efficiency of the DSFS algorithm was improved by about 1.2 times compared with the Q-RRT*algorithm while ensuring correct computational results.
文摘RRT(rapidly exploring random tree)算法是一种基于采样的路径规划算法,可以在高维环境中搜索出一条路径。传统的RRT算法存在节点利用率低、计算量偏大的问题。针对这些问题,基于快速RRT*(Quick-RRT*)算法,通过优化重选父节点与剪枝范围策略、改进采样方式、引入自适应步长,对快速RRT*算法进行改进,使得算法耗时和路径长度更短。同时,加入节点连接筛选策略,消除路径中过大的转弯角。实验结果表明,改进后的算法在三维环境下能快速找到一条距离最短的无碰撞路径,且运行时间也大幅降低。
文摘针对传统的快速扩展随机树(rapidly-exploring random tree,RRT)算法收敛速度较慢、规划航迹曲折的缺点,提出基于启发式引导策略、动态步长策略、双层平滑度优化策略的综合改进RRT算法。利用概率对随机树的生长方向进行引导;采用动态步长进行未知空间的搜索;通过双层平滑度优化策略进行规划航迹的平滑,规划出适合四旋翼无人机飞行的可行航迹。与其它改进方法进行仿真比较,实验结果表明,综合改进RRT算法规划的航迹更短且平滑度更好,已将其应用于四旋翼无人机两种类型的突发障碍的航迹规划中。
