Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms ...Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms have achieved good results in many planning tasks.However,sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages.Therefore,several algorithms have been proposed to overcome these drawbacks.As one of the improved algorithms,Rapidlyexploring random vines(RRV)can achieve better results,but it may perform worse in cluttered environments and has a certain environmental selectivity.In this paper,we present a new improved planning method based on RRT-Connect and RRV,named adaptive RRT-Connect(ARRT-Connect),which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments.The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.展开更多
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
针对邮轮推舱序列自动规划问题,采用投影法建立推舱路径规划模型,并提出一种基于改进双向快速搜索随机树(Bidirectional Rapidly-Exploring Random Tree,Bi-RRT)算法嵌入的贪心算法进行邮轮推舱序列规划的方法。以大型邮轮H1508船甲板...针对邮轮推舱序列自动规划问题,采用投影法建立推舱路径规划模型,并提出一种基于改进双向快速搜索随机树(Bidirectional Rapidly-Exploring Random Tree,Bi-RRT)算法嵌入的贪心算法进行邮轮推舱序列规划的方法。以大型邮轮H1508船甲板中段区域为例,在Unity3D软件中对预制模块化舱室单元(Pre-fabricated Modular Cabin Unit,PMCU)的推舱序列规划进行仿真试验。试验结果表明,该方法可兼顾避障验证与序列规划,比传统蛇形推舱序列规划具有更高的效率。展开更多
为了提高直捻机上纱机械臂的避障路径规划效率,提出一种动态目标圆采样结合回归机制的改进型双向快速扩展随机树算法(Dynamic-target-circle Sampling and Regression mechanism Bidirectional Rapidly-exploring Random Trees,DSRB-RRT...为了提高直捻机上纱机械臂的避障路径规划效率,提出一种动态目标圆采样结合回归机制的改进型双向快速扩展随机树算法(Dynamic-target-circle Sampling and Regression mechanism Bidirectional Rapidly-exploring Random Trees,DSRB-RRT)。为解决随机树盲目采样问题,提出了一种动态目标圆采样法,引导随机树在以目标为圆心的动态圆区域内进行采样;为解决随机树拓展速度慢,提出了一种变步长变概率法,根据障碍物信息自行改变拓展步长和偏置概率,加快随机树收敛;引入了回归机制防止随机树在区域内过度采样;算法生成路径后,裁剪路径中冗余节点来缩短路径长度,并用三次B样条曲线平滑优化路径。仿真结果表明,DSRB-RRT算法相比于加入目标偏置的RRT、BI-RRT和GS-RRT在不同障碍场景下的收敛效率更高,平均路径更短。在ROS系统中对上纱机械臂进行仿真,验证了DSRB-RRT算法的有效性,可以提高机械臂路径规划效率。展开更多
基金supported in part by the National Science Foundation of China(61976175,91648208)the Key Project of Natural Science Basic Research Plan in Shaanxi Province of China(2019JZ-05)。
文摘Sampling-based planning algorithms play an important role in high degree-of-freedom motion planning(MP)problems,in which rapidly-exploring random tree(RRT)and the faster bidirectional RRT(named RRT-Connect)algorithms have achieved good results in many planning tasks.However,sampling-based methods have the inherent defect of having difficultly in solving planning problems with narrow passages.Therefore,several algorithms have been proposed to overcome these drawbacks.As one of the improved algorithms,Rapidlyexploring random vines(RRV)can achieve better results,but it may perform worse in cluttered environments and has a certain environmental selectivity.In this paper,we present a new improved planning method based on RRT-Connect and RRV,named adaptive RRT-Connect(ARRT-Connect),which deals well with the narrow passage environments while retaining the ability of RRT algorithms to plan paths in other environments.The proposed planner is shown to be adaptable to a variety of environments and can accomplish path planning in a short time.
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.
文摘为了提高直捻机上纱机械臂的避障路径规划效率,提出一种动态目标圆采样结合回归机制的改进型双向快速扩展随机树算法(Dynamic-target-circle Sampling and Regression mechanism Bidirectional Rapidly-exploring Random Trees,DSRB-RRT)。为解决随机树盲目采样问题,提出了一种动态目标圆采样法,引导随机树在以目标为圆心的动态圆区域内进行采样;为解决随机树拓展速度慢,提出了一种变步长变概率法,根据障碍物信息自行改变拓展步长和偏置概率,加快随机树收敛;引入了回归机制防止随机树在区域内过度采样;算法生成路径后,裁剪路径中冗余节点来缩短路径长度,并用三次B样条曲线平滑优化路径。仿真结果表明,DSRB-RRT算法相比于加入目标偏置的RRT、BI-RRT和GS-RRT在不同障碍场景下的收敛效率更高,平均路径更短。在ROS系统中对上纱机械臂进行仿真,验证了DSRB-RRT算法的有效性,可以提高机械臂路径规划效率。
