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
Path planning is a crucial concern in the field of mobile robotics,particularly in complex scenarios featuring narrow passages.Sampling-based planners,such as the widely utilized probabilistic roadmap(PRM),have been e...Path planning is a crucial concern in the field of mobile robotics,particularly in complex scenarios featuring narrow passages.Sampling-based planners,such as the widely utilized probabilistic roadmap(PRM),have been extensively employed in various robot applications.However,PRM’s utilization of random node sampling often results in disconnected graphs,posing a significant challenge when dealing with narrow passages.In order to tackle this issue,we present equipotential line sampling strategy for probabilistic roadmap(EPL-PRM),a novel approach derived from PRM.This paper initially proposes a sampling potential field,followed by the construction of equipotential lines that are denser in the proximity of obstacles and narrow passages.Random sampling is subsequently conducted along these lines.Consequently,the sampling strategy enhances the likelihood of sampling nodes around obstacles and narrow passages,thereby addressing the issue of sparsity encountered in traditional sampling-based planners.Furthermore,we introduce a nodal optimization method based on an artificial repulsive field,which prompts sampled nodes to move in the direction of repulsion.As a result,nodes around obstacles are distributed more uniformly,while nodes within narrow passages gravitate toward the middle of the passages.Finally,extensive simulations are conducted to evaluate the proposed method.The results demonstrate that our approach achieves path planning with superior efficiency,lower cost,and higher reliability compared with traditional algorithms.展开更多
针对微型无人车使用传统RRT(Rapidly-exploring Random Trees)算法在地下空间探索过程中,相对于狭窄通道或狭窄入口的环境,路径生长困难,探索效率低.提出了一种改进的IA-RRT(Improve Adaptation RRT)算法,引入距离优先的启发式采样策略...针对微型无人车使用传统RRT(Rapidly-exploring Random Trees)算法在地下空间探索过程中,相对于狭窄通道或狭窄入口的环境,路径生长困难,探索效率低.提出了一种改进的IA-RRT(Improve Adaptation RRT)算法,引入距离优先的启发式采样策略和环境自适应的步长选择策略.微型无人车首先计算环境中障碍物占比,优化选择较小的步长保障有效通过地下空间狭窄环境.为了验证算法的有效性,设置3个障碍物占比不同的试验环境场景,IA-RRT算法分别与RRT、RRT*、Informed RRT*算法的路径规划结果对比,结果表明:改进的IA-RRT算法的平均搜索时间比传统RRT减少约53%,搜索覆盖率减少约28%,能够解决地下狭窄空间通道较多的环境中路径规划困难的问题.展开更多
In order to solve the sensing and motion uncertainty problem of motion planning in narrow passage environment,a partition sampling strategy based on partially observable Markov decision process(POMDP)was proposed.The ...In order to solve the sensing and motion uncertainty problem of motion planning in narrow passage environment,a partition sampling strategy based on partially observable Markov decision process(POMDP)was proposed.The method combines partition sampling strategy and can improve the success rate of the robot motion planning in the narrow passage.Firstly,the environment is divided into open area and narrow area by using a partition sampling strategy,and generates the initial trajectory of the robot with fewer sampling points.Secondly,the method can calculate a local optimal solution of the initial nominal trajectory by solving POMDP problem,and iterates an overall optimal trajectory of robot motion.The proposed method follows the general POMDP solution framework,in which the belief dynamics is approximated by an extended Kalman filter(EKF),and the value function is represented by an effective quadratic function in the belief space near the nominal trajectory.Using a belief space variant of iterative linear quadratic Gaussian(iLQG)to perform the value iteration,which results in a linear control policy over the belief space that is locally optimal around the nominal trajectory.A new nominal trajectory is generated by executing the control strategy iteration,and the process is repeated until it converges to a locally optimal solution.Finally,the robot gets the optimal trajectory to safely pass through a narrow passage.The experimental results show that the proposed method can efficiently improves the performance of motion planning under uncertainty.展开更多
An experimental study is made on the flow characteristics of two-phase mixture of air and water flowing through the annular passage with three different clearances at normal temperature and pressure.It is pointed out ...An experimental study is made on the flow characteristics of two-phase mixture of air and water flowing through the annular passage with three different clearances at normal temperature and pressure.It is pointed out that the two phase pressure drop is related to the geometric parameters of the annular narrow passage.A two-phase pressure drop correlation,which takes the geometric parameters of the annular narrow passage into account,is propsed by applying the separated flow model.The predicted values of the correlation agreement with the experimental data.展开更多
针对无人机在障碍间存在狭窄通道的城市环境中进行低空航路规划的问题,根据障碍之间的空间几何关系确定障碍之间的狭窄通道,再综合所有狭窄通道生成复杂环境中的狭窄通道路径树。设计了结合狭窄通道路径树的双向快速扩展随机树(Rapidly-...针对无人机在障碍间存在狭窄通道的城市环境中进行低空航路规划的问题,根据障碍之间的空间几何关系确定障碍之间的狭窄通道,再综合所有狭窄通道生成复杂环境中的狭窄通道路径树。设计了结合狭窄通道路径树的双向快速扩展随机树(Rapidly-exploring Random Tree,RRT)算法,在两棵搜索树的扩展过程中,通过判断搜索树与狭窄通道路径树的位置关系,将狭窄通道路径树添加到搜索树上,实现搜索树在狭窄通道中的快速扩展,减少两棵搜索树的无用扩展,提升航路树生成的速度。仿真结果表明,该方法能够解决无人机在存在狭窄通道的复杂环境中进行快速有效航路规划的问题。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supported by the National Key R&D Program of China(2018YFB1307400).
文摘Path planning is a crucial concern in the field of mobile robotics,particularly in complex scenarios featuring narrow passages.Sampling-based planners,such as the widely utilized probabilistic roadmap(PRM),have been extensively employed in various robot applications.However,PRM’s utilization of random node sampling often results in disconnected graphs,posing a significant challenge when dealing with narrow passages.In order to tackle this issue,we present equipotential line sampling strategy for probabilistic roadmap(EPL-PRM),a novel approach derived from PRM.This paper initially proposes a sampling potential field,followed by the construction of equipotential lines that are denser in the proximity of obstacles and narrow passages.Random sampling is subsequently conducted along these lines.Consequently,the sampling strategy enhances the likelihood of sampling nodes around obstacles and narrow passages,thereby addressing the issue of sparsity encountered in traditional sampling-based planners.Furthermore,we introduce a nodal optimization method based on an artificial repulsive field,which prompts sampled nodes to move in the direction of repulsion.As a result,nodes around obstacles are distributed more uniformly,while nodes within narrow passages gravitate toward the middle of the passages.Finally,extensive simulations are conducted to evaluate the proposed method.The results demonstrate that our approach achieves path planning with superior efficiency,lower cost,and higher reliability compared with traditional algorithms.
基金supported by the National Natural Science Foundation of China(61701270)Young Doctor Cooperation Foundation of Qilu University of Technology(Shandong Academy of Sciences)(2017BSHZ008)。
文摘In order to solve the sensing and motion uncertainty problem of motion planning in narrow passage environment,a partition sampling strategy based on partially observable Markov decision process(POMDP)was proposed.The method combines partition sampling strategy and can improve the success rate of the robot motion planning in the narrow passage.Firstly,the environment is divided into open area and narrow area by using a partition sampling strategy,and generates the initial trajectory of the robot with fewer sampling points.Secondly,the method can calculate a local optimal solution of the initial nominal trajectory by solving POMDP problem,and iterates an overall optimal trajectory of robot motion.The proposed method follows the general POMDP solution framework,in which the belief dynamics is approximated by an extended Kalman filter(EKF),and the value function is represented by an effective quadratic function in the belief space near the nominal trajectory.Using a belief space variant of iterative linear quadratic Gaussian(iLQG)to perform the value iteration,which results in a linear control policy over the belief space that is locally optimal around the nominal trajectory.A new nominal trajectory is generated by executing the control strategy iteration,and the process is repeated until it converges to a locally optimal solution.Finally,the robot gets the optimal trajectory to safely pass through a narrow passage.The experimental results show that the proposed method can efficiently improves the performance of motion planning under uncertainty.
文摘An experimental study is made on the flow characteristics of two-phase mixture of air and water flowing through the annular passage with three different clearances at normal temperature and pressure.It is pointed out that the two phase pressure drop is related to the geometric parameters of the annular narrow passage.A two-phase pressure drop correlation,which takes the geometric parameters of the annular narrow passage into account,is propsed by applying the separated flow model.The predicted values of the correlation agreement with the experimental data.
文摘针对无人机在障碍间存在狭窄通道的城市环境中进行低空航路规划的问题,根据障碍之间的空间几何关系确定障碍之间的狭窄通道,再综合所有狭窄通道生成复杂环境中的狭窄通道路径树。设计了结合狭窄通道路径树的双向快速扩展随机树(Rapidly-exploring Random Tree,RRT)算法,在两棵搜索树的扩展过程中,通过判断搜索树与狭窄通道路径树的位置关系,将狭窄通道路径树添加到搜索树上,实现搜索树在狭窄通道中的快速扩展,减少两棵搜索树的无用扩展,提升航路树生成的速度。仿真结果表明,该方法能够解决无人机在存在狭窄通道的复杂环境中进行快速有效航路规划的问题。
