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
针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权...针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权值系数,提高随机树扩展的导向性和灵活性;同时采用局部节点过滤机制,过滤局部区域内聚集的节点;最后,使用节点直连策略对初始路径进行优化处理。仿真实验的结果表明,改进的RRT算法规划路径的速度更快且生成的路径质量更高,充分证明了改进算法的有效可行性。展开更多
针对快速搜索随机树(rapidly-exploring random tree,RRT)算法的随机采样特征导致的收敛速度慢、路径冗余度高、采样点利用率低问题,给出一种新的解决方法。首先,根据图复杂度公式,计算出图的复杂度后确定目标偏执概率,建立偏置概率自...针对快速搜索随机树(rapidly-exploring random tree,RRT)算法的随机采样特征导致的收敛速度慢、路径冗余度高、采样点利用率低问题,给出一种新的解决方法。首先,根据图复杂度公式,计算出图的复杂度后确定目标偏执概率,建立偏置概率自适应模型;其次,在首次规划好路线后,路径中仍存在一些不必要的拐点与棱角,针对传统路径裁剪依赖局部搜索策略,可能导致次优解生成,提出PRM-Dijkstra(probabilistic roadmap-dijkstra)算法对路径进行裁剪,将改进RRT算法生成的树节点利用PRM算法相互连接起来,通过Dijkstra算法计算出一条最优路径;最后,改进RRT算法与PRM-Dijkstra种算法优势相结合,在保证有一条路径的前提下,最大概率的寻找最优路径。通过复杂图下仿真避障实验,结果显示:改进RRT算法在节点生成数量与规划用时相较传统RRT算法平均减少80%,相较于Goal-bias RRT算法均减少40%。并通过机器人操作系统(robot operating system,ROS)下的MoveIt!集成开发平台进行现实环境下避障实验,验证了算法的可行性与有效性。展开更多
面对采摘作业的复杂环境,提出了一种终点区域RRT(Goal Area RRT,GA-RRT)算法,以提高路径生成的效率并降低路径成本。根据环境系数确定初始步长与终点区域,当拓展节点进入终点区域后,随机点生成范围缩小至终点区域,同时调整步长;然后,在...面对采摘作业的复杂环境,提出了一种终点区域RRT(Goal Area RRT,GA-RRT)算法,以提高路径生成的效率并降低路径成本。根据环境系数确定初始步长与终点区域,当拓展节点进入终点区域后,随机点生成范围缩小至终点区域,同时调整步长;然后,在此基础上引入目标概率偏向方法,提高路径搜索效率;最后,对生成的路径进行简化节点处理以减少路径代价,并使用三次B样条方法平滑路径。仿真实验结果表明:二维环境下,GA-RRT算法相较于RRT、RRT-Connect算法,耗时缩短85.15%、29.86%,路径代价减少19.18%、18.26%;机械臂仿真环境下,与引入目标概率偏向方法的RRT算法进行比较,耗时缩短54.70%,路径代价减少51.59°。利用IRB120机械臂实验平台,验证了算法的可行性。展开更多
A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit ...A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit workers to complete manual operations. Artificial intelligence and robotics, which are rapidly evolving, offer potential solutions to this problem. In this paper, a navigation method dedicated to solving the issues of the inability to pass smoothly at corners in practice and local obstacle avoidance is presented. In the system, a Gaussian fitting smoothing rapid exploration random tree star-smart(GFS RRT^(*)-Smart) algorithm is proposed for global path planning and enhances the performance when the robot makes a sharp turn around corners. In local obstacle avoidance, a deep reinforcement learning determiner mixed actor critic(MAC) algorithm is used for obstacle avoidance decisions. The navigation system is implemented in a scaled-down simulation factory.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
针对六自由度工业机器人在复杂的分拣环境中分拣速度慢、避障效果差等问题,提出了一种融合人工势场(Artificial Potential Field,APF)算法的快速扩展随机树(Rapidly-exploring Random Tree,RRT)改进算法。传统RRT算法路径规划随机性强...针对六自由度工业机器人在复杂的分拣环境中分拣速度慢、避障效果差等问题,提出了一种融合人工势场(Artificial Potential Field,APF)算法的快速扩展随机树(Rapidly-exploring Random Tree,RRT)改进算法。传统RRT算法路径规划随机性强、收敛速度慢,在该算法中引入APF机制引导其向目标点进行有效扩展,减少路径搜索过程中的无效分支,提高搜索效率;优化对父系节点的选择策略,对原路径局部节点进行优化重连,提高路径质量及平滑性。根据实际分拣中可能出现的状况,在MATLAB软件中建立了3个不同的仿真场景,并将所提出的改进APF-RRT算法与传统RRT算法、APF-RRT算法进行对比仿真实验。结果表明,改进APF-RRT算法于不同分拣环境中,在路径长度、搜索时间、节点个数和迭代次数4个指标上均有一定提升,能以更高的效率搜索到更高质量的路径。展开更多
As autonomous underwater vehicles(AUVs)merely adopt the inductive obstacle avoidance mechanism to avoid collisions with underwater obstacles,path planners for underwater robots should consider the poor search efficien...As autonomous underwater vehicles(AUVs)merely adopt the inductive obstacle avoidance mechanism to avoid collisions with underwater obstacles,path planners for underwater robots should consider the poor search efficiency and inadequate collision-avoidance ability.To overcome these problems,a specific two-player path planner based on an improved algorithm is designed.First,by combing the artificial attractive field(AAF)of artificial potential field(APF)approach with the random rapidly exploring tree(RRT)algorithm,an improved AAF-RRT algorithm with a changing attractive force proportional to the Euler distance between the point to be extended and the goal point is proposed.Second,a twolayer path planner is designed with path smoothing,which combines global planning and local planning.Finally,as verified by the simulations,the improved AAF-RRT algorithm has the strongest searching ability and the ability to cross the narrow passage among the studied three algorithms,which are the basic RRT algorithm,the common AAF-RRT algorithm,and the improved AAF-RRT algorithm.Moreover,the two-layer path planner can plan a global and optimal path for AUVs if a sudden obstacle is added to the simulation environment.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.
文摘针对基本的快速搜索随机树(rapidly-exploring random tree,RRT)算法用于路径规划时存在的树扩展无导向性、密集障碍物区域规划效率低、局部区域节点聚集等问题,提出一种新的RRT改进算法。该算法采用增强的目标偏向策略,并引入可变的权值系数,提高随机树扩展的导向性和灵活性;同时采用局部节点过滤机制,过滤局部区域内聚集的节点;最后,使用节点直连策略对初始路径进行优化处理。仿真实验的结果表明,改进的RRT算法规划路径的速度更快且生成的路径质量更高,充分证明了改进算法的有效可行性。
文摘面对采摘作业的复杂环境,提出了一种终点区域RRT(Goal Area RRT,GA-RRT)算法,以提高路径生成的效率并降低路径成本。根据环境系数确定初始步长与终点区域,当拓展节点进入终点区域后,随机点生成范围缩小至终点区域,同时调整步长;然后,在此基础上引入目标概率偏向方法,提高路径搜索效率;最后,对生成的路径进行简化节点处理以减少路径代价,并使用三次B样条方法平滑路径。仿真实验结果表明:二维环境下,GA-RRT算法相较于RRT、RRT-Connect算法,耗时缩短85.15%、29.86%,路径代价减少19.18%、18.26%;机械臂仿真环境下,与引入目标概率偏向方法的RRT算法进行比较,耗时缩短54.70%,路径代价减少51.59°。利用IRB120机械臂实验平台,验证了算法的可行性。
基金National Natural Science Foundation of China (No.61903078)。
文摘A large number of logistics operations are needed to transport fabric rolls and dye barrels to different positions in printing and dyeing plants, and increasing labor cost is making it difficult for plants to recruit workers to complete manual operations. Artificial intelligence and robotics, which are rapidly evolving, offer potential solutions to this problem. In this paper, a navigation method dedicated to solving the issues of the inability to pass smoothly at corners in practice and local obstacle avoidance is presented. In the system, a Gaussian fitting smoothing rapid exploration random tree star-smart(GFS RRT^(*)-Smart) algorithm is proposed for global path planning and enhances the performance when the robot makes a sharp turn around corners. In local obstacle avoidance, a deep reinforcement learning determiner mixed actor critic(MAC) algorithm is used for obstacle avoidance decisions. The navigation system is implemented in a scaled-down simulation factory.
文摘针对六自由度工业机器人在复杂的分拣环境中分拣速度慢、避障效果差等问题,提出了一种融合人工势场(Artificial Potential Field,APF)算法的快速扩展随机树(Rapidly-exploring Random Tree,RRT)改进算法。传统RRT算法路径规划随机性强、收敛速度慢,在该算法中引入APF机制引导其向目标点进行有效扩展,减少路径搜索过程中的无效分支,提高搜索效率;优化对父系节点的选择策略,对原路径局部节点进行优化重连,提高路径质量及平滑性。根据实际分拣中可能出现的状况,在MATLAB软件中建立了3个不同的仿真场景,并将所提出的改进APF-RRT算法与传统RRT算法、APF-RRT算法进行对比仿真实验。结果表明,改进APF-RRT算法于不同分拣环境中,在路径长度、搜索时间、节点个数和迭代次数4个指标上均有一定提升,能以更高的效率搜索到更高质量的路径。
基金Supported by Zhejiang Key R&D Program 558 No.2021C03157the“Construction of a Leading Innovation Team”project by the Hangzhou Munic-559 ipal government,the Startup funding of New-joined PI of Westlake University with Grant No.560(041030150118)the funding support from the Westlake University and Bright Dream Joint In-561 stitute for Intelligent Robotics.
文摘As autonomous underwater vehicles(AUVs)merely adopt the inductive obstacle avoidance mechanism to avoid collisions with underwater obstacles,path planners for underwater robots should consider the poor search efficiency and inadequate collision-avoidance ability.To overcome these problems,a specific two-player path planner based on an improved algorithm is designed.First,by combing the artificial attractive field(AAF)of artificial potential field(APF)approach with the random rapidly exploring tree(RRT)algorithm,an improved AAF-RRT algorithm with a changing attractive force proportional to the Euler distance between the point to be extended and the goal point is proposed.Second,a twolayer path planner is designed with path smoothing,which combines global planning and local planning.Finally,as verified by the simulations,the improved AAF-RRT algorithm has the strongest searching ability and the ability to cross the narrow passage among the studied three algorithms,which are the basic RRT algorithm,the common AAF-RRT algorithm,and the improved AAF-RRT algorithm.Moreover,the two-layer path planner can plan a global and optimal path for AUVs if a sudden obstacle is added to the simulation environment.
