The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operationa...The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.展开更多
In response to the problems of low sampling efficiency,strong randomness of sampling points,and the tortuous shape of the planned path in the traditional rapidly-exploring random tree(RRT)algorithm and bidirectional R...In response to the problems of low sampling efficiency,strong randomness of sampling points,and the tortuous shape of the planned path in the traditional rapidly-exploring random tree(RRT)algorithm and bidirectional RRT algorithm used for unmanned aerial vehicle(UAV)path planning in complex environments,an improved bidirectional RRT algorithm was proposed.The algorithm firstly adopted a goal-oriented strategy to guide the sampling points towards the target point,and then the artificial potential field acted on the random tree nodes to avoid collision with obstacles and reduced the length of the search path,and the random tree node growth also combined the UAV’s own flight constraints,and by combining the triangulation method to remove the redundant node strategy and the third-order B-spline curve for the smoothing of the trajectory,the planned path was better.The planned paths were more optimized.Finally,the simulation experiments in complex and dynamic environments showed that the algorithm effectively improved the speed of trajectory planning and shortened the length of the trajectory,and could generate a safe,smooth and fast trajectory in complex environments,which could be applied to online trajectory planning.展开更多
A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of...A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.展开更多
针对传统RRT(rapidly exploring random tree)算法在复杂环境下收敛速度慢、存在重复采样、缺乏目标导向性和规划的路径质量不高的问题,提出一种贪婪搜索和目标导向的RRT算法(RRT-D算法),在传统RRT算法的基础上,改进节点的采样方式和父...针对传统RRT(rapidly exploring random tree)算法在复杂环境下收敛速度慢、存在重复采样、缺乏目标导向性和规划的路径质量不高的问题,提出一种贪婪搜索和目标导向的RRT算法(RRT-D算法),在传统RRT算法的基础上,改进节点的采样方式和父节点的选取策略,取消步长限制,通过贪婪式的搜索方式一次生长10个候选节点,选取符合条件的且距离目标点最近的候选点作为子节点生长到树中,提高了算法的搜索能力,降低了路径代价;用动态减少重复搜索区域的方式减少了无效搜索;每次采样后判断采样点能否与目标点直接相连,增加了采样的目标导向性,提高了搜索效率,遍历全树构成无向图时,可根据总采样点数量,通过限制无向图边的长度来减少边的数量,由Dijkstra算法搜索代价最小的路径;最后由分段三次Hermite插值函数对路径进行平滑处理。试验结果表明,与传统RRT算法相比,RRT-D算法不仅大幅缩短了规划时间,而且得到的路径代价更小、更加平滑,节点的利用率更高,验证了RRT-D算法在路径规划中的优势。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
为使机械臂在料框分拣应用中快速规划出较优的拾取路径,提出一种融合人工势场的改进RRT(rapidly-exploring random tree)机械臂路径规划方法。首先,利用人工势场进行预规划,在预规划路径上找到能够与目标节点无碰撞直连的路径节点,并将...为使机械臂在料框分拣应用中快速规划出较优的拾取路径,提出一种融合人工势场的改进RRT(rapidly-exploring random tree)机械臂路径规划方法。首先,利用人工势场进行预规划,在预规划路径上找到能够与目标节点无碰撞直连的路径节点,并将其作为RRT的规划目标节点,避免对空白区域的无用搜索。其次,在RRT算法基础上加入目标引导采样以及搜索参数自适应计算策略,提高算法的指向性以及鲁棒性。引入一种基于机械臂末端姿态约束的采样节点拒绝机制,降低有效性检查次数,提高规划效率。最后,对生成的原始路径进行剪枝优化,降低路径代价与转角数量,随后利用准均匀三次B样条结合四元数球面姿态插值进行平滑优化,提高路径质量。实验结果表明,所提出的改进算法与RRT算法相比,规划成功率提高了12.66%,规划时间与路径成本分别降低了79.05%以及34.80%。通过消融实验证明了各部分改进的有效性。在硬件平台上进行分拣测试,验证了该方法的实用性。展开更多
为加快末端物流配送的效率,提出一种配送无人机的航迹规划问题。针对传统快速搜索随机树(rapidlysearch random tree,RRT)算法在航迹规划中存在的盲目性和路径不平滑等问题,将人工势场法(artificial potential field,APF)与Informed-RRT...为加快末端物流配送的效率,提出一种配送无人机的航迹规划问题。针对传统快速搜索随机树(rapidlysearch random tree,RRT)算法在航迹规划中存在的盲目性和路径不平滑等问题,将人工势场法(artificial potential field,APF)与Informed-RRT^(*)算法融合,提出一种自适应步长增长策略的改进APF-Informed-RRT^(*)算法。首先在选择新节点时,考虑到障碍物和目标点的影响,提出一种自适应步长增长策略来解决采样的盲目性;其次采用三次B样条对拐点处进行平滑处理;最后分别采用RRT^(*)算法、Informed-RRT^(*)算法和改进APF-Informed-RRT^(*)算法在两种环境中进行仿真实验。结果表明,改进APF-Informed-RRT^(*)算法相较于RRT^(*)算法和Informed-RRT^(*)算法,在运行时间、迭代次数以及路径平滑上都得到提升。展开更多
针对RRT(rapidly exploring random tree)路径规划算法搜索范围大、目标导向差、容易陷入局部最小值以及路径曲折等问题,提出了一种限制自适应采样区域的改进RRT路径规划算法。将整个搜索空间划分成均匀的等级,根据新节点所在等级和该...针对RRT(rapidly exploring random tree)路径规划算法搜索范围大、目标导向差、容易陷入局部最小值以及路径曲折等问题,提出了一种限制自适应采样区域的改进RRT路径规划算法。将整个搜索空间划分成均匀的等级,根据新节点所在等级和该等级内采样点数量动态调整采样区域,减小搜索范围;利用新节点改进策略使随机树根据环境信息自适应地向目标点调整,并改变扩展步长生成新节点;利用障碍物躲避策略提高算法的目标导向性和躲避障碍物的性能;利用改进的逆向寻优和插入节点并减小转向角的三次B样条曲线对路径进行优化处理。该算法在不同的路径环境中相较于RRT算法的搜索时间和迭代次数均减少了70%以上,且经过优化的路径更短、更平滑。展开更多
文摘The burgeoning robotics industry has catalyzed significant strides in the development and deployment of industrial and service robotic arms, positioning path planning as a pivotal facet for augmenting their operational safety and efficiency. Existing path planning algorithms, while capable of delineating feasible trajectories, often fall short of achieving optimality, particularly concerning path length, search duration, and success likelihood. This study introduces an enhanced Rapidly-Exploring Random Tree (RRT) algorithm, meticulously designed to rectify the issues of node redundancy and the compromised path quality endemic to conventional RRT approaches. Through the integration of an adaptive pruning mechanism and a dynamic elliptical search strategy within the Informed RRT* framework, our algorithm efficiently refines the search tree by discarding branches that surpass the cost of the optimal path, thereby refining the search space and significantly boosting efficiency. Extensive comparative analysis across both two-dimensional and three-dimensional simulation settings underscores the algorithm’s proficiency in markedly improving path precision and search velocity, signifying a breakthrough in the domain of robotic arm path planning.
基金supported by Gansu Provincial Science and Technology Program Project(No.23JRRA868)Lanzhou Municipal Talent Innovation and Entrepreneurship Project(No.2019-RC-103)。
文摘In response to the problems of low sampling efficiency,strong randomness of sampling points,and the tortuous shape of the planned path in the traditional rapidly-exploring random tree(RRT)algorithm and bidirectional RRT algorithm used for unmanned aerial vehicle(UAV)path planning in complex environments,an improved bidirectional RRT algorithm was proposed.The algorithm firstly adopted a goal-oriented strategy to guide the sampling points towards the target point,and then the artificial potential field acted on the random tree nodes to avoid collision with obstacles and reduced the length of the search path,and the random tree node growth also combined the UAV’s own flight constraints,and by combining the triangulation method to remove the redundant node strategy and the third-order B-spline curve for the smoothing of the trajectory,the planned path was better.The planned paths were more optimized.Finally,the simulation experiments in complex and dynamic environments showed that the algorithm effectively improved the speed of trajectory planning and shortened the length of the trajectory,and could generate a safe,smooth and fast trajectory in complex environments,which could be applied to online trajectory planning.
基金This research was funded by National Natural Science Foundation of China(No.62063006)Guangxi Science and Technology Major Program(No.2022AA05002)+2 种基金Key Laboratory of AI and Information Processing(Hechi University),Education Department of Guangxi Zhuang Autonomous Region(No.2022GXZDSY003)Guangxi Key Laboratory of Spatial Information and Geomatics(Guilin University of Technology)(No.21-238-21-16)Innovation Project of Guangxi Graduate Education(No.YCSW2023352).
文摘A Rapid-exploration Random Tree(RRT)autonomous detection algorithm based on the multi-guide-node deflection strategy and Karto Simultaneous Localization and Mapping(SLAM)algorithm was proposed to solve the problems of low efficiency of detecting frontier boundary points and drift distortion in the process of map building in the traditional RRT algorithm in the autonomous detection strategy of mobile robot.Firstly,an RRT global frontier boundary point detection algorithm based on the multi-guide-node deflection strategy was put forward,which introduces the reference value of guide nodes’deflection probability into the random sampling function so that the global search tree can detect frontier boundary points towards the guide nodes according to random probability.After that,a new autonomous detection algorithm for mobile robots was proposed by combining the graph optimization-based Karto SLAM algorithm with the previously improved RRT algorithm.The algorithm simulation platform based on the Gazebo platform was built.The simulation results show that compared with the traditional RRT algorithm,the proposed RRT autonomous detection algorithm can effectively reduce the time of autonomous detection,plan the length of detection trajectory under the condition of high average detection coverage,and complete the task of autonomous detection mapping more efficiently.Finally,with the help of the ROS-based mobile robot experimental platform,the performance of the proposed algorithm was verified in the real environment of different obstacles.The experimental results show that in the actual environment of simple and complex obstacles,the proposed RRT autonomous detection algorithm was superior to the traditional RRT autonomous detection algorithm in the time of detection,length of detection trajectory,and average coverage,thus improving the efficiency and accuracy of autonomous detection.
文摘针对传统RRT(rapidly exploring random tree)算法在复杂环境下收敛速度慢、存在重复采样、缺乏目标导向性和规划的路径质量不高的问题,提出一种贪婪搜索和目标导向的RRT算法(RRT-D算法),在传统RRT算法的基础上,改进节点的采样方式和父节点的选取策略,取消步长限制,通过贪婪式的搜索方式一次生长10个候选节点,选取符合条件的且距离目标点最近的候选点作为子节点生长到树中,提高了算法的搜索能力,降低了路径代价;用动态减少重复搜索区域的方式减少了无效搜索;每次采样后判断采样点能否与目标点直接相连,增加了采样的目标导向性,提高了搜索效率,遍历全树构成无向图时,可根据总采样点数量,通过限制无向图边的长度来减少边的数量,由Dijkstra算法搜索代价最小的路径;最后由分段三次Hermite插值函数对路径进行平滑处理。试验结果表明,与传统RRT算法相比,RRT-D算法不仅大幅缩短了规划时间,而且得到的路径代价更小、更加平滑,节点的利用率更高,验证了RRT-D算法在路径规划中的优势。
文摘为使机械臂在料框分拣应用中快速规划出较优的拾取路径,提出一种融合人工势场的改进RRT(rapidly-exploring random tree)机械臂路径规划方法。首先,利用人工势场进行预规划,在预规划路径上找到能够与目标节点无碰撞直连的路径节点,并将其作为RRT的规划目标节点,避免对空白区域的无用搜索。其次,在RRT算法基础上加入目标引导采样以及搜索参数自适应计算策略,提高算法的指向性以及鲁棒性。引入一种基于机械臂末端姿态约束的采样节点拒绝机制,降低有效性检查次数,提高规划效率。最后,对生成的原始路径进行剪枝优化,降低路径代价与转角数量,随后利用准均匀三次B样条结合四元数球面姿态插值进行平滑优化,提高路径质量。实验结果表明,所提出的改进算法与RRT算法相比,规划成功率提高了12.66%,规划时间与路径成本分别降低了79.05%以及34.80%。通过消融实验证明了各部分改进的有效性。在硬件平台上进行分拣测试,验证了该方法的实用性。
文摘为加快末端物流配送的效率,提出一种配送无人机的航迹规划问题。针对传统快速搜索随机树(rapidlysearch random tree,RRT)算法在航迹规划中存在的盲目性和路径不平滑等问题,将人工势场法(artificial potential field,APF)与Informed-RRT^(*)算法融合,提出一种自适应步长增长策略的改进APF-Informed-RRT^(*)算法。首先在选择新节点时,考虑到障碍物和目标点的影响,提出一种自适应步长增长策略来解决采样的盲目性;其次采用三次B样条对拐点处进行平滑处理;最后分别采用RRT^(*)算法、Informed-RRT^(*)算法和改进APF-Informed-RRT^(*)算法在两种环境中进行仿真实验。结果表明,改进APF-Informed-RRT^(*)算法相较于RRT^(*)算法和Informed-RRT^(*)算法,在运行时间、迭代次数以及路径平滑上都得到提升。
文摘针对RRT(rapidly exploring random tree)路径规划算法搜索范围大、目标导向差、容易陷入局部最小值以及路径曲折等问题,提出了一种限制自适应采样区域的改进RRT路径规划算法。将整个搜索空间划分成均匀的等级,根据新节点所在等级和该等级内采样点数量动态调整采样区域,减小搜索范围;利用新节点改进策略使随机树根据环境信息自适应地向目标点调整,并改变扩展步长生成新节点;利用障碍物躲避策略提高算法的目标导向性和躲避障碍物的性能;利用改进的逆向寻优和插入节点并减小转向角的三次B样条曲线对路径进行优化处理。该算法在不同的路径环境中相较于RRT算法的搜索时间和迭代次数均减少了70%以上,且经过优化的路径更短、更平滑。
