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
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
针对传统Informed-RRT*(informing rapidly-exploring random trees with path distance lower bounds)算法在机械臂避障路径规划中目标导向性不足、随机性大、规划效率低以及路径冗余造成运动不平滑等问题,提出一种改进的APF-Inform...针对传统Informed-RRT*(informing rapidly-exploring random trees with path distance lower bounds)算法在机械臂避障路径规划中目标导向性不足、随机性大、规划效率低以及路径冗余造成运动不平滑等问题,提出一种改进的APF-Informed RRT*算法。在采样阶段,引入人工势场法引导采样点,提高随机树扩展的目标导向性。在树生长阶段,通过碰撞检测缓存键和自适应变步长策略,缓存碰撞检测结果,并生成唯一标识为缓存键,通过与障碍物的距离动态调整步长,减少碰撞检测次数,提高搜索效率。在优化阶段,采用分段式贪心算法和三次B样条曲线优化路径。通过与Informed-RRT*、GoalBias-RRT*和RRT*算法在三维环境下的仿真对比实验。结果表明,相较于Informed-RRT*算法,APF-Informed RRT*算法路径长度减少14.08%,时间节约96.17%;与RRT*和GoalBias-RRT*算法相比,APF-Informed RRT*算法路径长度分别减少19.89%和12.24%,时间分别节约88.52%和12.5%,算法的搜索效率得到显著的提升。将算法应用到AUBO i5机械臂平台,机械臂能够成功避开障碍物并准确到达目标点,且各关节的运动曲线平滑,进一步证明了改进算法的有效性。展开更多
针对RRT(Rapidly-exploring Random Tree)算法在机器人路径规划过程存在采样点随机性高、算法效率低、路径规划时间长以及规划路径冗长等问题,文中提出一种结合人工势场法的双向RRT路径规划算法。将传统RRT算法中单向扩展方式改为由起...针对RRT(Rapidly-exploring Random Tree)算法在机器人路径规划过程存在采样点随机性高、算法效率低、路径规划时间长以及规划路径冗长等问题,文中提出一种结合人工势场法的双向RRT路径规划算法。将传统RRT算法中单向扩展方式改为由起点和终点同时进行扩展,在节点扩展时加入人工势场法进行引导,增加节点扩展的目的性。将固定步长改换为可变步长,使随机树可以更快地向目标点扩展。对生成路径进行剪枝处理,删除路径中的冗余节点,进一步缩短路径长度。利用MATLAB仿真平台在相同环境下对比所提改进算法与RRT-Connect算法、DRRT-Connect(Dynamic Rapidly-exploring Random Tree Connect)算法、GB(Goal-Biased)-RRT算法、A*算法、PRM(Probabilistic Road Map)算法的路径规划效果。仿真结果表明,所提改进算法与其他改进算法相比最短路径缩短了7%,最短搜索时间降低了65%,提高了算法的规划效率。将所提算法应用于机器人,结果证明了其具有较强可行性。展开更多
为提高果园自主移动机器人在山区、丘陵等复杂地形环境下运动路径规划效果,增强机器人野外工作性能,针对果园非结构化布局和起伏地形,本文提出了一种基于改进PF-RRT*(Plane fitting-rapidly exploring random trees star)算法的果园机...为提高果园自主移动机器人在山区、丘陵等复杂地形环境下运动路径规划效果,增强机器人野外工作性能,针对果园非结构化布局和起伏地形,本文提出了一种基于改进PF-RRT*(Plane fitting-rapidly exploring random trees star)算法的果园机器人路径规划方法。利用自适应采样步长策略,在果园不同障碍物密度环境下能够灵活地调节随机树扩展步长。采用自适应目标偏置的势场引导采样方法,能够有效引导随机树避开障碍物并向目标点扩展。此外,在随机树扩展过程引入地形评估函数。通过平面拟合技术对地形坡度、稀疏度和粗糙度进行综合评估,确保规划的路径在复杂地形中可通过性和安全性。最后,采用三次样条插值与高斯过程回归对路径进行平滑和优化。在仿真环境中,改进PF-RRT*算法与RRT*、Q-RRT*和PF-RRT*算法相比,轨迹偏差最多减少46.97%,路径长度最多减少7.64%,规划时间最多减少23.58%,且成功率和避障效果均优于对比算法。真实果园行间试验结果表明,与RRT*、Q-RRT*和PF-RRT*算法相比,改进PF-RRT*算法与理想轨迹偏差量减少34.04%、26.19%、27.91%、路径长度减少6.58%、3.16%和4.10%、规划时间减少30.56%、18.25%和33.52%。表明本文算法在果园复杂地形下能够实现自主移动机器人最优路径规划与导航。展开更多
针对快速扩展随机树(Rapidly-Exploring Random Tree,RRT)算法在结合无人船进行路径规划时存在规划时间长、路径冗余大、路径平滑度不符合欠驱动无人船航行要求等问题,提出一种改进RRT的无人船全局路径规划算法。算法中将贝叶斯优化算...针对快速扩展随机树(Rapidly-Exploring Random Tree,RRT)算法在结合无人船进行路径规划时存在规划时间长、路径冗余大、路径平滑度不符合欠驱动无人船航行要求等问题,提出一种改进RRT的无人船全局路径规划算法。算法中将贝叶斯优化算法融入目标采样过程,增强目标点采样导向性;引入动态步长和双向贪心剪枝策略作为重要辅助,进一步提升算法效率和路径质量;得到初始路径后采用动态权重3次B样条曲线进一步平滑处理。最后在3种类型障碍物环境下进行仿真实验并与RRT、RRT*算法进行对比。结果表明,改进RRT算法在规划时长、路径长度以及路径质量等方面有明显优势。改进后算法效率更高,路径平滑度更高,研究成果可为无人船自主航行提供参考。展开更多
针对无人船在障碍物环境中路径规划效率低、路径冗余大、平滑性差等问题,提出一种融合人工势场法的改进RRT*算法(Artificial Potential Field Rapidly-exploring Random Tree*,APF-RRT*)。该算法以人工势场模型为引导机制,构...针对无人船在障碍物环境中路径规划效率低、路径冗余大、平滑性差等问题,提出一种融合人工势场法的改进RRT*算法(Artificial Potential Field Rapidly-exploring Random Tree*,APF-RRT*)。该算法以人工势场模型为引导机制,构建包含信息激励势场、引力势场和斥力势场的复合势能函数,从而有目的性地引导采样点朝向目标区域并远离障碍物,提高算法采样效率;同时引入动态步长策略加快扩展速度,最后结合贪心剪枝和路径平滑机制优化初始路径。在2种典型环境下进行仿真对比实验,结果表明文中改进RRT*算法在规划效率、路径长度、平滑度以及稳定性方面优于传统算法,平均规划时长、路径长度、初始路径节点数与改进前相比分别减少了50.88%、6.24%、30.29%。改进算法为无人船在障碍物环境下生成安全、高效的航行路径提供了有力保障。展开更多
基金supported by Gansu Provincial Science and Technology Program Project(No.23JRRA868)Lanzhou Municipal Talent Innovation and Entrepreneurship Project(No.2019-RC-103)。
摘要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.
摘要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.
摘要针对传统Informed-RRT*(informing rapidly-exploring random trees with path distance lower bounds)算法在机械臂避障路径规划中目标导向性不足、随机性大、规划效率低以及路径冗余造成运动不平滑等问题,提出一种改进的APF-Informed RRT*算法。在采样阶段,引入人工势场法引导采样点,提高随机树扩展的目标导向性。在树生长阶段,通过碰撞检测缓存键和自适应变步长策略,缓存碰撞检测结果,并生成唯一标识为缓存键,通过与障碍物的距离动态调整步长,减少碰撞检测次数,提高搜索效率。在优化阶段,采用分段式贪心算法和三次B样条曲线优化路径。通过与Informed-RRT*、GoalBias-RRT*和RRT*算法在三维环境下的仿真对比实验。结果表明,相较于Informed-RRT*算法,APF-Informed RRT*算法路径长度减少14.08%,时间节约96.17%;与RRT*和GoalBias-RRT*算法相比,APF-Informed RRT*算法路径长度分别减少19.89%和12.24%,时间分别节约88.52%和12.5%,算法的搜索效率得到显著的提升。将算法应用到AUBO i5机械臂平台,机械臂能够成功避开障碍物并准确到达目标点,且各关节的运动曲线平滑,进一步证明了改进算法的有效性。
摘要针对RRT(Rapidly-exploring Random Tree)算法在机器人路径规划过程存在采样点随机性高、算法效率低、路径规划时间长以及规划路径冗长等问题,文中提出一种结合人工势场法的双向RRT路径规划算法。将传统RRT算法中单向扩展方式改为由起点和终点同时进行扩展,在节点扩展时加入人工势场法进行引导,增加节点扩展的目的性。将固定步长改换为可变步长,使随机树可以更快地向目标点扩展。对生成路径进行剪枝处理,删除路径中的冗余节点,进一步缩短路径长度。利用MATLAB仿真平台在相同环境下对比所提改进算法与RRT-Connect算法、DRRT-Connect(Dynamic Rapidly-exploring Random Tree Connect)算法、GB(Goal-Biased)-RRT算法、A*算法、PRM(Probabilistic Road Map)算法的路径规划效果。仿真结果表明,所提改进算法与其他改进算法相比最短路径缩短了7%,最短搜索时间降低了65%,提高了算法的规划效率。将所提算法应用于机器人,结果证明了其具有较强可行性。
摘要为提高果园自主移动机器人在山区、丘陵等复杂地形环境下运动路径规划效果,增强机器人野外工作性能,针对果园非结构化布局和起伏地形,本文提出了一种基于改进PF-RRT*(Plane fitting-rapidly exploring random trees star)算法的果园机器人路径规划方法。利用自适应采样步长策略,在果园不同障碍物密度环境下能够灵活地调节随机树扩展步长。采用自适应目标偏置的势场引导采样方法,能够有效引导随机树避开障碍物并向目标点扩展。此外,在随机树扩展过程引入地形评估函数。通过平面拟合技术对地形坡度、稀疏度和粗糙度进行综合评估,确保规划的路径在复杂地形中可通过性和安全性。最后,采用三次样条插值与高斯过程回归对路径进行平滑和优化。在仿真环境中,改进PF-RRT*算法与RRT*、Q-RRT*和PF-RRT*算法相比,轨迹偏差最多减少46.97%,路径长度最多减少7.64%,规划时间最多减少23.58%,且成功率和避障效果均优于对比算法。真实果园行间试验结果表明,与RRT*、Q-RRT*和PF-RRT*算法相比,改进PF-RRT*算法与理想轨迹偏差量减少34.04%、26.19%、27.91%、路径长度减少6.58%、3.16%和4.10%、规划时间减少30.56%、18.25%和33.52%。表明本文算法在果园复杂地形下能够实现自主移动机器人最优路径规划与导航。
摘要针对快速扩展随机树(Rapidly-Exploring Random Tree,RRT)算法在结合无人船进行路径规划时存在规划时间长、路径冗余大、路径平滑度不符合欠驱动无人船航行要求等问题,提出一种改进RRT的无人船全局路径规划算法。算法中将贝叶斯优化算法融入目标采样过程,增强目标点采样导向性;引入动态步长和双向贪心剪枝策略作为重要辅助,进一步提升算法效率和路径质量;得到初始路径后采用动态权重3次B样条曲线进一步平滑处理。最后在3种类型障碍物环境下进行仿真实验并与RRT、RRT*算法进行对比。结果表明,改进RRT算法在规划时长、路径长度以及路径质量等方面有明显优势。改进后算法效率更高,路径平滑度更高,研究成果可为无人船自主航行提供参考。
摘要针对无人船在障碍物环境中路径规划效率低、路径冗余大、平滑性差等问题,提出一种融合人工势场法的改进RRT*算法(Artificial Potential Field Rapidly-exploring Random Tree*,APF-RRT*)。该算法以人工势场模型为引导机制,构建包含信息激励势场、引力势场和斥力势场的复合势能函数,从而有目的性地引导采样点朝向目标区域并远离障碍物,提高算法采样效率;同时引入动态步长策略加快扩展速度,最后结合贪心剪枝和路径平滑机制优化初始路径。在2种典型环境下进行仿真对比实验,结果表明文中改进RRT*算法在规划效率、路径长度、平滑度以及稳定性方面优于传统算法,平均规划时长、路径长度、初始路径节点数与改进前相比分别减少了50.88%、6.24%、30.29%。改进算法为无人船在障碍物环境下生成安全、高效的航行路径提供了有力保障。