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
针对快速扩展随机树(Rapidly-exploring Random Tree,RRT)算法及其衍生算法路径规划时间长且规划效率低的问题,提出RRT算法与人工势场法结合的PAAPF-RRT机械臂路径规划算法,旨在最短的时间、最小的迭代次数内,在静态环境中找到连接起始...针对快速扩展随机树(Rapidly-exploring Random Tree,RRT)算法及其衍生算法路径规划时间长且规划效率低的问题,提出RRT算法与人工势场法结合的PAAPF-RRT机械臂路径规划算法,旨在最短的时间、最小的迭代次数内,在静态环境中找到连接起始点与终点的最优路径。首先,引入基于地图障碍物分布评估策略和采样区域优化策略,根据地图的障碍物分布、数量调整算法的步长以及偏向概率。然后,伴随随机树的生长,更新随机点的采样区域,保证随机树向目标点生长。其次,将RRT算法与人工势场法结合,当随机树与障碍物发生碰撞时,使用人工势场法引导随机树节点生长避开障碍物,解决了RRT算法随机树生长到障碍物附近且朝目标点生长的方向被障碍物遮挡时随机树无法生长的问题。最后,利用节点修剪策略,把算法生成的初始路径中的冗余节点进行修剪,得到拐点更少、路径更简洁的优化路径。实验结果表明,PAAPF-RRT算法在路径规划时间上对于RRT算法、GB-RRT算法以及RRT*算法分别减少了93.64%、73.58%、93.28%,在迭代次数方面分别下降了91.40%、79.64%、90.58%,在路径长度方面只占其他3种算法的79.34%、86.21%、95.58%。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.
文摘针对快速扩展随机树(Rapidly-exploring Random Tree,RRT)算法及其衍生算法路径规划时间长且规划效率低的问题,提出RRT算法与人工势场法结合的PAAPF-RRT机械臂路径规划算法,旨在最短的时间、最小的迭代次数内,在静态环境中找到连接起始点与终点的最优路径。首先,引入基于地图障碍物分布评估策略和采样区域优化策略,根据地图的障碍物分布、数量调整算法的步长以及偏向概率。然后,伴随随机树的生长,更新随机点的采样区域,保证随机树向目标点生长。其次,将RRT算法与人工势场法结合,当随机树与障碍物发生碰撞时,使用人工势场法引导随机树节点生长避开障碍物,解决了RRT算法随机树生长到障碍物附近且朝目标点生长的方向被障碍物遮挡时随机树无法生长的问题。最后,利用节点修剪策略,把算法生成的初始路径中的冗余节点进行修剪,得到拐点更少、路径更简洁的优化路径。实验结果表明,PAAPF-RRT算法在路径规划时间上对于RRT算法、GB-RRT算法以及RRT*算法分别减少了93.64%、73.58%、93.28%,在迭代次数方面分别下降了91.40%、79.64%、90.58%,在路径长度方面只占其他3种算法的79.34%、86.21%、95.58%。
