Fang Chenzhe, Wang Jinpeng, Yuan Fei, Chen Sunan, Zhou Hongping
College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.
Sensors (Basel). 2025 Apr 27;25(9):2773. doi: 10.3390/s25092773.
This paper proposes an enhanced RRT* algorithm (XN-RRT*) to address the challenges of low path planning efficiency and suboptimal picking success rates in complex pitaya harvesting environments. The algorithm generates sampling points based on normal distribution and dynamically adjusts the center and range of the sampling distribution according to the target distance and tree density, thus reducing redundant sampling. An improved artificial potential field method is employed during tree expansion, incorporating adjustment factors and target points to refine the guidance of sampling points and overcome local optima and infeasible targets. A greedy algorithm is then used to remove redundant nodes, shorten the path, and apply cubic B-spline curves to smooth the path, improving the stability and continuity of the robotic arm. Simulations in both two-dimensional and three-dimensional environments demonstrate that the XN-RRT* algorithm performs effectively, with fewer iterations, high convergence efficiency, and superior path quality. The simulation of a six-degree-of-freedom robotic arm in a pitaya orchard environment using the ROS2 platform shows that the XN-RRT* algorithm achieves a 98% picking path planning success rate, outperforming the RRT* algorithm by 90.32%, with a 27.12% reduction in path length and a 14% increase in planning success rate. The experimental results confirm that the proposed algorithm exhibits excellent overall performance in complex harvesting environments, offering a valuable reference for robotic arm path planning.
本文提出了一种增强型RRT算法（XN-RRT），以应对复杂火龙果采摘环境中路径规划效率低和采摘成功率次优的挑战。该算法基于正态分布生成采样点，并根据目标距离和树木密度动态调整采样分布的中心和范围，从而减少冗余采样。在树木扩展过程中采用了改进的人工势场法，纳入调整因子和目标点以优化采样点的引导，克服局部最优和不可行目标。然后使用贪心算法去除冗余节点、缩短路径，并应用三次B样条曲线平滑路径，提高机器人手臂的稳定性和连续性。二维和三维环境中的仿真表明，XN-RRT算法有效执行，迭代次数更少、收敛效率高且路径质量优越。使用ROS2平台在火龙果果园环境中对六自由度机器人手臂进行的仿真表明，XN-RRT算法实现了98%的采摘路径规划成功率，比RRT*算法高出90.32%，路径长度减少27.12%，规划成功率提高14%。实验结果证实，所提出的算法在复杂采摘环境中表现出优异的整体性能，为机器人手臂路径规划提供了有价值的参考。
