In Automated Fiber Placement (AFP), the substantial structure of the placement robot, the variable mold shapes, and the limited free space pose significant challenges for planning collision-free robot transitions. The task involves planning a collision-free path within the robot's high-dimensional configuration space. Informed RRT* is a common approach for such problems but often struggles with efficiency and path quality in environments with large informed sampling spaces influenced by obstacles. To address these issues, this paper proposes an improved Informed RRT* algorithm with a Local Knowledge Acceleration sampling strategy (LKA-Informed RRT*), aimed at enhancing planning efficiency and adaptability in complex obstacle settings. An Adaptive Sampling Control (ASC) rate is introduced, measuring the algorithm’s convergence speed, guides the algorithm to switch between informed and local sampling adaptively. The proposed local sampling method uses failure nodes from the exploration process to estimate obstacle distributions, steering sampling toward regions that expedite path convergence. Experimental results show that LKA-Informed RRT* significantly outperforms state-of-the-art algorithms in convergence efficiency and path cost. Compared to the original Informed RRT*, the proposed method reduces planning time by about 60%, substantially boosting efficiency for collision-free transitions in complex environments.

在自动纤维铺放 (AFP) 中，铺放机器人的实体结构、可变的模具形状和有限的自由空间对规划无碰撞机器人转换提出了重大挑战。该任务涉及在机器人的高维配置空间内规划一条无碰撞路径。知情 RRT* 是解决此类问题的常用方法，但在具有受障碍物影响的大型知情采样空间的环境中，通常会在效率和路径质量方面遇到困难。为了解决这些问题，本文提出了一种采用局部知识加速采样策略的改进 Informed RRT* 算法（LKA-Informed RRT*），旨在提高复杂障碍物环境中的规划效率和适应性。引入自适应采样控制（ASC）速率，衡量算法的收敛速度，引导算法在通知采样和局部采样之间自适应切换。所提出的局部采样方法使用探索过程中的故障节点来估计障碍物分布，将采样引导至加速路径收敛的区域。实验结果表明，LKA-Informed RRT* 在收敛效率和路径成本方面显着优于最先进的算法。与原始的 Informed RRT* 相比，所提出的方法减少了约 60% 的规划时间，大大提高了复杂环境中无碰撞过渡的效率。