A collision-free transition path planning method for placement robots in complex environments

Abstract

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.