Hierarchical Path Planning for On‐Board Mechanical Arm Construction Robots Based on I‐Bi‐RRT‐APF and LLM‐DT

Abstract

Path planning for construction robots in complex construction environments suffers from insufficient adaptability, poor path quality, and high computational cost. This paper proposes a hierarchical path planning method based on improved‐bidirectional‐rapidly‐exploring random tree‐artificial potential field. The proposed method integrates the following key techniques: (1) a semantically enhanced unified bounding volume hierarchy collision detection mechanism for identifying multiple categories of construction obstacle; (2) goal‐biased sampling and dynamic step size to enhance global search efficiency, combined with a modified potential field to improve obstacle avoidance; turning‐angle constraints and B‐spline smoothing are applied to optimize trajectory feasibility; (3) a weakly coupled mechanism model of the mobile vehicle and the robotic arm to reduce computational complexity. Digital twin and large language model are introduced to further enhance the engineering practicality. Ablation experiments are conducted in multiple simulation scenarios based on a scaled cable‐truss structure, demonstrating the superiority of the method in effectiveness and efficiency.