Dual‐Strategy Collaborative Reinforcement Learning for Path Planning and Optimisation in Concrete 3D Printing

ABSTRACT As concrete 3D printing emerges as a transformative technology in construction, optimising printing path planning has become crucial for enhancing manufacturing efficiency and structural integrity. Conventional path planning methods often struggle with complex geometries, leading to discontinuous paths, excessive directional changes and inefficient start–stop cycles. To address these limitations, this study introduces a novel reinforcement learning (RL)–based framework incorporating a dual‐strategy coordination mechanism. The proposed approach employs two specialised deep Q‐network (DQN) models: one for continuous path optimisation and the other for start–stop sequence refinement. The state and action spaces are designed based on sliced geometric features of concrete components, while a multi‐objective reward function penalises nonprintable movements, abrupt turns and unnecessary interruptions. In addition, a path merging strategy and a G‐code generation module are incorporated to ensure path continuity and operational feasibility. Experimental evaluations across diverse complex topological structures demonstrate that the proposed approach achieves complete edge coverage, minimises idle motions and path oscillations, and significantly improves printing efficiency and geometric fidelity. The results underscore the potential of the dual‐strategy RL method as an intelligent and viable solution for advanced additive manufacturing path planning.