Path planning in additive manufacturing with multi-robot collaboration based on structural primitive partitioning

Abstract

Directed Energy Deposition (DED) technology is increasingly favored for swiftly fabricating large structural components due to its high printing efficiency. Despite its advantages, challenges persist in achieving satisfactory surface finish and forming precision, hindering its widespread adoption across industries. To address these issues, this paper presents a novel multi-robot collaborative path planning method based on structural primitive partitioning. This method simplifies path planning complexities and seamlessly integrates into process planning software, thereby enhancing overall functionality. This method decomposes complex polygons into tiny primitives (TP), organizing them into TP sets based on bridge and adjacency relations. These sets are then structured into first-level structural TP (F-TP) and second-level structural TP (S-TP), followed by the establishment of monotonic structural TP (M-TP). A minimum rectangular box recalculates the filling path for each M-TP, while the external contour path and internal zigzag path form a complete printing path. Additionally, an optimal printing sequence planning algorithm for multi-robot using a KD-tree-based search algorithm is presented, ensuring the shortest non-productive path and collision avoidance during printing. Experimental verification with four structures of varying geometric features demonstrates a partitioning accuracy of 99.5 % and absence of surface defects in the printed parts. The proposed method presents a viable and effective solution for enhancing the quality of parts produced via DED.