Decentralized and Centralized Planning for Multi-Robot Additive Manufacturing

Abstract In this paper, we present a decentralized approach to scheduling multi-robot cooperative additive manufacturing. The results of the decentralized approach are compared with those obtained from an optimization-based method, representing the class of centralized approaches for manufacturing scheduling. Two case studies are conducted to evaluate both approaches in total makespan. In the first case, four rectangular bars of different dimensions from small to large are printed. Each bar is first divided into small subtasks (called chunks), and four robots are then assigned to print the resulting chunks. The second case study focuses on testing geometric complexity, where four robots are used to print a mask stencil (an inverse stencil, not face covering). The result shows that the centralized approach provides a better solution compared to the decentralized approach for small-scale problems. However, the gap between the solutions shrinks while the scale increases. Additionally, the runtime for the centralized approach increased by 39-fold for the extra-large problem compared to the small-scale problem. In contrast, the runtime for the decentralized approach was not affected by the scale of the problem. Finally, a Monte Carlo analysis was performed to evaluate the robustness of the centralized approach against AM uncertainties. The result shows that the variations in the printing time of different robots can lead to a significant discrepancy between the generated optimal plan and the actual implementation, thereby causing collisions between robots that should have not happened if there were no uncertainties.