Citation
Crawford, L. S.; Do, M. B.; Ruml, W.; Hindi, H.; Eldershaw, C.; Zhou, R.; Kuhn, L.; Fromherz, M. P. J.; Biegelsen, D. K.; de Kleer, J.; Larner, D. L. Online reconfigurable machines. AI Magazine. Fall 2013; 34 (3): 73-88.
Abstract
A recent trend in intelligent machines and manufacturing has been toward reconfigurable manufacturing systems. Such systems move away from a fixed factory line executing an unchanging set of operations and toward the goal of an adaptable factory structure. The logical next challenge in this area is that of online reconfigurability. With this capability, machines can reconfigure while running, enable or disable capabilities in real time, and respond quickly to changes in the system or the environment (including faults). We propose an approach to achieving online reconfigurability based on a high level of system modularity supported by integrated, model-based planning and control software. Our software capitalizes on many advanced techniques from the artificial intelligence research community, particularly in model-based domain-independent planning and scheduling, heuristic search, and temporal resource reasoning. This fine-grained modularity is supported by integrated, model-based planning and control software. We describe the implementation of this design in a prototype highly modular, parallel printing system.
