On-Line Reconfigurable Machines

Lara S. Crawford, Minh Binh Do, Wheeler S. Ruml, Haitham Hindi, Craig Eldershaw, Rong Zhou, Lukas Kuhn, Markus P. J. Fromherz, David Biegelsen, Johan de Kleer, Daniel Larner


A recent trend in intelligent machines and manufacturing has been toward reconfigurable manufacturing systems, which move away from the idea of 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 on-line 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 on-line 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. We describe the implementation of this design in a prototype highly modular, parallel printing system.


real time systems, planning, control, diagnosis, reconfigurable, manufacturing, intelligent machines, distributed, on-line, modular

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DOI: https://doi.org/10.1609/aimag.v34i3.2387

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