Online reconfigurable machines


Event AI Magazine


Do, Minh B.
Hindi, Haitham
Eldershaw, Craig
Zhou, Rong
Kuhn, Lukas
Fromherz, Markus P. J.
Biegelsen, David K.
Technical Publications
October 11th 2013
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.


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.

Additional information

Focus Areas

Our work is centered around a series of Focus Areas that we believe are the future of science and technology.

Licensing & Commercialization Opportunities

We’re continually developing new technologies, many of which are available for¬†Commercialization.


PARC scientists and staffers are active members and contributors to the science and technology communities.