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. 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. We describe the implementation of this design in a prototype highly modular, parallel printing system.

We believe that these goals can be attained through the use of a very high level of modularity, both in hardware and software, combined with intelligent software. To test this hypothesis, Palo Alto Research Center (PARC) designed and built a prototype highly modular system in the printing domain. This "hypermodular" printer explores the extremes of modularity, reconfigurability, and parallelism in both hardware and software. The hardware prototype connects four standard Xerox marking engines (the component of a printer that does the actual printing) in parallel using a highly modular paper path. This configuration can achieve a print rate of four times that of an individual print engine. Reconfigurable manufacturing systems supports flexibility in configuration, graceful degradation (RMSs) were introduced as a concept in the late under component failure, and rerouting of inprocess 1990s (Koren et al. 1999), but the prerequisites, in sheets under exception conditions. These both software and hardware, for implementing them capabilities were made possible by utilizing advanced successfully have proved daunting; very few examples AI techniques in model-based planning, scheduling, of RMSs exist today in practice. These prerequisites search, and temporal reasoning such as state-space include modular, reconfigurable hardware components regression planning, partial-order scheduling, temporal as well as the software and control planning graph-based heuristic estimates, multiobjective architectures and logic to support them. RMSs can search, and fast, simple temporal network include both hard reconfigurability (physical reconfiguration) reasoning. The AI planner / scheduler incorporates and soft reconfigurability (logical reconfiguration) mostly domain-independent techniques from the (ElMaraghy 2006). This latter concept planning and scheduling research community, includes the idea of flexible routing as well as replanning enabling its flexibility and configurability to be and rescheduling.