Reconfigurable Manufacturing Systems (RMS) is a modern engineering technology addressing changes in manufactured products via rapid reconfiguration and improved flexibility of manufacturing systems – machines, controllers, design methods, and software modules. RMS consists of networked robotic machines working in collaboration with humans. To transition existing machine tools and industrial robots to RMS, it is necessary to enable self-awareness in these robots, namely, real-world situational awareness and a capability to adapt their modes of operation to the situation. Self-awareness is a computational capability for which this proposal provides estimation, planning, and control technology.
Briefly, the proposed technology relies on capturing user specifications in the form of Linear Temporal Logic (LTL) formulas. The proposed technology uses advanced graph search algorithms to find an optimal sequence of decisions or actions (namely, the shortest path) to satisfy these specifications, which can adapt quickly to changing requirements. The long-term project objective is to develop mathematical, algorithmic, and simulation tools to enable a networked collaborative self-aware robotic RMS solution to be prototyped at Warner-Robins Air Logistics Complex and later commercialized for deployment in other manufacturing facilities.