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The purpose of this paper is to provide a method for the generation of information machines for part mating process diagnosis. Recognition of contact states between parts during robotized part mating represents a significant element of the system for active compliant robot motion. All proposed information machines for contact states recognition will recognize one of the possible contact states even when irregular events in the process occur, and the active motion planner will continue to send commands to robot controller according to the planned trajectory.

The presented framework is based on the general theory of automata and formal languages. Starting from possible regular contact states transitions in part mating, the authors create an automaton for diagnostics, which, besides regular, accepts all irregular (observable and unobservable) process sequences.

Contact states do not appear arbitrarily during regular processes, but in certain context. Theory of automata represents a solid basis for contextual recognition and diagnosis of irregularities in part mating.

The proposed methodology is elaborated and experimentally verified using an example of cylindrical part mating, and stick-slip effect as an observable irregularity. The future work will address the generation of diagnosers for other types of part mating tasks and extension of the set of observable irregularities.

The process diagnosis increases the robustness of active compliant motion system.

Although very important feedback information provider for active motion planner, part mating process monitoring was not frequently addressed in the past. In this paper, the authors propose a methodology for generation of part mating process diagnoser that is based on general automata theory.