On a primitive skill-based supervisory robot control architecture

Smart interaction of humanoid robots in a complex public, private or industrial environment requires the introduction of primitive skill-based discrete-continuous supervisory control concepts. The functionality of the proposed hierarchical robot supervisory control architecture captures both the hierarchy that is required for representing complex skills as well as the mechanisms for detecting failures during their execution. At first by means of several complementary (e.g. internal, optical, tactile or acoustic) sensors and by neuro-fuzzy based fusion of relevant sensor features, the actual motion phase or fault event is continuously diagnosed. Depending on the identified motion phase or random fault event, the most appropriate discrete-continuous control strategy coping optimally with the corresponding situation will be selected and executed. First experimental and simulation results are reported in this paper