Automatic decomposition of planned assembly sequences into skill primitives

This paper presents a new method to decompose complex sequences of assembly operations into skill primitives. This can be realized by analyzing hyperarcs of the underlying AND/ORgraphs representing automatically generated assembly plans. Features like local depart spaces, symbolic spatial relations, and the necessary tools classify the type of assembly operation (peg in hole, placements, alignments, etc.). Skill primitives are robot movements or commands for grippers and tools. The unified modeling language (UML) is used to model the robot tasks and skill primitives. A robot control system uses the skill primitives as input to select the desired control scheme (position, force, or hybrid). In addition to this, we use an algorithm to identify assembly process states considering static friction under uniform gravity to execute skill primitives. This enables a robot to select and modify its motion strategies adequately according to the state of the assembly operation.