Generalized recognition of single-ended contact formations for use in automated assembly operations

Robots are preferred over any other form of automated machines for assembly tasks due to their capability of being programmed to perform a variety of tasks. However, in the present day industries, the turn around time for new designs have dramatically reduced. Therefore, the need for robots which can adapt its teaching and programming to new situations is strongly felt. This is especially true in the tasks such as assembly operations, which involve the robot making frequent contacts with its environment. This research addresses the problems that arise due to small changes in the work settings after the system has been programmed or trained. In an industry setting it is very likely that changes such as orientation and translation of the grasped object with respect to the robot axes can occur due to many unforeseen causes. The research here is focused on generalizing a Hybrid Control System, in which an assembly skill is described as a sequence of qualitative states and the desired transition between the states. In this case, the qualitative state takes the form of a single-ended contact formation, which describes how a grasped object touches its environment. Skill acquisition involves learning the sequence of qualitative states, the transition between those states, and the mapping from the sensor signals to the qualitative states. The authors discuss impact of changes in the orientation and the position of the grasped object with respect to the robot axes on the recognition of these qualitative states. They also propose a method of decreasing the performance degradation caused by this orientation change in recognition of these qualitative states, by adapting to the new situation with as minimum retraining as possible. Experimental results are presented which illustrate and validate the approach