An outstanding problem in model-based recognition of objects by robot systems is how the system should proceed when the acquired data are insufficient to identify the model instance and model pose that best interpret the object. Such a situation can arise when there are multiple model instances that could be interpretations of the object, or when there are ambiguous poses of a given model instance.

This work proposes a generic method for autom atically finding a path along which the robot could move a tactile sensor, so that the robot system can uniquely and efficiently identify the object. The problem framework is defined, a methodology for finding paths is proposed, and an evaluation of the costs and benefits of sensing paths is presented, all of which must be done in the presence of geometric uncertainty about the possible locations and orientations of the object.

The two-dimensional problem is solved by a projection-space approach, in which the optim al sensing path is found by efficiently searching through the sets of paths passing through each object face. A path is sought which distinguishes as many distinct interpretations as possible, subject to design constraints. It is shown that employing realistic assumptions the problem is tractable, and that for the two-dimensional case the solution time is com parable to the robot motion time.

For the three-dimensional problem, an analysis of the structure of the path param eter space shows why the problem is inherently difficult. Several alternative solutions are examined, and a taxonomy of approaches classifies related work into a more general hierarchy of problem decompositions.