Employing sensor repositioning to refine spatial reasoning in an industrial robotic environment

摘要

Industrial and space applications present environments in which it is possible, and in fact desirable to solve robotic problems using a model-based approach. From a sensory standpoint, the reasons for employing knowledge about objects to be manipulated are twofold. First, such knowledge permits high-level expectation driven reasoning as opposed to low-level data driven searches for primitive features. This is advantageous since purely data driven feature extraction is typically undirected and the search space is unconstrained. The second reason is that expectation driven reasoning can exploit knowledge derived from features that have already been found, thus expediting subsequent searches. Conversely, however, there is a rigid requirement to specify the geometry and kinematics for object models about which reasoning is to occur.