Localization for Robotic Assemblies with Position Uncertainty

This dissertation deals with the class of robotic assemblies where position uncertainty far exceeds assembly clearance, and visual assistance is not available to resolve the uncertainty. Our research is motivated by actual assemblies from vehicular transmissions that fall under this class. For this class of assemblies, the focus shifts from the dynamics of the assembly to the problem of searching for part alignment. A novel idea is introduced to transform the search for part alignment into one of localizing the peg-hole misalignment on the hyper-surface formed in the peg-hole contact configuration space (C-space). This idea is developed into an intelligent localization strategy for resolving the uncertainty in the relative configuration of parts. The strategy is to explore the assembly contact C-space and match observations to a pre-acquired map of the C-space. The implementation of our localization strategy is described using both analytical and sampled maps of the contact C-space. Thus, one can either model the contact C-space using equations of the three-dimensional volumetric intersections of the mating parts, or sample it using a robot or CAD model. However, a sampled map does not provide a complete representation of the continuous contact C-space. Hence, the concepts of assembly sufficiency, goal region, and approximate localization are introduced to help in localizing sufficiently for assembly. With increasing dimensionality of the assembly uncertainty and small assembly clearances, the computational load becomes large and uneven over the localization period. An algorithm, termed the cell approach, is developed to implement the localization strategy in stages of increasing resolution, thus distributing the computational load more evenly. To make the localization strategy more robust, the application of particle filtering for robotic assemblies with position uncertainty was pioneered in this dissertation. Particle filtering is a probabilistic scheme that maintains a set of weighted particles, where each particle represents an estimate of the relative peg-hole configuration; it can handle errors in actuation and observation, and also errors in mapping. Moreover, the number of particles can be adjusted to accommodate the computational resources available. The ideas presented in this dissertation were validated with mathematical analyses, computer simulations, and actual robotic assemblies.