Estimating pose statistics for robotic part feeders

In automated assembly lines, part feeders often impose a bottleneck that restricts throughput. To facilitate the design of parts and assembly lines, we’d like to estimate feedrates based on CAD models of parts. A previous paper [8] described how to predict throughput for a vision-based robotic part feeder given the distribution of part poses when parts are randomly dropped on a conveyor belt. Estimating this distribution is also useful for the design of traditional feeders such as vibratory bowls. In this paper, we describe three algorithms for estimating pose distributions. We review the quasi-static estimate reported in [21] and introduce a refinement that takes into account some measure of dynamic stability. The perturbed quasi-static estimate can be computed very rapidly and is more accurate than the quasi-static. Still more accurate are estimates based on Monte Carlo simulation using Impulse [12; 13], although the latter comes at the penalty of increased computation time. We compare estimates from all three algorithms with physical experiments. We believe this is the first paper to systematically compare estimators with experiments using real industrial parts.