Particle filtering for localization in robotic assemblies with position uncertainty

摘要

This paper deals with the class of robotic assemblies where position uncertainty far exceeds assembly clearance, and visual assistance is not available to resolve the uncertainty. Under this scenario, we can implement a localization strategy that resolves the uncertainty using a pre-acquired map of all possible peg-hole contact configurations. Prior to assembly, the strategy explores the contact configuration space (C-space) by sequentially bringing the peg (under different configurations) into contact with the stationary (fixtured) hole and matching the contact configurations thus recorded with the map. The different peg configurations can be actively chosen to maximize uncertainty-reduction. However, with a sampled map of the contact C-space, discretization errors are introduced, and implementing deterministic matching (at the fine-grained level necessary for assembly) would soon become prohibitively expensive in terms of computation. Additionally, with global initial uncertainty, multiple solutions abound in our localization problem. In this paper, we introduce a particle filter implementation which can not only handle the discretization errors in map-matching, but also track multiple solutions simultaneously. The particle filter implementation was validated on computer simulations of round and square peg-in-hole assemblies, before testing it on corresponding actual robotic assemblies. The implementation was highly successful on both the assemblies, reducing the uncertainty by more than 95 % and making it easy for a previously-devised compliant strategy to achieve assembly. Results from the simulations and actual assemblies are reported. We also present a comparison of these results (using random localization: peg moves selected randomly) with preliminary results from assemblies using active localization.