Localization for robotic assemblies using probing and particle filtering

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. Specifically, we focus on the assembly of a key in a lock, with uncertainty in the position of the lock in (x,y,z). Under this scenario, we implement a localization strategy that resolves the uncertainty using a pre-acquired map of key-lock contact configurations. Prior to assembly, the strategy explores the contact configuration space (C-space) by using the key to probe the stationary (fixtured) lock-surface at various different positions and matching the contact configurations thus recorded with the map. Thus, the strategy is progressively able to localize the lock-position in (x,y,z) and achieve assembly. 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. Here, we use 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 highly successful in localizing the lock-position, reducing the uncertainty by more than 95% and making it easy for a compliant strategy to achieve assembly. Results from 50 trials of the lock-key assembly are reported