A relative-change-based hierarchical taxonomy for cantilever-snap assembly verification

Snap assembly automation remains a challenging task. While progress is being made in localization of parts, force controllers, and control strategies, little work has been done to help the robot reason about its current state, such that if necessary, the robot can assume corrective actions to accomplish the task. Error prone situations caused by the unexpected motion of parts, localization errors, jamming or wedging, cannot be solved by force controllers alone. For this reason we propose a snap assemblies verification system for cantilever-snap fasteners. The verification works in concert with a control strategy that makes use of constraint designs embedded in the snap parts' physical design. The constrained assembly motion generates similar sensory-signal patterns across trials that facilitates force signal discrimination into higher level abstractions of intuitive behavior. This work's contribution is the design of a hierarchical taxonomy for cantilever-snap verification based on increasingly abstract layers that encode relative-change in the task's force signatures. A five-layered taxonomy is built on the concept that relative-change patterns can be classified through a small category set and aided by contextual information. The verification system yielded human apropos intuitive categorizations of task behavior for every state and effectively determined the assembly result. This simple yet effective approach will be expanded to perform probabilistic online system verification to aid in fault tolerance and the automation of cantilever-based snap assemblies.