Contextualized early failure characterization of cantilever snap assemblies

Failure detection and correction is essential in robust systems. In robotics, failure detection has focused on traditional parts assembly, tool breakage, and threaded fastener assembly. However, not much work has focused on classifying failure into various sub-modes. This is an important step in order to provide accurate failure recovery. Our work implemented a contextualized failure characterization scheme for cantilever snap assemblies. A rule based approach was used through which assemblies whose trajectories deviated from the normal approach trajectory were identified in the beginning of the task. We not only identified failure but also the failure type that occurred. The method identified exemplars that characterized salient features for specific deviations from the initial approach trajectory in the assembly task. A contact-state map was generated through sampling the contact space during training. Contextualized statistical measures were used to classify trials during the testing phase. Our work classified failure deviations with 88% accuracy. According to the statistic measures used, varying success was experienced in correlating failure deviation modes. Each case was analyzed using gaussian statistics and one and two standard deviations. Cases with trajectory deviations in one direction had {75%, 92%} accuracy, cases with deviations in two directions had {61%, 94%} accuracy, and cases with deviations in three directions had {69%, 100%} accuracy. Our work provides further insights into the early failure characterization of complex geometrical parts which will serve to implement failure recovery techniques in the face of significant and unexpected errors.