In this article, we develop a novel strategy for automatic error classification and recovery in robotic assembly tasks. The strategy does not require error diagnosis. It allows for effective reduction of an undetermined number of error states to 4, without the need for further operator updates of error space. The strategy integrates existing methods for computer vision, active vision and active manipulation. Our solution is implemented in a generic software framework, which is independent from software and hardware for implementing error detection and allows for application in other assembly types and components. The value of our strategy was experimentally validated on a simple case, where we inserted a battery into a cell phone. The experiment was performed on 1500 assembly attempts and included 500 detected errors. The whole experiment ran for 42 hours, with no need for operator assistance or supervision. The resulting classification rate is 99.6% and the resulting recovery rate is 98.8%. The 6 unrecovered errors were successfully resolved in a successive assembly attempt.

在本文中，我们开发了一种新颖的策略，用于机器人装配任务中的自动错误分类和恢复。该策略不需要错误诊断。它允许将不确定的错误状态有效地减少到4，而无需操作员进一步更新错误空间。该策略整合了计算机视觉，主动视觉和主动操纵的现有方法。我们的解决方案在通用软件框架中实现，该软件框架独立于软件和硬件以实现错误检测，并允许应用于其他装配类型和组件。我们在一个简单的案例中通过实验验证了我们策略的价值，在该案例中，我们将电池插入了手机。该实验是在1500次组装尝试中进行的，其中包括500次检测到的错误。整个实验进行了42小时，无需操作员的帮助或监督。最终的分类率为99.6％，最终的回收率为98.8％。在连续的组装尝试中，成功解决了6个未恢复的错误。