Abstract
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.
Highlights
Companies must cope with short product lifecycles, which are caused by rapidly changing technologies and intense competition
Flexible robotic assembly is difficult to automate because the variations between different batches and the uncertainties and compliance introduced by flexible fixtures and tools, make the conventional position-based motion planning unreliable
In order to validate the performance of developed strategy for error classification and error recovery, an external camera system is installed
Summary
Companies must cope with short product lifecycles, which are caused by rapidly changing technologies and intense competition. When automating manufacturing processes companies can invest in dedicated, hard automation. These production lines must be reconfigured for a new product variant, which is both time consuming and expensive. In [3,4,5], the authors develop the real-time force controller to guide the robotic assembly. In [6] contact forces are used for minimizing assembly time by empirical self-tuning of parameters by the robot and using a learning algorithm. Automated flexible assembly based on force based strategies requires complex models for part interactions and/or they need to be refined over many runs with machine learning before they are applied for controlling the assembly process
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