Success/Failure Identification of Skill Movement by Neural Network Using Force Information

Currently, in the field of FA(Factory Automation), automation has been accomplished for only tasks with high reproducibility. Meanwhile, there remain a range of areas where tasks cannot reliably be performed due to their complexity or larger environmental variation. In these cases, the reliability of the task can be improved by recognizing the task failure through success/failure identifications and executing the task again when it fails. However, the success/failure identification using conventional machine learning methods has not been discussed for determining the success or failure for unlearned objects. Thus, this paper examined assembly tasks and demonstrated that the success or failure for an unlearned object can be identified by taking advantage of generalized nature of the neural network using force information. The results of making success/failure identifications using information on force, image, and position were compared and the advantage of force information in tasks was demonstrated.

I. Introduction

In recent years, assembly tasks at factories are one of the areas where automation by robot is most prosperous [1] [2]. Currently, a wide range of assembly tasks have been automated, e.g., mounting of rotary equipment on turbo chargers of the car, and assembling of main wings of the aircraft. However, tasks that have been put into practice at this moment are limited to those that are highly reproducible and are constituted by a series of simple actions. At factories, there are many tasks with lower reproducibility due to their complexity and stringent success conditions. Therefore, it is difficult to automate complex tasks at factories; such complex tasks are now being carried out by human hands.