Abstract
For smart manufacturing, an automated robotic assembly system built upon an autoprogramming environment is necessary to reduce setup time and cost for robots that are engaged in frequent task reassignment. This article presents an approach to the autoprogramming of robotic assembly tasks with minimal human assistance. The approach integrates “robotic learning of assembly tasks from observation” and “robotic embodiment of learned assembly tasks in the form of skills.” In the former, robots observe human assembly operations to learn a sequence of assembly tasks, which is formalized into a human assembly script. The latter transforms the human assembly script into a robot assembly script in which a sequence of robot-executable assembly tasks are defined based on action planning supported by workspace modeling and simulated retargeting. The assembly tasks, in the form of the robot assembly script, are then implemented via pretrained robot skills. These skills aim to enable robots to execute difficult tasks that involve inherent uncertainties and variations. We validate the proposed approach by building a prototype of the automated robotic assembly system for a power breaker and an electronic set-top box. The results verify that the proposed automated robotic assembly system is not only feasible but also viable, as it is associated with a dramatic reduction in the human effort required for automating robotic assembly.
Highlights
As the manufacturing industry trends toward smaller batch production, production lines rely more and more on flexible or smart work cells
SYSTEMOVERVIEW The proposed approach to building an autoprogramming environment for robots engaged in a smart assembly cell consists of three parts: 1) robotic learning of assembly tasks through observation of the human assembly process, which results in a high-level description of learned assembly tasks as a formal human assembly script; 2) automatic transformation of the human assembly script into a robot assembly script, in which a sequence of robotexecutable tasks is specified based on robot action planning supported by workspace modeling and simulated retargeting, referred to here as “robotic embodiment;” and 3) automatic execution of the sequence of robotexecutable tasks defined in the robot assembly script with pretrained robot skills
In this article, we presented an automated robotic assembly system built upon an autoprogramming environment that can reduce the setup time and cost for reconfiguring and reprogramming robots when it is frequently necessary to reassign robot tasks, as in smart manufacturing plants
Summary
As the manufacturing industry trends toward smaller batch production, production lines rely more and more on flexible or smart work cells. Many cooperative robots have recently been developed and released to support the flexibility of work cells, there is an immediate need for further improvement in the convenience of teaching the robots to accomplish a task in a more direct and intuitive manner. This means that, in order to achieve simultaneous improvements in productivity and flexibility based on robotized smart work cells, it is critical to increase the level of automation in programming and robotic execution. The development of the proposed AI-based automated assembly system is supported by the recent emergence of two significant technological advancements: 1) deep learning (DL)-based real-time segmentation, modeling, and understanding of static and dynamic scenes and 2) an increase in the power of reinforcement learning (RL) for implementing robot skills
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