Natural Language-Based Automatic Programming for Industrial Robots

Cyber-Physical System (CPS), which is a part of Industry 4.0, suggests that the physical systems such as robots can be controlled by automation systems to minimize human workload. With the rise of automatic programming systems, programmers are no longer required to have a thorough understanding of the code. As a result of the interaction between the robot and the human, automatic programming systems generate a robot program. Many processing and recognition technologies for human-computer interaction interface are required to make the robot realize a more natural interaction. It is necessary to consider disruptive technologies in order to provide innovation and enable us to change the way we program. In this paper, we proposed approach to do automatic programming for industrial robots with natural language. To begin, we use a multi-attention mechanism to measure the matching probability of natural language instructions to objects in the environment. Then, using a modular programming method, we generate code for robots and combine the prediction results. We extend the existing dataset for evaluation to make it more suitable for describing the actual manufacturing environment, taking into account position, attribute, and constraints. The experimental results show that the model in this paper has a 20% higher recognition rate than other existing methods for accurately locating the object position, and the similarity between code written by experienced engineers and code generated by our more reached 80%.