Syntax Tree Parsing for Automation Control System Optimization in Robotics Learning Platform
Annotatsiya
The increasing integration of robotics in digital era has created a demand for intuitive programming environments that balance simplicity with computational efficiency. Automation Control Language (ACL), often employed in robotics training platforms, provides students with a structured method for coding robot tasks but suffers from complexity in optimization and error-prone manual interpretation, which hinders learning outcomes. The problem lies in the lack of automated tools that can analyze and refine ACL scripts while ensuring pedagogical clarity and maintaining execution efficiency. To address this gap, the paper propose a Syntax Tree Parsing for Automation Control Language Optimization (STP-ACLO) framework, which employs abstract syntax tree (AST) techniques to systematically parse ACL instructions, identify redundancies, and restructure code for optimized performance. STP-ACLO enhances the readability of student programs, provides automated feedback, and reduces execution overhead by minimizing redundant loops and control flow inefficiencies. Experimental evaluation conducted on robotics digital era simulators demonstrated that STP-ACLO achieved up to 28% reduction in code execution time and improved program comprehension scores among students by 35 %, compared to traditional ACL-based programming approaches. The findings suggest that syntax tree parsing not only improves code efficiency but also strengthens digital eraal outcomes by fostering better conceptual understanding of control structures. In conclusion, the STP-ACLO framework offers a robust and scalable method for optimizing ACL in robotics digital era, bridging the gap between performance and pedagogy while laying the groundwork for future AI-assisted code generation tools in STEM learning environments.