LLM-Enhanced Control of a Mobile Robotic Platform for Smart Industry

The emergence of highly complex generative AI and large language models represents both a significant challenge and an opportunity for multiple engineering domains. Under the Industry 4.0 paradigm, various connected automation and industrial engineering applications can leverage the inference and generative design capabilities of these models to improve control algorithms and systems. In particular, widespread deployment of mobile robotic platforms in modern industry, enhanced with LLM capabilities, can provide a substantial increase in the efficiency and cost-effectiveness of such solutions. In this study, we investigate the suitability of current-generation LLM systems for industrial mobile robot control. We present a systematic, end-to-end methodology for benchmarking four GenAI/LLMs, SmolLM2, Llama 3.2, Gemma3, and Gemma3-qat, for a typical mobile robot platform configuration. The approach is two-staged, based on both assessing the specific domain knowledge of the models in an industrial context and their integration with a robotic simulation environment based on ROS2. Reported results focus on quantitative assessment of multiple metrics (quality, coverage, speed, and reliability) and their integration in aggregated scoring mechanisms, which can help developers select and adapt the best model for a particular application, together with custom software implementation.