Automated Data Analysis with Large Language Models for Warehouse Robotics Applications

Abstract

The research develops two automated data analysis frameworks, ARMADA and FACTS, to tackle essential operational problems in warehouse robotics systems. Warehouse robotics environments encounter ongoing problems with maintenance optimization and technical documentation processing that negatively affect operational efficiency.

The ARMADA(Anomaly Recognition for Maintenance and Diagnostic Applications) system implements Context-Augmented Anomaly Detection through a method that unites statistical modeling with operational context awareness. The analysis of operational states and temporal sensor data patterns by ARMADA resulted in a 43.5\% improvement in precision and a 13.7\% decrease in false positive occurrences. The system reduces operational costs substantially because it minimizes unneeded maintenance stoppages while preserving computational efficiency for systems with limited \# resources.

The FACTS (Factual Assessment and Content Trustworthiness System) uses layout-aware processing and hierarchical verification to tackle document understanding challenges. Through its multi-stage pipeline, which combined computer vision with language model reasoning when processing complex technical documentation, FACTS achieved a 37.2\% better information extraction accuracy and reduced verification errors by 29.8\%.

The frameworks underwent complete empirical testing through both experimental simulations and operational field implementations. The mathematical frameworks establish flexible methods to merge context-sensitive multimodal processing with LLM functionality for industrial applications, showing how AI can improve warehouse operations through smarter automated systems.