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All Journal IAES International Journal of Robotics and Automation (IJRA)
Talasila Ram Kumar
Malla Reddy Engineering College for Women
Automotive Engineering Electrical & Electronics Engineering

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Integrating large language models for context-aware decision making in autonomous mobile robots Vishnu Kumar Mishra; Megha Mishra; Talasila Ram Kumar; Yenna Geetha Reddy; Gundla Rajesh; Battula Phijik; Bandla Srinivasa Rao
IAES International Journal of Robotics and Automation (IJRA) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijra.v15i3.pp607-620

Abstract

The dynamic evolution of industrial automation has created an imperative need to transition from inflexible, rule-based systems to flexible, intelligent agents that can facilitate human-robot collaboration. The purpose of this research was to integrate large language models (LLMs) with autonomous mobile robots (AMRs) to improve context-aware decision-making. The inflexibility of traditional systems has often hindered performance in dynamic environments, as systems often rely on predefined algorithms and sensor configurations. To improve this, a modular framework was created, consisting of a central processing unit and an LLM API to interpret natural language and process environmental information. Quantitative results have been clearly specified in the abstract, which states that the success rate in resolving navigation exceptions by the proposed framework was 89% with a 5% localization error rate. Moreover, substantial savings were observed in token usage and computational resources. This study provided an imperative framework for smarter industrial automation, filling the gap between mechanical precision and artificial intelligence. The practical experimentation of an AMR model gives an outcome of a successful navigation exception resolution rate of 89% by means of the proposed framework, with an error rate of 5% localized to each exception. Additionally, significant reductions in the number of tokens used and the time taken to process tokens provide a scalable means for developing contextually-based, robust, autonomous mobile platforms’ decisions.
Co-Authors Bandla Srinivasa Rao Battula Phijik Gundla Rajesh Megha Mishra Vishnu Kumar Mishra Yenna Geetha Reddy