Applying Large Language Model to a Control System for Multi-Robot Task Assignment
Wen Zhao, Liqiao Li, Hanwen Zhan, Y. F. Wang, Yiqi Fu
Abstract
The emergence of large language models (LLMs), such as GPT (Generative Pre-trained Transformer), has had a profound impact and brought about significant changes across various sectors of human society. Integrating GPT-3.5 into a multi-robot control system, termed MultiBotGPT (Multi-Robot Control System with GPT), represents a notable application. This system utilizes layered architecture and modular design to translate natural language commands into executable tasks for UAVs (Unmanned Aerial Vehicles) and UGVs (Unmanned Ground Vehicles), enhancing capabilities in tasks such as target search and navigation. Comparative experiments with BERT (Bidirectional Encoder Representations from Transformers) in the natural language-processing component show that MultiBotGPT with GPT-3.5 achieves superior task success rates (94.4% and 55.0%) across 50 experiments, outperforming BERT significantly. In order to test the auxiliary role of the MultiBotGPT-controlled robot on a human operator, we invited 30 volunteers to participate in our comparative experiments. Three separate experiments were performed, Participant Control (Manual Control only), Mix Control (Mix Manual Contr and MultiBotGPT Control), and MultiBotGPT Control (MultiBotGPT Control only). The performance of MultiBotGPT is recognized by the human operators and it can reduce the mental and physical consumption of the human operators through the scoring of the participants’ questionnaires.