LLMBot: Multi-Agent Robotic Systems for Adaptive Task Execution

Task planning for autonomous systems often requires significant computational and financial resources, limiting their accessibility and scalability. In this paper, we have introduced a framework that integrates Large Language Models (LLMs) with autonomous systems for natural language interaction and task execution. The proposed method combines the LLMs for high-level planning with a simulated 3D environment in Unreal Engine and behaviour trees for robust low-level execution of robotic actions. We employ prompt engineering, multi-modal input, and parameter optimization techniques to enhance LLM performance and reduce computational overhead. A comprehensive test suite evaluates success rates, spatial distributions, and cost-effectiveness across various scenarios. The simulation results demonstrate that our approach outperforms traditional planning systems in terms of computational efficiency and cost-effectiveness while maintaining comparable task success rates.