Environment-Driven and LLM-Guided Multi-Robot Task Inference and Allocation Under Temporal Logic Specifications

In multi-robot systems, the successful execution of tasks typically depends on predefined instructions. However, existing approaches encounter substantial challenges in dynamic environments, particularly in autonomously reasoning, generating task instructions, and allocating tasks. These challenges are further exacerbated by the need to address complex temporal, spatial, and heterogeneous task constraints. To address these limitations, inspired by the success of the Large Language Models (LLMs) in natural language understanding and logical inference, this paper proposes an environment-driven and LLM-guided task inference and allocation framework with dual-system temporal logics. The framework consists of three key modules: the Environment Module, which employs environment LTL to continuous monitor and verify environmental resource constraints; the Inference Module, leveraging LLMs for autonomous generation and verification of robotic tasks in response to resource changes; and the Robot Module, which explores the feasible task allocation for the multi-robot system. When the resources in the environment do not satisfy the specification, the Inference Module is used to analyze and infer the feasible actions to be executed by the multi-robot system, so as to alleviate the environmental resource problem. Experimental results demonstrate the scalability, efficiency, and autonomy of our framework across varying task environments and robot configurations.