LLM-CBT: LLM-Driven Closed-Loop Behavior Tree Planning for Heterogeneous UAV-UGV Swarm Collaboration

The heterogeneous cluster system holds significant application potential in scenarios such as collaborative logistics, disaster response operations, and precision agriculture, but achieving effective task planning for its subsystems remains a challenging issue due to specialized robotic hardware and distinct action spaces. To this end, an innovative framework called LLM-driven Closed-Loop Behavior Tree (LLM-CBT) is proposed. LLMs and behavior trees (BTs) are integrated for task planning in heterogeneous unmanned clusters, including Unmanned Aerial Vehicles (UAVs) and Unmanned Ground Vehicles (UGVs). Particularly, a novel mechanism, Generation-Refinement-Execution-Feedback (GREF), is introduced, in which an initial behavior tree is generated by LLM and iteratively refined. The refined behavior tree is then executed, and adjustments are made based on the execution results, forming a closed-loop process that ultimately achieves the task objectives. In this way, the executability of BTs is improved, and the robustness of task execution in dynamic environments is enhanced. Experiments were conducted across three scenarios with varying task complexity. The results show that the GREF closed-loop mechanism is essential for the effective operation of heterogeneous unmanned clusters.