Autonomous Subtask Generation for Indoor Search and Rescue Mission via Large-Language-Model and Behavior-Tree Integration

Abstract

The ability of autonomous subtask generation is important for robots to effectively cope with unforeseen situations during indoor search and rescue missions. While prior work mainly focused on improving individual low-level skills of the rescue robot, this paper proposes AutoExpand: a high-level framework that takes advantage of the extensive knowledge and reasoning abilities inherent in large language models (LLM) to understand human instructions and environmental situation. Through tight coupling LLM with behavior tree, our method enables the robot to autonomously generate reactive context-aware operational subtasks on-site without human intervention or additional training. A series of real-world experiments demonstrate that AutoExpand can effectively generate appropriate tasks for search and rescue missions, leading to a search scope increased by 34.45% when compared with traditional methods. The sample code is available at https://github.com/nubot-nudt/AutoExpand.