Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees
This addresses mission planning for heterogeneous multi-robot teams, but appears incremental as it combines existing hierarchical planning with LLMs.
The paper tackles mission planning for heterogeneous multi-robot teams by using LLMs to construct hierarchical trees that break down complex missions into sub-tasks, then optimizing schedules for each robot's constraints and capabilities, demonstrating effectiveness through examples showing flexibility and scalability.
We present a novel mission-planning strategy for heterogeneous multi-robot teams, taking into account the specific constraints and capabilities of each robot. Our approach employs hierarchical trees to systematically break down complex missions into manageable sub-tasks. We develop specialized APIs and tools, which are utilized by Large Language Models (LLMs) to efficiently construct these hierarchical trees. Once the hierarchical tree is generated, it is further decomposed to create optimized schedules for each robot, ensuring adherence to their individual constraints and capabilities. We demonstrate the effectiveness of our framework through detailed examples covering a wide range of missions, showcasing its flexibility and scalability.