Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees

TL;DR

This paper introduces a hierarchical tree-based mission planning framework for heterogeneous multi-robot teams, leveraging LLMs to generate and decompose tasks into optimized schedules respecting individual robot constraints.

Contribution

It presents a novel LLM-driven hierarchical planning approach that systematically manages complex multi-robot missions with diverse capabilities.

Findings

Effective task decomposition demonstrated across various mission types

Framework ensures adherence to individual robot constraints

Shows scalability and flexibility in multi-robot mission planning

Abstract

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.