用大模型构建分层任务树，实现异构机器人群体高效协同规划
Generalized Mission Planning for Heterogeneous Multi-Robot Teams via LLM-constructed Hierarchical Trees
- 大模型调用专用工具生成分层任务结构
- 按机器人能力与约束优化个体调度方案
- 支持多种复杂任务，灵活可扩展
我们提出一种针对异构多机器人团队的新型任务规划策略，充分考虑每台机器人的特定约束与能力。通过构建分层树结构，将复杂任务系统性地分解为可管理的子任务。开发专用API与工具，供大型语言模型（LLMs）高效生成这些分层树。生成分层树后，进一步分解以制定各机器人的优化调度计划，确保满足其个体约束与能力。通过涵盖多种任务的详细案例验证了该框架的有效性，展示了其灵活性与可扩展性。
原文摘要 · Abstract (English)
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.
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