多机器人自主组队探索，用大模型决定下一目标
LLM-Guided Decentralized Exploration with Self-Organizing Robot Teams
- 机器人自主组队，无需中心控制
- 利用大语言模型规划探索目标，提升效率
- 适合大规模分布式机器人任务
当单个机器人感知能力有限或容错性不足时，需通过多机器人组队来扩展整体观测范围并提高可靠性。传统上，群体行为常由中心控制器管理；但从鲁棒性和灵活性角度，更优方案是使群体在无中心控制情况下仍能自主运行。此外，在多团队探索场景中，各团队探索目标的确定对效率至关重要。本文提出一种结合(1)自组织算法实现多团队的自主动态组队，以及(2)各团队自主决策下一探索目标的策略。尤其针对后者，探索了一种基于大语言模型（LLMs）的新方法，相较传统的基于前缘的方法和深度强化学习方法更具潜力。该方法在涉及数十至数百个机器人的仿真中得到验证。
原文摘要 · Abstract (English)
When individual robots have limited sensing capabilities or insufficient fault tolerance, it becomes necessary for multiple robots to form teams during exploration, thereby increasing the collective observation range and reliability. Traditionally, swarm formation has often been managed by a central controller; however, from the perspectives of robustness and flexibility, it is preferable for the swarm to operate autonomously even in the absence of centralized control. In addition, the determination of exploration targets for each team is crucial for efficient exploration in such multi-team exploration scenarios. This study therefore proposes an exploration method that combines (1) an algorithm for self-organization, enabling the autonomous and dynamic formation of multiple teams, and (2) an algorithm that allows each team to autonomously determine its next exploration target (destination). In particular, for (2), this study explores a novel strategy based on large language models (LLMs), while classical frontier-based methods and deep reinforcement learning approaches have been widely studied. The effectiveness of the proposed method was validated through simulations involving tens to hundreds of robots.
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