MRBTP：高效的多机器人行为树规划与协作
MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration
AI总结：
提出MRBTP算法，通过跨树扩展和意图共享实现多机器人行为树规划，并利用LLM插件生成任务子树，提升规划速度与协作效率，在仓库和服务场景验证了鲁棒性。
摘要：
多机器人任务规划与协作是机器人领域的关键挑战。尽管行为树（BTs）已成为一种流行的控制架构，并且对于单个机器人是可规划的，但由于协调多样化动作空间的复杂性，开发有效的多机器人BT规划算法仍然具有挑战性。我们提出了多机器人行为树规划（MRBTP）算法，并提供了可靠性和完备性的理论保证。MRBTP具有跨树扩展特性，以协调不同BT之间的异构动作，从而实现团队目标。对于同质动作，我们在BT之间保留备份结构以确保鲁棒性，并通过意图共享防止冗余执行。虽然MRBTP能够为同质和异构机器人团队生成BT，但其效率可以进一步提高。随后，当大型语言模型（LLMs）可用时，我们为MRBTP提出了一个可选插件，用于推理每个机器人与目标相关的动作。这些相关动作可以预先规划以形成长视界子树，显著提升MRBTP的规划速度和协作效率。我们在仓库管理和日常服务场景中评估了我们的算法。结果表明，MRBTP在不同设置下具有鲁棒性和执行效率，并且预训练的LLM能够为MRBTP生成有效的任务特定子树。
英文摘要：
Multi-robot task planning and collaboration are critical challenges in robotics. While Behavior Trees (BTs) have been established as a popular control architecture and are plannable for a single robot, the development of effective multi-robot BT planning algorithms remains challenging due to the complexity of coordinating diverse action spaces. We propose the Multi-Robot Behavior Tree Planning (MRBTP) algorithm, with theoretical guarantees of both soundness and completeness. MRBTP features cross-tree expansion to coordinate heterogeneous actions across different BTs to achieve the team's goal. For homogeneous actions, we retain backup structures among BTs to ensure robustness and prevent redundant execution through intention sharing. While MRBTP is capable of generating BTs for both homogeneous and heterogeneous robot teams, its efficiency can be further improved. We then propose an optional plugin for MRBTP when Large Language Models (LLMs) are available to reason goal-related actions for each robot. These relevant actions can be pre-planned to form long-horizon subtrees, significantly enhancing the planning speed and collaboration efficiency of MRBTP. We evaluate our algorithm in warehouse management and everyday service scenarios. Results demonstrate MRBTP's robustness and execution efficiency under varying settings, as well as the ability of the pre-trained LLM to generate effective task-specific subtrees for MRBTP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
