MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration

TL;DR

This paper introduces MRBTP, a novel algorithm for multi-robot behavior tree planning that guarantees soundness and completeness, improves efficiency with LLM-assisted pre-planning, and demonstrates robustness in practical scenarios.

Contribution

The paper presents a new multi-robot behavior tree planning algorithm with theoretical guarantees and an LLM-based plugin to enhance planning speed and collaboration.

Findings

MRBTP achieves robust multi-robot coordination.

LLM-assisted pre-planning significantly speeds up planning.

Algorithm performs well in warehouse and service scenarios.

Abstract

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.