MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration

• URL: http://arxiv.org/abs/2502.18072v1
• Date: Tue, 25 Feb 2025 10:39:28 GMT
• Title: MRBTP: Efficient Multi-Robot Behavior Tree Planning and Collaboration
• Authors: Yishuai Cai, Xinglin Chen, Zhongxuan Cai, Yunxin Mao, Minglong Li, Wenjing Yang, Ji Wang,
• Abstract summary: Multi-robot task planning and collaboration are critical challenges in robotics.<n>We propose the Multi-Robot Behavior Tree Planning (MRBTP) algorithm, with theoretical guarantees of both soundness and completeness.<n>We then propose an optional plugin for MRBTP when Large Language Models (LLMs) are available to reason goal-related actions for each robot.
• Score: 6.239895985962529
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• REMAC: Self-Reflective and Self-Evolving Multi-Agent Collaboration for Long-Horizon Robot Manipulation [57.628771707989166]
  We propose an adaptive multi-agent planning framework, termed REMAC, that enables efficient, scene-agnostic multi-robot long-horizon task planning and execution. ReMAC incorporates two key modules: a self-reflection module performing pre-conditions and post-condition checks in the loop to evaluate progress and refine plans, and a self-evolvement module dynamically adapting plans based on scene-specific reasoning.
  arXiv  Detail & Related papers  (2025-03-28T03:51:40Z)
• Together We Rise: Optimizing Real-Time Multi-Robot Task Allocation using Coordinated Heterogeneous Plays [5.206057210246862]
  This paper addresses the real-time multi-robot task allocation problem in dynamic warehouse environments. We introduce MRTAgent, a dual-agent Reinforcement Learning framework inspired by self-play. For safe navigation, a modified linear quadratic controller (LQR) approach is employed.
  arXiv  Detail & Related papers  (2025-02-22T04:59:27Z)
• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce kinematically feasible trajectories.<n>The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• COHERENT: Collaboration of Heterogeneous Multi-Robot System with Large Language Models [49.24666980374751]
  COHERENT is a novel LLM-based task planning framework for collaboration of heterogeneous multi-robot systems. A Proposal-Execution-Feedback-Adjustment mechanism is designed to decompose and assign actions for individual robots. The experimental results show that our work surpasses the previous methods by a large margin in terms of success rate and execution efficiency.
  arXiv  Detail & Related papers  (2024-09-23T15:53:41Z)
• A Meta-Engine Framework for Interleaved Task and Motion Planning using Topological Refinements [51.54559117314768]
  Task And Motion Planning (TAMP) is the problem of finding a solution to an automated planning problem. We propose a general and open-source framework for modeling and benchmarking TAMP problems. We introduce an innovative meta-technique to solve TAMP problems involving moving agents and multiple task-state-dependent obstacles.
  arXiv  Detail & Related papers  (2024-08-11T14:57:57Z)
• HBTP: Heuristic Behavior Tree Planning with Large Language Model Reasoning [6.2560501421348]
  Heuristic Behavior Tree Planning (HBTP) is a reliable and efficient framework for BT generation. This paper introduces the BT expansion process, along with two variants designed for optimal planning and satising planning. Experiments demonstrate the theoretical bounds of HBTP, and results from four datasets confirm its practical effectiveness in everyday service robot applications.
  arXiv  Detail & Related papers  (2024-06-03T03:38:56Z)
• Integrating Intent Understanding and Optimal Behavior Planning for Behavior Tree Generation from Human Instructions [5.31484618181979]
  Behavior Tree (BT) is an appropriate control architecture for robots executing tasks following human instructions. This paper proposes a two-stage framework for BT generation, which first employs large language models to interpret goals from high-level instructions. We represent goals as well-formed formulas in first-order logic, effectively bridging intent understanding and optimal behavior planning.
  arXiv  Detail & Related papers  (2024-05-13T05:23:48Z)
• EmbodiedGPT: Vision-Language Pre-Training via Embodied Chain of Thought [95.37585041654535]
  Embodied AI is capable of planning and executing action sequences for robots to accomplish long-horizon tasks in physical environments. In this work, we introduce EmbodiedGPT, an end-to-end multi-modal foundation model for embodied AI. Experiments show the effectiveness of EmbodiedGPT on embodied tasks, including embodied planning, embodied control, visual captioning, and visual question answering.
  arXiv  Detail & Related papers  (2023-05-24T11:04:30Z)
• Learning to Coordinate for a Worker-Station Multi-robot System in Planar Coverage Tasks [16.323122275188354]
  We focus on the multi-robot coverage path planning problem in large-scale planar areas with random dynamic interferers. We introduce a worker-station MRS consisting of multiple workers with limited resources for actual work, and one station with enough resources for resource replenishment. We propose an end-to-end decentralized online planning method, which simultaneously solves coverage planning for workers and rendezvous planning for station.
  arXiv  Detail & Related papers  (2022-08-05T05:36:42Z)
• Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints [52.58352707495122]
  We present a multi-robot allocation algorithm that decouples the key computational challenges of sequential decision-making under uncertainty and multi-agent coordination. We validate our results over a wide range of simulations on two distinct domains: multi-arm conveyor belt pick-and-place and multi-drone delivery dispatch in a city.
  arXiv  Detail & Related papers  (2020-05-27T01:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.