让不同能力的机器人在通信不稳定时自主协作规划任务
Cooperative and Asynchronous Transformer-based Mission Planning for Heterogeneous Teams of Mobile Robots
- 用异步Transformer架构实现分布式决策，支持不同机器人的能力差异
- 在网格环境中表现优于传统方法，通信中断时仍稳定运行
- 适合实际部署中机器人数量和类型多变的复杂场景
异构移动机器人团队的协同任务规划面临通信受限与计算资源不足的挑战。为此，我们提出基于Transformer的异步协同任务规划框架CATMiP，采用多智能体强化学习（MARL）协调具有不同感知、运动和执行能力的智能体，在间歇性自组织通信下进行分布式决策。构建了基于类别的宏动作部分可观测马尔可夫决策过程（CMacDec-POMDP）以建模异构团队的异步决策。框架采用异步集中训练、分布式执行机制，依托提出的异步多智能体Transformer（AMAT）架构，使单一模型可泛化至更大环境，并适应不同团队规模与组成。在二维网格世界仿真中评估，相比基于规划的探索方法，CATMiP展现出更优的效率、可扩展性及对通信中断和输入噪声的鲁棒性，具备在真实异构移动机器人系统中应用的潜力。代码已开源。
原文摘要 · Abstract (English)
Cooperative mission planning for heterogeneous teams of mobile robots presents a unique set of challenges, particularly when operating under communication constraints and limited computational resources. To address these challenges, we propose the Cooperative and Asynchronous Transformer-based Mission Planning (CATMiP) framework, which leverages multi-agent reinforcement learning (MARL) to coordinate distributed decision making among agents with diverse sensing, motion, and actuation capabilities, operating under sporadic ad hoc communication. A Class-based Macro-Action Decentralized Partially Observable Markov Decision Process (CMacDec-POMDP) is also formulated to effectively model asynchronous decision-making for heterogeneous teams of agents. The framework utilizes an asynchronous centralized training and distributed execution scheme, enabled by the proposed Asynchronous Multi-Agent Transformer (AMAT) architecture. This design allows a single trained model to generalize to larger environments and accommodate varying team sizes and compositions. We evaluate CATMiP in a 2D grid-world simulation environment and compare its performance against planning-based exploration methods. Results demonstrate CATMiP's superior efficiency, scalability, and robustness to communication dropouts and input noise, highlighting its potential for real-world heterogeneous mobile robot systems. The code is available at https://github.com/mylad13/CATMiP
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