打造可插拔的多机器人任务分配仿真平台，加速算法测试与对比。
SPACE: A Python-based Simulator for Evaluating Decentralized Multi-Robot Task Allocation Algorithms
- 基于Python构建，支持插件式算法开发与图形化行为树配置。
- 在动态任务场景下验证CBBA与GRAPE算法，实现标准化性能对比。
- 适合研究分布式多机器人系统、需高效仿真评估的团队使用。
群体机器人研究关注多个机器人协同完成集体目标，其中去中心化决策是核心。该过程涉及机器人自主做出局部决策并进行通信，进而影响整体涌现行为。在真实环境中对数百个以上机器人测试此类算法通常不切实际，亟需高效的仿真工具。本文提出SPACE（Swarm Planning and Control Evaluation），一个基于Python的仿真器，旨在支持去中心化多机器人任务分配（MRTA）算法的研究、评估与比较。SPACE通过允许用户以Python插件形式实现决策算法、通过直观图形界面构建代理行为树，并内置支持机器人间通信与局部任务感知，简化了核心算法开发流程。为验证其实用性，我们在模拟器中实现了CBBA与GRAPE算法，在不同指标下进行评估，尤其在动态引入任务的场景中表现良好。结果表明，SPACE能有效支持对MRTA算法的严谨且标准化的比较，助力该领域未来研究。
原文摘要 · Abstract (English)
Swarm robotics explores the coordination of multiple robots to achieve collective goals, with collective decision-making being a central focus. This process involves decentralized robots autonomously making local decisions and communicating them, which influences the overall emergent behavior. Testing such decentralized algorithms in real-world scenarios with hundreds or more robots is often impractical, underscoring the need for effective simulation tools. We propose SPACE (Swarm Planning and Control Evaluation), a Python-based simulator designed to support the research, evaluation, and comparison of decentralized Multi-Robot Task Allocation (MRTA) algorithms. SPACE streamlines core algorithmic development by allowing users to implement decision-making algorithms as Python plug-ins, easily construct agent behavior trees via an intuitive GUI, and leverage built-in support for inter-agent communication and local task awareness. To demonstrate its practical utility, we implement and evaluate CBBA and GRAPE within the simulator, comparing their performance across different metrics, particularly in scenarios with dynamically introduced tasks. This evaluation shows the usefulness of SPACE in conducting rigorous and standardized comparisons of MRTA algorithms, helping to support future research in the field.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。