AssemblyGrid v1：具有临时联盟、局部信息和几何约束的多机器人生产基准
AssemblyGrid v1: A Benchmark for Multi-Robot Production with Temporary Coalitions, Local Information, and Geometric Constraints
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中文总结 AI 辅助
AssemblyGrid v1是一个多机器人生产基准，结合流程进展、局部信息、临时联盟和几何约束，通过三类工作负载和MARL实验验证了分散策略的有效性。
中文摘要 AI 辅助
灵活机器人生产需要对流程进展、物料路由、资源分配、临时合作和并行执行进行联合决策，因为每个决策都可能影响其他决策的可行性。在分散控制下，这一挑战更为严峻，每个机器人仅基于有限的局部信息行动，而系统进展取决于集体决策、共享资源、物料状态和工作空间兼容性。这些特性与部分可观测和资源竞争下的合作式多智能体决策高度吻合。本文介绍了AssemblyGrid v1，一个可复现的多机器人重复生产基准，它在单一任务级公式中结合了显式流程进展、分散观测、物料传递、临时多机器人联盟、生产性并发以及几何相关的可行性。该基准包括Flow、Coalition和Concurrency三类工作负载族，每类包含三个场景级别。任务成功和评估指标独立于学习奖励和求解方法定义，使基于学习和非学习方法能够解决相同的生产问题。AssemblyGrid v1通过可执行一致性检查、机制研究和算法实验进行评估，使用了特权集中式参考、结构化分散控制器以及包括IPPO、MAPPO和QMIX在内的MARL方法。结果表明在集中和分散控制下均能实现生产性执行。MARL实验进一步表明，分散策略能够从局部观测和行动中学习有效的生产行为，支持AssemblyGrid作为研究灵活机器人生产中合作决策的受控基准。
英文摘要
Flexible robotic production requires joint decisions on process progression, material routing, resource assignment, temporary cooperation, and simultaneous execution, since each decision can affect the feasibility of the others. The challenge is greater under decentralized control, where each robot acts from bounded local information while system progress depends on collective decisions, shared resources, material state, and workspace compatibility. These properties closely match cooperative multi-agent decision making under partial observability and resource contention. This paper introduces AssemblyGrid v1, a reproducible benchmark for repeated multi-robot production that combines explicit process progression, decentralized observations, material transfer, temporary multi-robot coalitions, productive concurrency, and geometry-dependent feasibility within one task-level formulation. The benchmark includes Flow, Coalition, and Concurrency workload families, each with three scenario levels. Task success and evaluation measures are defined independently of learning reward and solution method, allowing learning-based and non-learning methods to address the same production problem. AssemblyGrid v1 is evaluated through executable conformance checks, mechanism studies, and algorithmic experiments using a privileged centralized reference, structured decentralized controllers, and MARL methods including IPPO, MAPPO, and QMIX. Results demonstrate productive execution under centralized and decentralized control. The MARL experiments further show that decentralized policies can learn effective production behavior from local observations and actions, supporting AssemblyGrid as a controlled benchmark for studying cooperative decision making in flexible robotic production.
发表机构
- Hochschule für Technik und Wirtschaft Dresden – University of Applied Sciences（德累斯顿应用科学大学）
机构由 AI 辅助整理，请以论文原文为准。
