面向狭窄工业环境中大型异构车辆的车流管理系统
A traffic management system for large and heterogeneous vehicles in narrow industrial environments
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中文总结 AI 辅助
本文提出基于L-MAPF和滚动时域CBS的AGV车流管理系统，适用于狭窄双向走廊的异构车辆环境，通过自适应冲突消解与死锁检测，吞吐量较传统方法提升最高11%。
中文摘要 AI 辅助
在物流4.0背景下，高密度工业环境中自动导引车（AGV）的协调是一个关键挑战，因为传统的车流管理方法往往因基于协商的优先级分配而导致效率低下。为克服上述局限，本文提出了一种创新的AGV车流管理系统，该系统基于在非均匀有理B样条（NURBS）曲线生成的道路图上运行的生命周期多智能体路径规划（L-MAPF）算法。该方法保证了局部最优协调，并确保大型异构AGV的安全运行。在此基础上，所提出的框架在滚动时域冲突消解策略中集成了改进版本的有限时域冲突搜索（CBS）技术，利用为每个智能体扩展的时间视野，以有效消解由拓扑地图识别的走廊中的冲突。与最先进的AGV车队车流管理方法相比，所提出的解决方案针对真实世界、非标准化（即非网格状）的工业环境设计，其特征是狭窄的双向走廊和高交通密度，不同尺寸和能力的AGV同时运行。主要贡献包括具有自适应时间视野调节的随时冲突消解策略、用于与真实AGV安全且符合标准交互的执行层，以及先进的死锁检测与消解机制。在真实工业环境中获得的实验结果表明，与传统的基于规则的车流管理系统、一种最先进的工业方法和一种基于优先级的L-MAPF变体相比，吞吐量更高，提升幅度最高达11%，同时保持连续运行并提高效率。
英文摘要
The coordination of Automated Guided Vehicles (AGVs) in high-density industrial environments represents a critical challenge within Logistics 4.0, as traditional traffic management methods often lead to inefficiencies caused by negotiation-based priority assignment. To overcome the resulting limitations, this paper presents an innovative AGV traffic management system based on a Lifelong Multi-Agent Path Finding (L-MAPF) algorithm operating on roadmaps generated with Non-Uniform Rational B-Splines (NURBS) curves. The approach guarantees locally optimal coordination and ensures safe operation of large and heterogeneous AGVs. Building on this concept, the proposed framework integrates a modified version of the Bounded Horizon Conflict Based Search (CBS) technique within a Rolling Horizon Conflict Resolution strategy, utilizing an extended time horizon for each agent to enable effective conflict resolution in corridors identified by a topological map. In contrast to state-of-the-art methods for AGV fleet traffic management, the proposed solution is designed for real-world, non-standardized (i.e., non-grid-like) industrial settings characterized by narrow bidirectional corridors and high-traffic density, where AGVs of various sizes and capabilities operate simultaneously. Key contributions include an anytime conflict resolution strategy with adaptive time horizon regulation, an execution layer for safe and standard-compliant interaction with real AGVs, and an advanced mechanism for deadlock detection and resolution. Experimental results obtained in realistic industrial environments demonstrate higher throughput, with improvements of up to 11% over a conventional rule-based traffic management system, a state-of-the-art industrial method, and a priority-based L-MAPF variant, while maintaining continuous operation and improved efficiency.
发表机构
- University of Modena and Reggio Emilia（摩德纳和雷焦艾米利亚大学）
- Gruppo TecnoFerrari S.p.a.（泰克诺法拉利集团股份公司）
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