柔性车间资源协同配送与AGV路径优化技术数据集

柔性车间资源协同配送与AGV路径优化技术数据集基于数字孪生技术构建高保真柔性车间仿真环境，涵盖车间布局、AGV参数及动态状态信息三大模块，为资源协同配送与路径优化研究提供多维数据支撑。通过强化学习驱动的多智能体系统，实时采集AGV位置、电池状态、碰撞频次等关键参数，并采用统一协议实现跨模块数据无缝传输。仿真实验模拟真实车间场景，包含6台AGV执行100项搬运任务的完整调度记录，数据采集覆盖路径规划、设备交互及环境动态变化特征。数据集采用结构化存储，符合工业数据标准，支持动态调度算法验证、数字孪生建模及多目标优化研究。柔性车间资源协同配送与AGV路径优化技术数据集为AGV调度系统提供了全面的车间环境、任务分配和路径规划支持，能够有效地进行动态调度与路径优化实验，支持柔性车间资源协同配送研究的进一步开展。Dataset for Collaborative Resource Distribution and AGV Path Optimization in Flexible Job Shops This dataset builds a high-fidelity flexible job shop simulation environment based on digital twin technology, covering three major modules: workshop layout, AGV parameters and dynamic status information, providing multi-dimensional data support for research on collaborative resource distribution and path optimization. A reinforcement learning-driven multi-agent system collects key parameters in real time, including AGV position, battery status, collision frequency and other critical metrics, and realizes seamless cross-module data transmission via a unified protocol. The simulation experiment replicates real workshop scenarios, containing complete scheduling records of 6 AGVs performing 100 handling tasks. Data collection covers characteristics such as path planning, equipment interaction and dynamic environmental changes. The dataset is stored in a structured format, complies with industrial data standards, and supports dynamic scheduling algorithm verification, digital twin modeling and multi-objective optimization research. This dataset offers comprehensive support for workshop environment, task allocation and path planning of AGV scheduling systems, enabling effective dynamic scheduling and path optimization experiments, and facilitating further research on collaborative resource distribution in flexible job shops.

搜集汇总

背景与挑战

背景概述

该数据集基于数字孪生技术构建高保真柔性车间仿真环境，涵盖车间布局、AGV参数及动态状态信息三大模块，为资源协同配送与AGV路径优化研究提供多维数据支撑。它通过强化学习驱动的多智能体系统实时采集AGV位置、电池状态等关键参数，并模拟了6台AGV执行100项搬运任务的完整调度记录，支持动态调度算法验证和数字孪生建模。