mirror of
https://github.com/dptech-corp/Uni-Lab-OS.git
synced 2026-02-04 05:15:10 +00:00
2a5ddd611d
Workbench example, adjust log level, and ci check (#220) * TestLatency Return Value Example & gitignore update * Adjust log level & Add workbench virtual example & Add not action decorator & Add check_mode & * Add CI Check Fix/workstation yb revision (#217) * Revert log change & update registry * Revert opcua client & move electrolyte node Workstation yb merge dev ready 260113 (#216) * feat(bioyond): 添加计算实验设计功能，支持化合物配比和滴定比例参数 * feat(bioyond): 添加测量小瓶功能，支持基本参数配置 * feat(bioyond): 添加测量小瓶配置，支持新设备参数 * feat(bioyond): 更新仓库布局和尺寸，支持竖向排列的测量小瓶和试剂存放堆栈 * feat(bioyond): 优化任务创建流程，确保无论成功与否都清理任务队列以避免重复累积 * feat(bioyond): 添加设置反应器温度功能，支持温度范围和异常处理 * feat(bioyond): 调整反应器位置配置，统一坐标格式 * feat(bioyond): 添加调度器启动功能，支持任务队列执行并处理异常 * feat(bioyond): 优化调度器启动功能，添加异常处理并更新相关配置 * feat(opcua): 增强节点ID解析兼容性和数据类型处理 改进节点ID解析逻辑以支持多种格式，包括字符串和数字标识符 添加数据类型转换处理，确保写入值时类型匹配 优化错误提示信息，便于调试节点连接问题 * feat(registry): 新增后处理站的设备配置文件 添加后处理站的YAML配置文件，包含动作映射、状态类型和设备描述 * 添加调度器启动功能，合并物料参数配置，优化物料参数处理逻辑 * 添加从 Bioyond 系统自动同步工作流序列的功能，并更新相关配置 * fix：兼容 BioyondReactionStation 中 workflow_sequence 被重写为 property * fix:同步工作流序列 * feat: remove commented workflow synchronization from `reaction_station.py`. * 添加时间约束功能及相关配置 * fix：自动更新物料缓存功能，添加物料时更新缓存并在删除时移除缓存项 * fix：在添加物料时处理字符串和字典返回值，确保正确更新缓存 * fix：更新奔曜错误处理报送为物料变更报送，调整日志记录和响应消息 * feat：添加实验报告简化功能，去除冗余信息并保留关键信息 * feat: 添加任务状态事件发布功能，监控并报告任务运行、超时、完成和错误状态 * fix: 修复添加物料时数据格式错误 * Refactor bioyond_dispensing_station and reaction_station_bioyond YAML configurations - Removed redundant action value mappings from bioyond_dispensing_station. - Updated goal properties in bioyond_dispensing_station to use enums for target_stack and other parameters. - Changed data types for end_point and start_point in reaction_station_bioyond to use string enums (Start, End). - Simplified descriptions and updated measurement units from μL to mL where applicable. - Removed unused commands from reaction_station_bioyond to streamline the configuration. * fix：Change the material unit from μL to mL * fix：refresh_material_cache * feat: 动态获取工作流步骤ID，优化工作流配置 * feat: 添加清空服务端所有非核心工作流功能 * fix：修复Bottle类的序列化和反序列化方法 * feat：增强材料缓存更新逻辑，支持处理返回数据中的详细信息 * Add debug log * feat(workstation): update bioyond config migration and coin cell material search logic - Migrate bioyond_cell config to JSON structure and remove global variable dependencies - Implement material search confirmation dialog auto-handling - Add documentation: 20260113_物料搜寻确认弹窗自动处理功能.md and 20260113_配置迁移修改总结.md * Refactor module paths for Bioyond devices in YAML configuration files - Updated the module path for BioyondDispensingStation in bioyond_dispensing_station.yaml to reflect the new directory structure. - Updated the module path for BioyondReactionStation and BioyondReactor in reaction_station_bioyond.yaml to align with the revised organization of the codebase. * fix: WareHouse 的不可哈希类型错误，优化父节点去重逻辑 * refactor: Move config from module to instance initialization * fix: 修正 reaction_station 目录名拼写错误 * feat: Integrate material search logic and cleanup deprecated files - Update coin_cell_assembly.py with material search dialog handling - Update YB_warehouses.py with latest warehouse configurations - Remove outdated documentation and test data files * Refactor: Use instance attributes for action names and workflow step IDs * refactor: Split tipbox storage into left and right warehouses * refactor: Merge tipbox storage left and right into single warehouse --------- Co-authored-by: ZiWei <131428629+ZiWei09@users.noreply.github.com> Co-authored-by: Andy6M <xieqiming1132@qq.com> fix: WareHouse 的不可哈希类型错误，优化父节点去重逻辑 fix parent_uuid fetch when bind_parent_id == node_name 物料更新也是用父节点进行报送 Add None conversion for tube rack etc. Add set_liquid example. Add create_resource and test_resource example. Add restart. Temp allow action message. Add no_update_feedback option. Create session_id by edge. bump version to 0.10.15 temp cancel update req
125 lines
4.5 KiB
Markdown
125 lines
4.5 KiB
Markdown
<div align="center">
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<img src="docs/logo.png" alt="Uni-Lab Logo" width="200"/>
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</div>
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# Uni-Lab-OS
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<!-- Language switcher -->
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**English** | [中文](README_zh.md)
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[](https://github.com/deepmodeling/Uni-Lab-OS/stargazers)
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[](https://github.com/deepmodeling/Uni-Lab-OS/network/members)
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[](https://github.com/deepmodeling/Uni-Lab-OS/issues)
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[](https://github.com/deepmodeling/Uni-Lab-OS/blob/main/LICENSE)
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Uni-Lab-OS is a platform for laboratory automation, designed to connect and control various experimental equipment, enabling automation and standardization of experimental workflows.
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## Key Features
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- Multi-device integration management
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- Automated experimental workflows
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- Cloud connectivity capabilities
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- Flexible configuration system
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- Support for multiple experimental protocols
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## Documentation
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Detailed documentation can be found at:
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- [Online Documentation](https://deepmodeling.github.io/Uni-Lab-OS/)
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## Quick Start
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### 1. Setup Conda Environment
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Uni-Lab-OS recommends using `mamba` for environment management. Choose the package that fits your needs:
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| Package | Use Case | Contents |
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|---------|----------|----------|
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| `unilabos` | **Recommended for most users** | Complete package, ready to use |
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| `unilabos-env` | Developers (editable install) | Environment only, install unilabos via pip |
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| `unilabos-full` | Simulation/Visualization | unilabos + ROS2 Desktop + Gazebo + MoveIt |
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```bash
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# Create new environment
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mamba create -n unilab python=3.11.14
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mamba activate unilab
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# Option A: Standard installation (recommended for most users)
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mamba install uni-lab::unilabos -c robostack-staging -c conda-forge
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# Option B: For developers (editable mode development)
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mamba install uni-lab::unilabos-env -c robostack-staging -c conda-forge
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# Then install unilabos and dependencies:
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git clone https://github.com/deepmodeling/Uni-Lab-OS.git && cd Uni-Lab-OS
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pip install -e .
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uv pip install -r unilabos/utils/requirements.txt
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# Option C: Full installation (simulation/visualization)
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mamba install uni-lab::unilabos-full -c robostack-staging -c conda-forge
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```
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**When to use which?**
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- **unilabos**: Standard installation for production deployment and general usage (recommended)
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- **unilabos-env**: For developers who need `pip install -e .` editable mode, modify source code
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- **unilabos-full**: For simulation (Gazebo), visualization (rviz2), and Jupyter notebooks
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### 2. Clone Repository (Optional, for developers)
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```bash
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# Clone the repository (only needed for development or examples)
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git clone https://github.com/deepmodeling/Uni-Lab-OS.git
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cd Uni-Lab-OS
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```
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3. Start Uni-Lab System
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Please refer to [Documentation - Boot Examples](https://deepmodeling.github.io/Uni-Lab-OS/boot_examples/index.html)
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4. Best Practice
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See [Best Practice Guide](https://deepmodeling.github.io/Uni-Lab-OS/user_guide/best_practice.html)
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## Message Format
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Uni-Lab-OS uses pre-built `unilabos_msgs` for system communication. You can find the built versions on the [GitHub Releases](https://github.com/deepmodeling/Uni-Lab-OS/releases) page.
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## Citation
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If you use [Uni-Lab-OS](https://arxiv.org/abs/2512.21766) in academic research, please cite:
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```bibtex
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@article{gao2025unilabos,
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title = {UniLabOS: An AI-Native Operating System for Autonomous Laboratories},
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doi = {10.48550/arXiv.2512.21766},
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publisher = {arXiv},
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author = {Gao, Jing and Chang, Junhan and Que, Haohui and Xiong, Yanfei and
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Zhang, Shixiang and Qi, Xianwei and Liu, Zhen and Wang, Jun-Jie and
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Ding, Qianjun and Li, Xinyu and Pan, Ziwei and Xie, Qiming and
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Yan, Zhuang and Yan, Junchi and Zhang, Linfeng},
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year = {2025}
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}
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```
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## License
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This project uses a dual licensing structure:
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- **Main Framework**: GPL-3.0 - see [LICENSE](LICENSE)
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- **Device Drivers** (`unilabos/devices/`): DP Technology Proprietary License
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See [NOTICE](NOTICE) for complete licensing details.
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## Project Statistics
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### Stars Trend
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<a href="https://star-history.com/#dptech-corp/Uni-Lab-OS&Date">
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<img src="https://api.star-history.com/svg?repos=dptech-corp/Uni-Lab-OS&type=Date" alt="Star History Chart" width="600">
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</a>
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## Contact Us
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- GitHub Issues: [https://github.com/deepmodeling/Uni-Lab-OS/issues](https://github.com/deepmodeling/Uni-Lab-OS/issues)
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