新软件让深海无人艇更智能，能自主决策并适应复杂环境。
Deployment and Development of a Cognitive Teleoreactive Framework for Deep Sea Autonomy
- 融合符号推理与机器学习，实现可解释的自主决策
- 在真实深海探测器Sentry上成功验证，支持灵活任务设定
- 适合海洋科研与机器人算法研究者快速上手使用
一种新型自主水下航行器（AUV）任务规划与执行软件DINOS-R已在AUV Sentry上测试。该系统受认知架构与传统控制系统的启发，旨在替代旧有的MC架构。不同于现有系统，DINOS-R从零构建，将符号化决策（保证行为可理解、可重复、可证明）与机器学习技术及反应式行为统一，以实现跨平台的实地部署能力。主要采用Python3实现，具有可扩展性、模块化和可重用性，强调非专家用户友好性，并为未来海洋学与机器人算法研究提供支持。任务定义灵活，支持声明式表达；行为规范同样灵活，支持实时任务规划与用户预设代码计划并行。这些特性已在Sentry的实际探测任务及多种仿真场景中得到验证。结果被分析，未来工作方向亦被提出。
原文摘要 · Abstract (English)
A new AUV mission planning and execution software has been tested on AUV Sentry. Dubbed DINOS-R, it draws inspiration from cognitive architectures and AUV control systems to replace the legacy MC architecture. Unlike these existing architectures, however, DINOS-R is built from the ground-up to unify symbolic decision making (for understandable, repeatable, provable behavior) with machine learning techniques and reactive behaviors, for field-readiness across oceanographic platforms. Implemented primarily in Python3, DINOS-R is extensible, modular, and reusable, with an emphasis on non-expert use as well as growth for future research in oceanography and robot algorithms. Mission specification is flexible, and can be specified declaratively. Behavior specification is similarly flexible, supporting simultaneous use of real-time task planning and hard-coded user specified plans. These features were demonstrated in the field on Sentry, in addition to a variety of simulated cases. These results are discussed, and future work is outlined.
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