具备空间记忆和语义推理认知的嵌入式机器人的视觉运动导航.
IEEE transactions on neural networks and learning systems
|September 17, 2024
概括
这项研究引入了一种新的导航框架,分层拓语义认知导航 (HTSCN),将内存和推理集成为增强体内代理智能. 在图像目标导航任务中,HTSCN提高了决策和路径效率.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 认知科学 认知科学
背景情况:
- 嵌入式代理需要自主认知来进行智能决策.
- 目前的代理人努力有效地整合情节性记忆和长期推理.
- 记忆和推理的异质模式限制了全面的机器人智能.
研究的目的:
- 引入一个统一的框架,HTSCN,用于无集成内存和推理在体内代理.
- 为了提高机器人的决策性能和整体智能.
- 解决认知能力之间现有的合机制的局限性.
主要方法:
- 开发了一个层次拓语义认知导航 (HTSCN) 框架.
- 在双层图形结构中使用拓图和语义关系图表来表示记忆和推理.
- 整合了一个基于神经的认知提取过程,用于交叉模式的关系捕获.
主要成果:
- 与现有的认知结构相比,HTSCN在图像目标导航中显著提高了性能和路径效率.
- 可视化和可解释性实验证实了集成记忆,推理及其学习关系的积极影响.
- 该框架证明了在现实世界中部署的可行性和适应性.
结论:
- HTSCN有效地弥合了不同的认知模式,增强了体内代理智能.
- 综合方法提高了导航性能和路径效率.
- 该框架对需要高级认知能力的现实世界机器人应用具有前景.
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